Global Trends and Insight in Transformer Oil Insulation Research: A Scientometric Perspective

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H. Nik Ali, Noor Khairin Mohd, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8082286/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study explores global trends and research insights in transformer oil insulation, focusing on the evolution of materials and diagnostic technologies from 1970 to 2024. Transformer oil plays a critical role in maintaining the reliability and longevity of high-voltage electrical equipment. Over the decades, research in this area has expanded significantly, covering advancements in base fluids, nano-enhancements, and diagnostic techniques. However, despite this growing body of literature, no comprehensive bibliometric study has been conducted specifically on transformer oil research. In particular, no study to date has utilized CiteSpace or similar tools to map the intellectual structure and evolving research themes in this domain. This gap presents an opportunity to provide a structured and data-driven understanding of the field’s development. To address this, the paper presents a combined descriptive and scientometric analysis of transformer oil research. The descriptive analysis highlights publication output, geographical distribution, and authorship patterns. Scientometric analysis, conducted using CiteSpace, visualizes bibliometric networks to identify influential authors, institutions, and emerging research clusters. A total of 2,801 publications were analyzed, resulting in the identification of 12 major clusters, with prominent themes including Dielectric Strength, DFT Studies, and Dissolved Gas Analysis (DGA). These clusters reveal the rise of nano-enhanced insulating fluids and advanced diagnostic techniques. By identifying key trends, contributors, and thematic focuses, this review offers a comprehensive overview of the transformer oil research landscape and provides strategic insights to guide future innovations in insulation technologies and sustainable energy systems. Scientometric Cluster Transformer Oil High-voltage Insulation Performance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Introduction Transformer oil is essential for ensuring the reliability and efficiency of high-voltage transformers, which are crucial components in power transmission and distribution networks. Traditionally, mineral oil has been the most commonly used insulating and cooling medium due to its favorable dielectric properties and thermal performance. However, concerns regarding environmental impact and fire safety have prompted the exploration of alternative oils, such as synthetic esters and natural ester-based oils, which offer superior biodegradability and fire resistance. In recent years, transformer oils have gained increased attention not only for their insulating capabilities but also for their potential to extend transformer lifespan and reduce maintenance costs. While these technical advancements are well-documented, there has been limited focus on mapping the evolution of transformer oil research from a scientometric perspective. Understanding the structure of this knowledge domain is essential to uncovering key contributors, collaborations, and emerging themes. Bibliometric tools such as CiteSpace offer a unique opportunity to visualize citation networks, intellectual turning points, and thematic clusters—helping researchers identify how the field has progressed over time. Despite the crucial role of transformer oil in maintaining transformer performance over time, the industry faces several challenges. The growing demand for higher-voltage transformers, the integration of renewable energy sources into the power grid, and the push for environmental sustainability have intensified the need for more advanced insulation technologies. Additionally, the evolution of research in this field has been shaped by the ability to track emerging trends, identify intellectual turning points, and visualize knowledge domains. As highlighted by [ 1 , 2 ] tools such as CiteSpace have proven instrumental in uncovering pivotal developments and transient patterns in scientific literature, aiding researchers in understanding the progression of transformer oil studies. Furthermore, the predictive effects of structural variation on research impact, as discussed by [ 3 ], provide a framework for analyzing how novel approaches—such as the use of nanoparticles in transformer oil—may influence future advancements. These bibliometric tools enable a deeper understanding of the interdisciplinary connections within the field and highlight opportunities for innovation. Several bibliometric and review studies have synthesized transformer insulating oil-specific topics, including reviews on oil-based nanofluids as next-generation insulation [ 4 ], green nanofluids for transformer applications [ 5 ], vegetable-based nanofluids for green transformer insulation [ 6 ], plant-based insulating fluids for transformers [ 7 ], the impacts of nanotechnology on liquid insulation [ 8 ], natural esters and nanofluids as environmentally friendly alternatives to mineral oil [ 9 ], recent progress and challenges in transformer oil nanofluid development [ 10 ], synthetic ester liquids for transformer applications [ 11 ], the application of nanomaterials in transformer oil [ 12 ], flash point improvement of mineral oil using nanoparticles [ 13 ] and natural esters for green transformers [ 14 ]. Other studies have focused on advancements in nanomaterials for environmental remediation [ 15 ] and even explored the impact of COVID-19 on the energy sector [ 16 ]. While several reviews have focused on the physicochemical performance of transformer oils and the integration of nanoparticles, these studies typically lack a bibliometric analysis of research patterns, influential works, and thematic evolutions over time as shown in Table 1 . Therefore, this study aims to conduct a comprehensive bibliometric and scientometric analysis of transformer oil research using CiteSpace, focusing on trends from 1970 to 2024. By examining patterns in publication output, co-authorship networks, and emerging clusters, this paper seeks to provide a data-driven perspective on the evolution of transformer oil insulation research. Scientometric analysis can provide insights into the dynamics and connections between journals, authors, and papers in transformer oil research, based on standards such as the growth of knowledge over time, links between subject areas, and intellectual turning points within a subject [ 17 – 19 ]. This gap underscores the importance of a bibliometric analysis to uncover leading contributors and track emerging themes within the field. Science mapping techniques, such as those reviewed by [ 20 ], provide valuable insights into the structure and dynamics of scientific fields. These methods enable the identification of emerging themes, intellectual turning points, and collaborative networks, offering a comprehensive view of the evolution of transformer oil research. Such tools not only reveal knowledge gaps but also guide future research directions, ensuring the alignment of scientific efforts with industry demands and global challenges. A bibliometric analysis offers a comprehensive approach to mapping the landscape of electrical insulator oil research, providing quantitative insights into publication patterns, citation networks, and influential contributors [ 21 , 22 ]. By analyzing bibliometric data, researchers can identify prolific authors, prominent journals, and the geographical distribution of research efforts. This method also highlights the interdisciplinary nature of the field, illustrating how the research intersects with disciplines such as materials science, nanotechnology, and environmental engineering. Additionally, bibliometric analysis can uncover emerging topics and gaps in the literature, guiding future research and informing policy and practice [ 23 , 24 ]. To date, no comprehensive bibliometric study using tools like CiteSpace has been performed on transformer oil insulation research. Existing studies have either focused on broader electrical insulation fields or lacked a science mapping approach. This study fills that gap by analyzing 2,801 publications and identifying research clusters, authorship patterns, and emerging research trends. This paper aims to reveal the descriptive and scientometric analysis conducted on transformer oil research to elucidate the evolution and current state of the field. This work contributes to the field by offering a structured visualization of global research trends, identifying influential authors and institutions, and revealing thematic developments. In doing so, it supports researchers, policymakers, and industry stakeholders in navigating the current research landscape and shaping future directions in transformer oil insulation studies. The findings will contribute to a deeper understanding of the role of transformer fluid in enhancing transformer performance and offer valuable insights for researchers, practitioners, and policymakers seeking to improve the design and implementation of these crucial materials in electrical systems. Table 1 Summary of literature review Author(s) Year Focus Area Method Key Findings Gap Identified S. N. Suhaimi et al. 2020 Oil-based nanofluids for transformer applications Narrative Review Highlighted nanofluids' potential to enhance dielectric properties Lacked detailed bibliometric/scientometric trend analysis S. O. Oparanti et al. 2024 Green nanofluids for transformer insulation Narrative Review Emphasized sustainability and biodegradability of natural ester-based nanofluids No in-depth mapping of author/country collaboration or research hotspots A. Siddique et al. 2024 Vegetable-based nanofluids Systematic Review Reviewed cost-effective and biodegradable options as future green resources Lacked visualization of research evolution or influential networks Z. Shen et al. 2021 30-year development of plant-based fluids Critical Review Identified long-term development trends and future potential Did not address the emerging impact of nanoparticles in insulation systems M. Rafiq et al. 2020 Nanotech impact on transformer insulation Narrative Review Discussed electrical and thermal property enhancements No bibliometric validation of emerging trends or knowledge clusters J. Jacob et al. 2020 Natural ester and nanofluids Narrative Review Combined nanotechnology with bio-based fluids for eco-friendly solutions Did not benchmark the topic using CiteSpace or science mapping tools D. Amin et al. 2019 Progress on nanofluid properties Narrative Review Detailed thermal and electrical improvements in nanofluids No structural analysis on topic co-occurrence or cluster evolution P. Rozga et al. 2020 Synthetic ester liquids Narrative Review Evaluated synthetic esters for transformer use No linkage to nanoparticle integration or scientometric trend evaluation M. Rafiq et al. 2021 Transformer oil nanofluid properties Narrative Review Focused on electrical, thermal, and physicochemical property enhancement Bibliometric evolution and leading contributors not covered Khoirudin et al. 2024 Flash point improvement using nanoparticles Narrative Review Demonstrated reduced fire risk in mineral oils with nanoparticle doping Scientometric development trajectory not visualized S. O. Oparanti et al. 2023 Challenges in natural ester serviceability Narrative Review Proposed keys to enhance serviceability and performance No use of bibliometric tools to track solution-oriented research trajectories N. Asghar et al. 2024 Nanomaterials in environmental remediation Systematic Review Included bibliometric analysis of synthesis routes and materials Not specific to transformer oil applications S. R. Arsad et al. 2023 COVID-19 and AI impact in the energy sector Analytical Review Assessed pre- and post-pandemic trends and AI roles in energy Indirectly relevant—no specific focus on transformer insulation This study 2024 Global transformer oil research Scientometric Review Used CiteSpace to identify top authors, countries, clusters Fills gap in science mapping Methodology This study employs a dual approach, integrating descriptive and scientometric analyses to systematically examine transformer oil research trends, key contributors, and major themes from 1970 to 2024, utilizing data extracted from the Web of Science Core Collection (WOSCC) database, as outlined in the study's flowchart as shown in Fig. 1 . For text processing, we extracted data from . txt files to gather information from manuscript titles, research abstracts, author-provided keywords, and Keywords Plus, which facilitated the creation of a robust dataset for descriptive analysis. Co-citation references, sourced from the WOSCC database, provided additional metadata, forming the basis for our scientometric evaluation. To achieve a comprehensive search, we employed the “TS” (topic search) function in the Web of Science, focusing on titles, abstracts, keywords, and author information. The search phrase used was “TS= ((“Transformer oil”) OR (“Transformer Fluid”) OR (“Transformer Liquid”) OR (“Insulating Oil”) OR (“Electrical Insulator Oil”))”. This strategic selection ensured a thorough yet manageable scope, reducing complexity that might arise from including every scientific term and avoiding confusion with irrelevant entries. To further enhance the validity of the study, non-English articles were excluded to minimize language-related biases. The search was limited to titles, keywords, and abstracts since the WOSCC database did not offer access to full-text articles. Despite this limitation, the selected scope enabled us to effectively capture and analyze relevant literature, while minimizing redundancy in article titles, keywords, or abstracts within the metadata. The methodological framework, rooted in scientometric analysis and a carefully constructed search strategy, provides a solid foundation for visualizing the development and research trends in transformer oil literature, highlighting the strengths and thoroughness of the approach adopted in this study Data Analysis The data analysis for insulating transformer oil research was divided into two main segments: descriptive analysis and scientometric analysis [ 23 , 24 ]. Descriptive Analysis The descriptive analysis focused on the annual number of publications, the journals, authors and countries involved in the research. It provided insights into the geographical distribution and highlighting trends over time. This segment aimed to identify key contributors and the evolution of research interest in transformer oil. Scientometric Analysis The scientometric analysis utilized CiteSpace 6.3 for Windows for visualization and knowledge graph analysis, following methodologies outlined by [ 23 ] and [ 24 ]. This tool facilitated the creation of bibliometric networks through various analyses: Dual-Map Overlay Analysis : This method visually categorized the literature into cited and citing journals, mapping the relationships between them. The sizes of the ovals in the map represent the volume of publications and citation counts, respectively, while the thickness of the lines indicates citation frequency across disciplines. This analysis provided a visual representation of how insulating transformer oil research is interconnected across various scientific fields. Document Co-Citation Analysis (DCA) : This analysis identified instances where two sources were simultaneously cited in a paper, providing metrics such as burstiness (indicating sudden increases in citations), centrality (highlighting influential articles), and sigma (a composite measure of centrality and burstiness to assess the novelty and impact of research). Articles with centrality values over 1 were deemed crucial, acting as connectors within the network. The clusters in the DCA were determined using the Log-Likelihood Ratio (LLR), ensuring optimal cluster uniqueness and coverage. The network structure was visualized through timeline and cluster views, aiding in understanding the evolution and interconnections within the research domain. The quality and coherence of the clusters were evaluated using the Modularity Q index and the Silhouette Metric, which assess the reliability and homogeneity of the clusters, respectively. By integrating both descriptive and scientometric analyses, this study provides a detailed and nuanced understanding of the current state and historical development of transformer oil research, identifying key trends, influential works, and potential future directions [ 25 ]. Results In total, the search outlined in the methodology section retrieved 2,801 publications related to insulating transformer oil research. These publications boast an h-index of 69. Collectively, these studies have been cited 42,210 times, resulting in an average citation rate of 15.07 citations per article. The dataset for insulating transformer oil research spans from 1970 to 2024. As shown in the Fig. 2 , the first publication about electrical insulating oil or transformer oil research emerged in 1970. From that point until around 2010, research activity in this area increased gradually, maintaining a low profile with the annual number of publications rarely exceeding 50. However, the trend significantly shifted after 2010, with a steep increase in research output. This sudden rise can be attributed to various factors, including the global emphasis on renewable energy integration, environmental concerns over the use of traditional mineral oils, and the search for alternative, more sustainable transformer oils such as natural and synthetic esters [ 26 – 28 ]. The graph clearly demonstrates that from 2010 onward, the number of publications surged dramatically, peaking at around 250 publications in 2023. The heightened interest in transformer oil research can be explained by the following developments in the field: The Shift Towards Renewable Energy Systems : The growing need for reliable insulation materials in power transformers that can support high voltage levels required for integrating renewable energy sources has become a key focus area. Researchers have explored transformer oil modifications to enhance their dielectric and thermal performance under such new operating conditions [ 12 , 29 ]. Nanotechnology Integration : The incorporation of nanoparticles into transformer oils has gained traction as a method to boost breakdown strength, thermal conductivity, and overall stability of transformer fluids, leading to improved efficiency and longevity of transformers. This aspect has drawn substantial research interest, as reflected in numerous studies published over the past decade [ 13 , 30 ]. Environmental Concerns and Safety Regulations : Stringent environmental regulations and the need for more sustainable transformer fluids have led to a shift from conventional mineral oils to biodegradable and less flammable natural esters and synthetic oils [ 5 , 14 ]. These efforts have been driven by the focus on green technology and safety standards, further boosting research in this area. In the period between 2010 and 2024, the year 2023 stands out as the peak in terms of publication output, with the number of papers nearing 250. This peak could be associated with an increased global focus on energy efficiency, digitalization in transformer monitoring, and the advancement of smart grid technologies. The COVID-19 pandemic also played a role, as it disrupted global energy systems and prompted a reevaluation of energy infrastructure resilience, including the use of more advanced insulation materials in transformers to cope with fluctuating loads and extreme weather events [ 16 ]. Overall, this trend suggests a growing recognition of the importance of transformer oil research, not only in ensuring reliable power system performance but also in addressing emerging challenges in energy transmission and distribution. It highlights the dynamic and evolving nature of the field, reinforcing the validity of conducting further in-depth scientometric studies to track developments and identify future research directions in transformer oil technologies. The dataset for transformer oil research spans across multiple countries, reflecting a global interest in the field. The geographical distribution of publications related to transformer oil research is visualized in Fig. 3 . The map chart uses a blue color scale to represent the number of records contributed by each country, ranging from 1 to 975 publications. The knowledge mapping investigation, conducted using CiteSpace, highlights significant contributions from various countries worldwide. The People's Republic of China leads as the largest contributor with 975 records, accounting for approximately 34.81% of the total 2,801 publications. This underscores China's prominent role in advancing transformer oil research. Following China, India ranks second with 289 publications (10.32%), showcasing substantial research efforts. The United States comes third, contributing 197 publications (7.03%), emphasizing its active participation in the global research community. Japan and Russia also make notable contributions with 146 (5.21%) and 150 (5.36%) publications, respectively. Other countries such as South Korea (86, 3.07%), Malaysia (85, 3.04%), Iran (83, 2.96%), and Poland (80, 2.86%) also reflect robust research engagement in this field. European nations, including England (81, 2.89%), France (60, 2.14%), and Germany (34, 1.21%), further demonstrate the widespread international focus on transformer oil research. These contributions highlight the global interest and cooperative efforts to enhance transformer insulation technologies, which are critical for energy infrastructure development. In conclusion, the geographical distribution, as illustrated in Fig. 3 , reveals China's dominant role alongside significant contributions from India, the United States, Japan, and Russia, among others. This international collaboration signifies the shared goal of improving transformer oil technology for better energy efficiency and reliability. Table 2 Publication Affiliations Affiliations Record Count Country Chongqing University 240 China State Grid Corporation of China 132 Beijing, China North China Electric Power University 106 Beijing, China Xi An Jiaotong University 100 Xi,An, China Egyptian Knowledge Bank EKB 96 Egypt Southwest University China 80 China China Southern Power Grid 74 Guangzhou, China India Institute of Technology System 70 India Russian Academy of Sciences 69 Moscow, Russia Guangxi University 60 Nanning, Guangxi China Over the study period, Table 2 highlights the top ten affiliations that have significantly contributed to transformer oil research. The data clearly shows that Chinese institutions dominate the field, followed by contributions from Egypt, India, and Russia. Chongqing University leads the list with 240 publications, establishing its position as a major research hub in this domain. The State Grid Corporation of China, headquartered in Beijing and recognized globally for its expertise in electricity transmission, smart grids, and renewable energy integration, ranks second with 132 publications, reflecting its substantial impact despite its specialized focus. Several other institutions exhibit comparable publication output, with between 70 and 100 records. These include Xi An Jiaotong University (China), the Egyptian Knowledge Bank (EKB), Southwest University (China), China Southern Power Grid (Guangzhou, China), and the Indian Institutes of Technology (India). The diversity of these affiliations underscores the international nature of transformer oil research, with academic and industry-driven institutions from various regions contributing meaningfully to the field. At the lower end of the top ten, the Russian Academy of Sciences (69 publications) and Guangxi University (60 publications) demonstrate that, while smaller in output, their inclusion signals broad global engagement in this area of study. From a scientometric perspective, identifying the most productive affiliations provides valuable insights into the geographical distribution of research activity and the leading contributors to scientific advancement in transformer oil technology. This information is crucial for mapping key research centers, fostering international collaborations, guiding funding strategies, and recognizing the institutions that shape innovation and discourse in this field. Authors Productivity As depicted in Table 3 and Fig. 4 , the majority of the top 10 authors are actively engaged in electrical power system engineering and transformer oil research. Leading the list is Kopcansky Peter, with 43 publications, whose work focuses on magnetic nanoparticles, liquid crystals, and biomedicine at the Institute of Experimental Physics, Slovak Academy of Sciences in Kosice, Slovakia. Following closely is Li Chengrong from the North China Electric Power University (NCEPU), who has published 40 papers, specializing in partial discharge, insulation, and transformer technologies. In third place is Rajnak Michal, with 36 publications, primarily researching physics, colloids, magnetism, ferrofluids, and dielectric liquids. In addition to these top contributors, several other researchers are approaching 35 publications, reflecting their ongoing contributions to transformer oil studies. The fewest publications among the top ten belong to Zahn Markus, affiliated with the Massachusetts Institute of Technology (MIT) School of Engineering. He has authored 28 papers, primarily in nanotechnology, transformers, and high voltage engineering. Although MIT’s researcher has fewer publications, achieving over 20 publications is still a notable accomplishment in this field. In summary, all the top ten authors have demonstrated significant expertise in transformer and insulation technology research, with each having published more than 20 papers. This demonstrates that the study of transformer oil and related technologies is an area of growing interest, as researchers from various countries continue to explore and contribute to this field. Table 3 List of top ten authors with their publication’s details. Researcher Profiles Records Count Research Areas Affiliations Countries tang, chao 29 Engineering, Physics, Biochemistry & Molecular Materials Science Hong Kong University of Science & Technology Beijing, Peoples R China Chen, Weigen 32 Engineering, Materials Science, Physics, Energy & Fuels Chongqing University Chongqing, Peoples R China liu, yiran 33 Engineering, Computer Science, Physics & Material Science Institute of High Energy Physics, CAS. Japan Zhang, Chaohai 30 Transformer, Renewable Energy Technologies & Power Quality Nanjing University of Aeronautics & Astronautics Nanjing, China Li, Chengrong 40 Partial Discharge, Insulation & Transformer North China Electric Power University, NCEPU Beijing, Peoples R China Liu, Jiefeng 31 Electrical & Electronics Engineering Guangxi University School of Electrical Engineering Nanning, Peoples R China Kopcansky, Peter 43 Magnetic Nanoparticles, Liquid Crystals & Biomedicine Institute of Experimental Physics, Slovak Academy of Sciences, Kosice Slovakia Slovakia Zahn,Markus 28 Nanotechnology, Transformer & High Voltage Engineering Massachusetts Institute of Technology (MIT) School of Engineering USA Cimbala, Roman 27 Engineering, Physics, Chemistry & Material Science Technical University of Kosice Slovakia Rajnak, Michal 36 Physics, Colloids, Magnetism, Ferrofluids & Dielectric Liquids Institute of Experimental Physics SAS, Kosice Slovakia Slovakia Out of 2,801 publications in the WOSCC database between 1970 and 2024, Fig. 5 highlights the top ten journals based on the number of publications on transformer oil research. The leading journal, IEEE Transactions on Dielectric and Electrical Insulation, accounts for 15.39% of the total publications, making it the most significant contributor in this field. Following this, the Energies journal and IET Science Measurement & Technology rank second and third, contributing 3.00% and 2.04% of the total publications, respectively. It is evident from Table 4 that research on transformer oil insulating topics spans a wide range of journals, including both high-ranked Q1 journals and lower-ranked Q3/Q4 journals. Among the Q1 journals, Journal of Molecular Liquids boasts the highest Journal Impact Factor (JIF) in 2023 at 4.7, reflecting its strong influence in the scientific community. Other notable Q1 journals include IEEE Transactions on Power Delivery (JIF = 3.3) and IEEE Access (JIF = 3.0). In the Q3 category, the Energies journal has a JIF of 2.4, highlighting its growing relevance in energy-related research. Additionally, IET Science Measurement & Technology (JIF = 1.4, Q3-Q4) and Journal of Electrostatics (JIF = 1.7, Q3) demonstrate how impactful transformer oil research can extend across different quartiles. The citation trends for insulating transformer oil research have shown a steady increase over the years, reflecting the growing academic and industrial interest in this domain. This upward trajectory underscores the importance of transformer oil research in advancing energy efficiency, sustainability, and reliability, particularly in light of global efforts to enhance electrical insulation technologies. Table 4 Number of publications from the top ten journals published between 1970 and 2024 Publication Titles 5 Year Impact Factor Quartile JIF JIF 2023 Quartile JCI JCI 2023 Record Count % Out of 2801 Journal Of Physics D Applied Physics 3.0 Q2 2.9 Q2 0.65 32 1.142 Journal of Molecular Liquids 5.2 Q1 – Q2 4.7 Q1 1.20 38 1.357 Journal of Electrostatics 1.9 Q3 1.7 Q3 0.47 32 1.142 IET Science Measurement & Technology 1.4 Q3 1.4 Q4 0.31 57 2.035 IET Generation Transmission & Distribution 2.3 Q2 – Q3 1.7 Q3 0.52 48 1.714 IEEE Transactions on Power Delivery 4.2 Q1 – Q2 3.3 Q1 – Q2 1.07 34 1.214 *IEEE Transactions on Electrical Insulation 3.0 Q2 – Q3 2.2 Q2 0.65 44 1.571 IEEE Transactions on Dielectric and Electrical Insulation 3.0 Q2 – Q3 2.2 Q2 0.65 431 15.387 IEEE Access 3.7 Q1 – Q2 3.0 Q1 – Q2 0.87 52 1.856 Energies 3.0 Q3 2.4 Q3 0.46 84 2.999 *JIF = Journal Impact Factor, JCI = Journal Citation Indicator Scientometric Dual Map Overlay The dual-map overlay visualization effectively illustrates the interdisciplinary dynamics of insulating oil transformer research, mapping the relationships between citing and cited journals. As visualized in Fig. 6 , journals citing others are displayed on the left, while those being cited are positioned on the right. Citation links bridge these two sides, revealing the sources referenced by the citing journals. By examining the trajectories of these links, interdisciplinary influences can be discerned; for instance, a shift in trajectory indicates that research in one field has been significantly influenced by studies from another domain. The citing map (left) predominantly represents journals focused on applied disciplines like electrical engineering, material science, and applied physics, underscoring the technical objectives of transformer oil research—primarily improving electrical and thermal properties. Meanwhile, the cited map (right) highlights foundational fields such as molecular physics, chemistry, and computational science, which contribute essential theoretical insights into chemical interactions, molecular behavior, and synthesis techniques for insulating fluids and nanomaterials. The directional lines linking the two maps underscore a symbiotic relationship between theoretical research and practical applications, showcasing how advancements in transformer oil technology rely heavily on interdisciplinary collaboration. The visualization emphasizes the convergence of engineering practices with foundational sciences, reinforcing the need for continued interdisciplinary efforts to innovate in transformer insulation systems, particularly with the incorporation of nano-enhanced oils. This comprehensive depiction affirms the vital role of cross-field knowledge integration in advancing insulating oil technology. Document Co-citation Analysis Document Co-citation Analysis (DCA) explores the frequency with which various documents are co-cited by later studies, providing insights into the interconnectedness and influence within a specific field. For transformer oil research, this analysis uses data from the Web of Science (WoS) database, revealing a complex network comprising 1,725 nodes and 3,508 links. This setup indicates a broad and interconnected field, characterized by frequent references across diverse studies. The network analysis reveals a high silhouette score of 0.0.9575, suggesting substantial thematic commonality among the articles within each cluster. This score highlights the cohesion of topics, where research themes are not only related but also tightly integrated, with consistent citation patterns across various studies. Additionally, a harmonic mean of 0.9521 points to the network's internal coherence, signifying a balanced spread of citations among the papers. Furthermore, the low network density score of 0.0024 indicates a specialized research network. This low density suggests that direct citation connections between all papers are uncommon, reflecting the specialized and diverse nature of insulating transformer oil research. The Table 5 provides data on top highly influential articles related to insulating transformer oil research, focusing on three metrics: Degree, Centrality, and Sigma. The Degree metric indicates how many other articles directly reference or are referenced by these studies. Figure 7 show the list of authors with a centrality score greater than or equal to 0.1, with 4 total important authors categorized as authors for co-citation analysis. The top three most central authors based on centrality score were Bakar et al., Liao et al., and Okabe et al., with centrality scores of 0.04, 0.05 and 0.04 respectively. However, based on the Sigma score, the top author is only Bakar et al., from Curtin University, Western Australia with an exceptional sigma score to 1.6 showing that he is the most influential author in transformer oil research in the world. Table 5 Top highly influential articles Article Degree Centrality Sigma (Bakar et al., 2014) 12 0.04 1.61 (Liao et al., 2011) 4 0.05 1.32 (Okabe et al., 2013) 11 0.04 1.32 Bakar et al. (2014): Their paper provides a comprehensive review of Dissolved Gas Analysis (DGA), emphasizing its importance in diagnosing faults in transformers. The article's high centrality score (0.04) and outstanding Sigma score (1.61) reflect its foundational contribution, as DGA is critical for transformer maintenance. Their integration of AI and modern statistical tools further showcases their role in advancing transformer diagnostics, making them a key reference in this domain. Liao et al. (2011): This article compares natural ester and mineral oil-based transformer insulation. It highlights natural ester's environmental advantages but notes areas for improvement in dielectric properties. With a centrality score of 0.05, it demonstrates the significant impact on understanding the potential of environmentally friendly transformer oils. This aligns with the growing interest in sustainable alternatives within transformer oil research. Okabe et al. (2013): Their work on aging degradation in oil-immersed transformers provides valuable insights into the lifespan and reliability of transformer insulation. The centrality score of 0.04 highlights their contribution to real-world applications, particularly in understanding how operational data can inform better maintenance and diagnostics. The high influence of these authors, as evidenced by the metrics (Degree, Centrality, and Sigma), directly correlates with their innovative contributions to transformer oil research. Their studies have shaped both the theoretical and practical understanding of insulating materials and diagnostics, making them indispensable references in the field. Cluster Analysis The Document Co-citation Analysis (DCA) of transformer oil research has identified 12 distinct co-citation clusters, ranked by their sizes, with the largest cluster designated as #0. In this analysis, the size of the circles within each cluster represents the influence of the publications, with larger circles indicating higher citation rates. This effectively highlights the key papers that have significantly impacted their fields. Additionally, the lines extending from each cluster illustrate the duration or "lifetime" of the cluster, providing insights into the development and evolution of research themes over time. The size of each cluster is directly proportional to the number of publications it contains, with six out of the twelve clusters including more than fifty publications each. Figure 8 illustrates the top 12 clusters, displayed horizontally with cluster labels positioned on the right. Additionally, Fig. 8 depicts the network of all the articles. The clusters are numbered and ranked by size, beginning with #0 as the largest cluster and #86 as the smallest. The size of each circle represents the magnitude of a publication's influence, where larger circles correspond to greater impact. This highlights significant engagement within these thematic areas, with Cluster #0 emerging as the most prominent based on the number of publications. The red rings surrounding the circles indicate the "bursts" of articles, revealing when bursts occurred and their relative strength. Furthermore, the silhouette scores for these clusters range from 0.919 to 0.998, reflecting a high level of homogeneity within each cluster's publications. Silhouette scores measure the consistency of objects within their own cluster compared to other clusters, with values above 0.900 demonstrating strong internal coherence. The clusters were named using four different methods: (i) Latent Semantic Indexing (LSI), (ii) Term Frequency–Inverted Document Frequency (TF-IDF), (iii) log-likelihood ratio (LLR), and (iv) Mutual Information (MI). The Log-Likelihood Ratio (LLR) is generally considered superior to Mutual Information (MI) and Latent Semantic Indexing (LSI) due to its higher discriminative power and statistical robustness. LLR identifies terms that are uniquely and significantly associated with a specific cluster, enabling more precise and representative labeling [ 31 , 32 ]. Unlike MI, which often highlights frequently occurring terms that may appear across multiple clusters—resulting in vague or generic labels—LLR emphasizes the distinctiveness of terms, thereby enhancing the clarity and specificity of each cluster's thematic identity. Similarly, while LSI is valuable for uncovering latent semantic structures through dimensionality reduction techniques, it tends to produce abstract or less interpretable terms that are not optimal for direct cluster labeling. LLR, by contrast, utilizes a statistical framework that evaluates the likelihood of a term’s association with a cluster beyond random chance, making it particularly effective for generating meaningful and contextually grounded descriptors [ 33 ]. As such, LLR is preferred in scientometric studies that prioritize clarity, specificity, and analytical rigor in the interpretation of knowledge domains. Following the approach outlined in [ 34 ],this paper focuses on the clusters identified using the log-likelihood ratio (LLR), as the outputs of the other methods occasionally produced less coherent results. Table 6 provides a comprehensive overview of research clusters, characterized by Cluster ID, Size, Silhouette score, Label (LSI), Label (LLR), Label (MI) and Average Year. The analysis identified the most influential clusters, which are summarized in Table 6 . The clusters range in size from 4 to 176 articles, with silhouette scores indicating the cohesion within clusters, where a score of 1 denotes perfect cohesion. The clusters represent diverse research themes, with Cluster 0 (Dielectric Strength) being the largest (176 publications, average year 2017), highlighting its longstanding importance in transformer insulation studies. Cluster 1 (DFT Study), while smaller (101 publications), focuses on emerging computational methods like Density Functional Theory (average year 2020) with a high silhouette score (0.970), indicating strong thematic coherence. Notable older themes include Cluster 7 (Copper Sulfide Deposition) with an average year of 2008 and Cluster 18 (Hydrothermal Synthesis) from 2009, which emphasize historical research trends. Conversely, newer themes like Cluster 8 (Health Index) and Cluster 86 (Alternative Liquid Dielectric) reflect contemporary innovations in transformer diagnostics and sustainable materials, both with an average year of 2020. This distribution highlights the field's evolution, balancing foundational studies with modern advancements. Table 6 The 12 major clusters that emerged from Document Co-citation Analysis. Size represents the number of publications in a cluster, and silhouette score indicates levels of homogeneity. Labels were derived from log likelihood ratios (LLR) Cluster ID Size Silhouette Label (LSI) Label (LLR) Label (MI) Average Year 0 176 0.930 Oxidation stability Dielectric Strength Insulating oil-based nanofluid (4.22) 2017 1 101 0.970 DFT method DFT Study Sensing substrate (0.52) 2020 2 70 0.976 Over-sampling technique Power Transformer Wavelength modulation (1.06) 2015 3 56 0.994 Dispersion behavior Recent Progress Turbulent electroconvection (0.35) 2011 4 54 0.955 Thermal aging Mixed Dielectric Fluid Alternative insulating liquid (0.8) 2013 5 54 0.919 Multilayer sensor Various Characteristics Multilayer sensor (0.12) 2010 7 37 0.998 Suppressive mechanism Copper Sulfide Deposition Transformer oil (0.06) 2008 8 37 0.951 Concentration prediction Health Index Multilayer sensor (0.4) 2020 11 28 0.943 Smart life prediction approach Precise Measurement Diffusion mechanism (0.1) 2018 12 25 0.995 Electro-insulating fluid Electro-Insulating Fluid Transformer oil (0.07) 2015 18 19 0.986 Structural characterization Hydrothermal Synthesis Transformer oil (0.07) 2009 86 4 0.998 Visual domain Alternative Liquid Dielectric Transformer oil (0.08) 2020 The most cited papers in the field shows in Table 7 . The citation statistics were collected from WoS database. The journal is a mixed source from mainstream business economics journals through field journals. The top three articles by [ 7 , 35 , 36 ] have been very heavily cited, with more than 20 citations. The results indicated that [ 36 ] received the highest citation count which is published in the most recognized journal, Applied Surface Science . The study titled “Pd-doped MoS₂ monolayer: A promising candidate for DGA in transformer oil based on DFT method” stands out as the most cited work among the top 10 publications in Cluster #1. Despite Cluster #1 having fewer publications than Cluster #0, it focuses on cutting-edge methods like DFT (Density Functional Theory), which has significant practical relevance. This emphasis on innovative approaches has made studies in this cluster highly influential in both academic and industrial circles. Article citation is an indicator that shows the impact of a study in its research field. Based on previous study, the direction of one research field is associated with its frequently cited articles [ 37 – 39 ]. H. Cui, X. Zhang, G. Zhang and J. Tang [ 36 ] study uses Density Functional Theory (DFT) to explore Pd-doped MoS 2 monolayers as advanced materials for dissolved gas analysis (DGA). Its high citation count likely stems from its innovative approach to diagnosing transformer oil degradation, combining computational methods with practical implications in improving transformer reliability. The focus on nanomaterials for DGA highlights the study's relevance to emerging trends in diagnostics for energy systems. H. Cui, D. Chen, Y. Zhang and X. Zhang [ 35 ] similar to the first study, this research emphasizes DFT to evaluate Pd-decorated MoSe 2 monolayers for gas sensing applications in transformer oil. Its high impact is due to its alignment with the ongoing interest in nano-engineered solutions for precise and efficient fault detection in transformers. The theoretical insights into gas adsorption mechanisms contribute to both material science and transformer diagnostics. Z. Shen, F. Wang, Z. Wang and J. Li [ 7 ] This comprehensive review of plant-based insulating fluids reflects a growing interest in sustainable alternatives to mineral oils. The high citation rate is likely due to its coverage of technological advancements, environmental benefits, and challenges in implementing plant-based fluids. The article's relevance is amplified by the global push towards greener technologies in the energy sector. In summary, the high citation counts of these articles are due to their cutting-edge focus on nanotechnology for transformer diagnostics [ 35 , 36 ] and sustainable insulating fluids [ 7 ]. Their combination of theoretical rigor, practical applications, and alignment with current research trends makes them highly influential in the transformer oil research domain. Table 7 Top 10 highest-cited articles in the transformer oil research theme Title of the article Authors Cluster ID (explanation) Publication year Total citation Reference Pd-doped MoS 2 monolayer: A promising candidate for DGA in transformer oil based on DFT method H. Cui, X. Zhang, G. Zhang and J. Tang 1 (novel method and material) 2019 53 [ 36 ] Dissolved gas analysis in transformer oil using Pd catalyst decorated MoSe 2 monolayer: A first-principles theory H. Cui, D. Chen, Y. Zhang and X. Zhang 1 (novel method and material) 2019 35 [ 35 ] A critical review of plant-based insulating fluids for transformer: 30-year development Z. Shen, F. Wang, Z. Wang and J. Li 0 (insulation properties) 2021 35 [ 7 ] A DFT study of dissolved gas (C 2 H 2 , H 2 , CH 4 ) detection in oil on CuO-modified BNNT X. He, Y. Gui, J. Xie, X. Liu, Q. Wang and C. Tang 1 (novel method and material) 2020 33 [ 40 ] Dispersion Behavior and Breakdown Strength of Transformer Oil Filled with TiO 2 Nanoparticles E. Atiya, D.-E. Mansour, R. Khattab and A. Azmy 0 (insulation properties) 2015 29 [ 41 ] A review of dissolved gas analysis measurement and interpretation techniques N. A. Bakar, A. Abu-Siada and S. Islam 2 (conventional transformer oil) 2014 27 [ 42 ] Adsorption and sensing of CO and C 2 H 2 by S-defected SnS 2 monolayer for DGA in transformer oil: A DFT study H. Cui, P. Jia, X. Peng and P. Li 1 (novel method and material) 2020 26 [ 43 ] Effects of conductivity and permittivity of nanoparticle on transformer oil insulation performance: experiment and theory W. Sima, J. Shi, Q. Yang, S. Huang and X. Cao 0 (insulation properties) 2015 26 [ 44 ] Dissolved gas analysis evaluation in electric power transformers using conventional methods a review J. Faiz and M. Soleimani 2 (conventional transformer oil) 2017 26 [ 45 ] Effect of semiconductive nanoparticles on insulating performances of transformer oil Y. Du, Y. Lv, C. Li, M. Chen, Y. Zhong, J. Zhou, et al. 3 (older articles) 2012 25 [ 46 ] Table 8 provides a comprehensive overview of most cited article in the top 3 research clusters. Cluster #0 is the largest cluster in this analysis, comprising 176 articles with a silhouette value of 0.930, indicating clear differentiation from other clusters. Labelled as “dielectric strength” through the Log-Likelihood Ratio (LLR) method, this cluster emphasizes improving transformer insulation performance, focusing on enhancing the dielectric properties of insulating fluids. In this cluster, [ 7 , 41 , 44 , 47 , 48 ] were identified as the most influential publications. It includes studies on plant-based insulating fluids, nanoparticle-enhanced transformer oils, and alternative dielectric fluids, reflecting a broad spectrum of approaches to address both technical and environmental challenges. Key articles in this cluster, such as [ 7 ] review of plant-based insulating fluids and [ 41 ] work on TiO 2 nanoparticle-enhanced transformer oil, illustrate the diversity and relevance of research. These studies influence academia and industry by highlighting sustainable solutions, advancing material science, and addressing performance optimization in transformer insulation systems. The focus on bridging experimental results, theoretical modeling, and practical applications makes this cluster pivotal in shaping the future of transformer technology. Cluster #1 is the second-largest cluster, comprising 101 articles with a high silhouette value of 0.970, labeled as “DFT study.” This cluster gained prominence through the application of Density Functional Theory (DFT) in addressing challenges related to transformer oil diagnostics. The most influential publications, such as [ 35 , 36 , 40 , 43 , 49 ] focus on leveraging advanced nanomaterials as sensors for dissolved gas analysis (DGA). These studies introduced novel methods for detecting critical fault gases, including C 2 H 2 , H 2 , and CO, by utilizing DFT to predict material behavior at the atomic scale. In particular, publications [ 35 , 36 ] emphasize the catalytic and adsorption properties of Pd-doped materials, showcasing their potential for enhanced gas detection in transformer diagnostics. These articles collectively demonstrate innovative approaches involving nanomaterial design, adsorption behavior, and catalytic properties. They align in their methodology (DFT) and objectives (improving transformer oil fault detection), forming a cohesive narrative within the cluster and highlighting their collective impact on advancing transformer diagnostics. Cluster #2 is the third-largest cluster, consisting of 70 articles with a silhouette value of 0.976, indicating clear differences from other clusters in the network. It is labeled as “Power Transformer” by the Log-Likelihood Ratio (LLR) test method. This cluster emphasizes advancements in dissolved gas analysis (DGA), focusing on fault diagnosis and condition monitoring in power transformers. The most influential articles in this cluster include [ 42 , 45 , 50 – 52 ], which highlight the importance of innovative diagnostic approaches. Research within this cluster discusses the evolution of DGA techniques, starting from traditional measurement and interpretation methods to cutting-edge applications like advanced sensors and artificial intelligence. Articles [ 42 , 45 , 52 ] provide comprehensive reviews of DGA methods, stressing the importance of real-time monitoring and precision. Meanwhile, [ 50 , 51 ] explore novel approaches such as Pd-decorated ZnO nanorod sensors and genetic algorithms for optimizing diagnostic accuracy. Collectively, this cluster underscores the need for continuous innovation to enhance transformer reliability, extend service life, and reduce downtime, making DGA a cornerstone of transformer maintenance strategies. Table 8 Most cited article of the top 3 cluster that emerged from Document Co-citation Analysis Cluster Title (Journal Published) Most cited article Ref Author Publication Year Cluster #0 A critical review of plant-based insulating fluids for transformer: 30-year development ( Renewable and Sustainable Energy Reviews ) Z. Shen, F. Wang, Z. Wang and J. Li 2021 [ 7 ] Dispersion Behavior and Breakdown Strength of Transformer Oil Filled with TiO 2 Nanoparticles ( IEEE Transactions on Dielectrics and Electrical Insulation ) E. Atiya, D.-E. Mansour, R. Khattab and A. Azmy 2015 [ 41 ] Effects of conductivity and permittivity of nanoparticle on transformer oil insulation performance: experiment and theory ( IEEE Transactions on Dielectrics and Electrical Insulation ) W. Sima, J. Shi, Q. Yang, S. Huang and X. Cao 2015 [ 44 ] Alternative Dielectric Fluids for Transformer Insulation System: Progress, Challenges, and Future Prospects ( IEEE Access ) U. M. Rao, I. Fofana, T. Jaya, E. M. Rodriguez-Celis, J. Jalbert, and P. Picher 2019 [ 48 ] Effect of nanoparticles on transformer oil breakdown strength: experiment and theory ( IET Science, Measurement & Technology ) M. E. Ibrahim, A. M. Abd-Elhady, and M. A. Izzularab 2016 [ 47 ] Cluster #1 Pd-doped MoS 2 monolayer: A promising candidate for DGA in transformer oil based on DFT method ( Applied Surface Science ) H. Cui, X. Zhang, G. Zhang and J. Tang 2019 [ 36 ] Dissolved gas analysis in transformer oil using Pd catalyst decorated MoSe 2 monolayer: A first-principles theory ( Sustainable Materials and Technologies ) H. Cui, D. Chen, Y. Zhang and X. Zhang 2019 [ 35 ] A DFT study of dissolved gas (C 2 H 2 , H 2 , CH 4 ) detection in oil on CuO-modified BNNT ( Applied Surface Science ) X. He, Y. Gui, J. Xie, X. Liu, Q. Wang and C. Tang 2020 [ 40 ] Adsorption and sensing of CO and C 2 H 2 by S-defected SnS 2 monolayer for DGA in transformer oil: A DFT study ( Applied Surface Science ) H. Cui, P. Jia, X. Peng and P. Li 2020 [ 43 ] Adsorption of SO 2 and NO 2 molecule on intrinsic and Pd-doped HfSe 2 monolayer: A first-principles study ( Applied Surface Science ) H. Cui, P. Jia, and X. Peng 2020 [ 49 ] Cluster #2 A review of dissolved gas analysis measurement and interpretation techniques ( IEEE Electrical Insulation Magazine ) N. A. Bakar, A. Abu-Siada and S. Islam 2014 [ 42 ] Dissolved gas analysis evaluation in electric power transformers using conventional methods a review ( IEEE Transactions on Dielectrics and Electrical Insulation ) J. Faiz and M. Soleimani 2017 [ 45 ] Dissolved Gas Analysis Equipment for Online Monitoring of Transformer Oil: A Review ( Sensors ) S. Bustamante, M. Manana, A. Arroyo, P. Castro, A. Laso, and R. Martinez 2019 [ 52 ] Dissolved hydrogen gas analysis in transformer oil using Pd catalyst decorated on ZnO nanorod array ( Sensors and Actuators B: Chemical ) A. S. M. I. Uddin, U. Yaqoob, and G.-S. Chung 2016 [ 51 ] Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine ( IEEE Transactions on Dielectrics and Electrical Insulation ) J. Li, Q. Zhang, K. Wang, J. Wang, T. Zhou, and Y. Zhang 2016 [ 50 ] A burst detection analysis was performed to identify the most influential or landmark publication in the field that had drawn researcher’s attention. The top 10 major bursts in citations, with the duration shows in Table 9 . The top 3 articles with the highest burst are (N. A. Bakar, A. Abu-Siada and S. Islam, 2014; E. Atiya, D.-E. Mansour, R. Khattab, and A. Azmy, 2015; W. Sima, J. Shi, Q. Yang, S. Huang, and X. Cao, 2015). (N. A. Bakar, A. Abu-Siada and S. Islam, 2014) [ 42 ], explores the use of Dissolved Gas Analysis (DGA) for diagnosing faults in transformers by monitoring gas concentrations in insulating oil. This method is recognized for its reliability and effectiveness in detecting incipient faults, making it widely influential in both research and practical applications. (E. Atiya, D.-E. Mansour, R. Khattab, and A. Azmy, 2015) [ 41 ] investigates the enhancement of transformer oil insulation properties using TiO 2 nanoparticles. It emphasizes improvements in breakdown strength and dielectric performance, making it a significant study in the adoption of nano-dielectric fluids for modern transformers. The third articles with high burstiness is (W. Sima, J. Shi, Q. Yang, S. Huang, and X. Cao, 2015) [ 44 ] provides both experimental and theoretical insights into how nanoparticle characteristics influence insulation performance. It underscores the role of nanoparticle conductivity and permittivity in optimizing oil properties, contributing to a deeper understanding of nano-enhanced transformer oil. Table 9 Top ten document burst computed via document co-citation analysis (DCA) Author Year Title Journal Burst Strength Begin End Ref N. A. Bakar, A. Abu-Siada and S. Islam 2014 A review of dissolved gas analysis measurement and interpretation techniques IEEE Electrical Insulation Magazine 11.93 2015 2019 [ 42 ] E. Atiya, D.-E. Mansour, R. Khattab, and A. Azmy 2015 Dispersion Behavior and Breakdown Strength of Transformer Oil Filled with TiO 2 Nanoparticles IEEE Transactions on Dielectrics and Electrical Insulation 11.47 2016 2020 [ 41 ] W. Sima, J. Shi, Q. Yang, S. Huang, and X. Cao 2015 Effects of conductivity and permittivity of nanoparticle on transformer oil insulation performance: experiment and theory IEEE Transactions on Dielectrics and Electrical Insulation 10.28 2016 2020 [ 44 ] J. Li, Z. Zhang, P. Zou, S. Grzybowski, and M. Zahn 2012 Preparation of a vegetable oil-based nanofluid and investigation of its breakdown and dielectric properties IEEE Electrical Insulation Magazine 8.93 2013 2017 [ 26 ] Y. Z. Lv, Y. Zhou, C. R. Li, Q. Wang, and B. Qi 2014 Recent progress in nanofluids based on transformer oil: preparation and electrical insulation properties IEEE Electrical Insulation Magazine 7.49 2015 2019 [ 53 ] S. Okabe, G. Ueta, and T. Tsuboi 2013 Investigation of aging degradation status of insulating elements in oil-immersed transformer and its diagnostic method based on field measurement data IEEE Transactions on Dielectrics and Electrical Insulation 6.42 2013 2018 [ 54 ] J. S. N’cho, I. Fofana, Y. Hadjadj, and A. Beroual 2016 Review of Physicochemical-Based Diagnostic Techniques for Assessing Insulation Condition in Aged Transformers Energies 5.78 2017 2021 [ 55 ] J. Li, Q. Zhang, K. Wang, J. Wang, T. Zhou, and Y. Zhang 2016 Optimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine IEEE Transactions on Dielectrics and Electrical Insulation 5.44 2017 2021 [ 50 ] H. Cui, G. Zhang, X. Zhang, and J. Tang 2019 Rh-doped MoSe 2 as a toxic gas scavenger: a first-principles study Nanoscale Advances 5.16 2019 2024 [ 56 ] J.-C. Lee, H.-S. Seo, and Y.-J. Kim 2012 The increased dielectric breakdown voltage of transformer oil-based nanofluids by an external magnetic field International Journal of Thermal Sciences 4.72 2013 2017 [ 57 ] Discussion This paper conducted a comprehensive scientometric analysis of insulating transformer oil research to explore its evolution, thematic focus, and influential contributions over the period 1970–2024. By examining publication trends, research clusters, and key works, this study provides an in-depth understanding of the field's development and its intersections with various scientific disciplines. (i) Overall Publication Trends in Terms of Output The publication trends demonstrate a significant and steady growth in research output, signaling increasing global interest in insulating transformer oils, particularly over the last two decades. The environmental concerns surrounding traditional mineral oils, such as their non-biodegradability and fire hazards, have further propelled interest in bio-based insulating fluids The biodegradability of insulating liquids affects how long they remain in the environment before breaking down. Mineral oils typically take between 1 and 10 years to decompose, whereas vegetable-based oils like canola oil degrade much faster, typically within 4 to 48 weeks, demonstrating their lower environmental persistence [ 58 ]. Biodegradable natural esters are capable of meeting the performance criteria required for insulating oils in power equipment applications [ 59 ]. This surge is closely tied to advancements in nanotechnology, which have enabled the development of nano-enhanced transformer oils with superior electrical and thermal properties, as well as the push for more sustainable and high-performance insulation solutions. A review paper [ 13 ] reports that most studies observe an enhancement in the flash point of mineral oil when nanoparticles are incorporated. Studies from [ 60 ], investigated the impact of nanocomposites and single nanoparticles on key thermal parameters such as the convective heat transfer coefficient (CHTC), Nusselt number (Nu), Grashof number (Gr), and Rayleigh number (Ra). Their findings revealed that titanium dioxide (TiO 2 ​) nanoparticles had a more pronounced effect on enhancing both CHTC and Nu compared to (ZnFe 2 ​O 4 ​) nanoparticles, highlighting the superior thermal performance of TiO 2 ​​-doped nanocomposites in transformer oil applications [ 60 ]. Palm-based transformer oil is recognized for its enhanced fire safety properties, exhibiting a flash point above 280°C and a fire point exceeding 300°C, both of which are considerably higher than those of mineral oil [ 61 ]. This improvement suggests that nanoparticle additives can increase the thermal stability and safety margins of transformer oil-based insulating fluids. Between 2015 and 2024, several research landscapes shifted toward exploring bio-based and nano-enhanced insulating oils, a trend supported by the urgent need for environmentally friendly and efficient alternatives to mineral oil in transformers. This shift is reflected in studies like those by Zakaria et al., who investigated palm-based oils as biodegradable alternatives for power transformers in Malaysia [ 29 ]. Additionally, research by Peppas et al. highlighted the improved performance of nanofluids, such as those incorporating nanoparticles, when compared to traditional oils [ 62 ]. Moreover, Mohd et al. explored the potential applications of palm oil products as electrical insulating mediums, further supporting the move towards sustainable alternatives in transformer technology [ 61 ]. The IEEE Transactions on Dielectric and Electrical Insulation stands out as the leading journal in this field, contributing 15.39% of total publications. Its dominance is attributed to its niche focus on dielectric materials, its global visibility as part of the IEEE ecosystem, and its role as a trusted platform for publishing cutting-edge research in transformer insulation technology. The journal also bridges practical engineering solutions with theoretical advancements, making it a preferred venue for academics and industry practitioners alike. Geographically, India and China emerge as prominent contributors to research on insulating transformer oils, driven by their substantial investments in energy infrastructure and policies that foster innovation. In India, government-backed institutions such as the Indian Institutes of Technology (IITs) and the Indian Institute of Science (IISc) play a pivotal role in advancing this field. Studies such as the investigation into thermal properties of graphene-silicone oil nanofluids [ 63 ] and the enhancement of ester-based insulating oils with nanocomposites [ 64 ] exemplify India’s commitment to applied research in this domain. These works are often carried out in collaboration with industry leaders like the Power Grid Corporation of India, underlining the importance of academic-industry partnerships in addressing national energy challenges [ 63 – 65 ]. Similarly, China has emerged as a prominent leader in transformer insulation research, supported by substantial government funding and policies aimed at advancing technological innovations in the energy sector. Research contributions include innovative approaches to modifying insulating oils for enhanced electrical and thermal performance, such as the development of KH550-TiO 2 -enhanced natural ester oils [ 66 ] and investigations into nano TiO 2 -modified transformer oils for extreme cold conditions [ 67 ]. These efforts reflect China’s emphasis on leveraging nanotechnology to create sustainable and efficient alternatives to traditional mineral oils, supported by policies encouraging academia-industry collaboration [ 59 , 66 – 68 ]. The prioritization of transformer insulation research by both countries highlights their strategic focus on modernizing power systems to meet rising energy demands while addressing environmental sustainability. Additionally, these countries lead global efforts in nanotechnology research, which has direct applications in insulating fluids, further reinforcing their dominance in this area. The multidisciplinary nature of this research field is underscored by its wide-reaching impact across journal quartiles, ranging from Q1 to Q3, reflecting the integration of diverse fields such as electrical engineering, material science, molecular physics, and environmental science. This underscores the need for cross-disciplinary collaboration to address the challenges of modern transformer insulation systems, particularly with the incorporation of nano-enhanced and bio-based insulating fluids. Such efforts are essential for meeting both performance and sustainability goals in the energy sector. (ii) Central Topics/Clusters In this scientometric review, Cluster 1, categorized as “Density Functional Theory (DFT) Study,” has emerged as a dominant and influential research area. The primary reason for its prominence lies in the ability of DFT methods to provide molecular-level insights into gas adsorption and sensing mechanisms. These studies focus on gases like C₂H₂, H₂, CH₄, SO₂, and NO₂, which are key indicators for Dissolved Gas Analysis (DGA)—a critical diagnostic technique for transformer health monitoring [ 35 , 36 , 40 , 43 , 49 ]. By using computational approaches, these studies uncover detailed adsorption behaviors, providing predictive and cost-effective solutions compared to experimental methods. The articles within Cluster 1 highlight the development of advanced nanomaterials to enhance gas detection sensitivity. For example, Pd-doped MoS₂ and Pd-doped HfSe₂ demonstrate the role of palladium doping in improving adsorption selectivity for fault gases like C₂H₂ and H₂ [ 36 , 49 ]. Meanwhile, modifications to monolayers such as MoSe₂ and SnS₂ with sulfur defects explore strategies for selective detection of gases [ 35 , 43 ]. Expanding beyond monolayers, CuO-modified boron nitride nanotubes (BNNTs) showcase their potential due to their high surface area and strong adsorption capability [ 40 ]. These innovations provide transformative improvements for DGA, offering practical solutions to enhance transformer fault diagnosis. The practical impact of Cluster 1 is significant because transformer failures caused by undetected faults can lead to severe economic and operational consequences. DFT-based studies contribute to improving the sensitivity, reliability, and accuracy of gas detection methods, bridging the gap between theoretical research and real-world applications. By advancing materials like Pd-doped MoS₂ and CuO-BNNTs, these studies propose efficient, next-generation diagnostic systems that support better transformer maintenance and grid reliability [ 36 , 40 , 43 ]. Another reason for the dominance of this cluster is its recent publication trend, with an average year of 2020. These recent studies reflect ongoing advancements in transformer oil diagnostics and computational materials research. By incorporating cutting-edge technologies and novel materials, these articles are more likely to attract attention and citations from the research community, maintaining their high influence [ 35 , 43 , 49 ]. The alignment of these studies with current technological needs highlights their relevance in the energy sector. Moreover, the interdisciplinary nature of DFT studies enhances their appeal to a broader audience. These studies connect key disciplines such as materials science, computational chemistry, and electrical engineering, which increases their visibility and applicability. Compared to older, more specialized clusters like Cluster 3 (average year: 2011), DFT studies remain at the forefront of transformer oil research by pushing the boundaries of innovation. Last but not least, Cluster 1 stands out due to its methodological importance, practical relevance, and alignment with current research trends. The top-cited articles demonstrate the potential of DFT-based studies to revolutionize transformer fault diagnostics through advanced material design and computational precision. By addressing key challenges in DGA, these studies contribute to both academic advancements and industry applications, solidifying their influence in transformer insulation research [ 35 , 36 , 40 , 43 , 49 ]. The dominant research themes in transformer oil studies include dielectric strength (Cluster 0), alongside emerging trends such as Density Functional Theory (DFT) studies and power transformer advancements. Through document cluster analysis, Cluster 0 emerged as a major focus due to its relevance to improving insulation performance and overall reliability in power systems. The studies within this cluster focus on traditional but critical aspects, including the breakdown strength of transformer oils, the integration of nanotechnology, and the development of sustainable insulating fluids. The analysis identified five highly cited articles [ 7 , 41 , 44 , 47 , 48 ] that contribute significantly to this research direction. These publications are categorized into four primary areas. First, dielectric strength studies [ 69 – 71 ] focus on enhancing the breakdown voltage of insulating oils, which remains central to improving the efficiency and safety of transformers. Dielectric strength is a fundamental property for insulating fluids, as it directly determines their ability to withstand high voltages without failure. Second, the innovation of nanofluids has emerged as a promising trend. Studies such as [ 72 – 74 ] explore the role of nanoparticles—including TiO₂, ZnO, and hybrid nanomaterials—in enhancing the electrical and thermal performance of transformer oils. By dispersing nanoparticles in traditional oils, researchers have achieved notable improvements in breakdown voltage, thermal conductivity, and dissipation factor, paving the way for nano-enhanced insulating fluids. These advancements address the limitations of conventional oils while improving transformer efficiency and performance. The third key focus within Cluster 0 is the shift toward natural esters and bio-based fluids as sustainable alternatives to mineral oil. Articles like [ 9 , 14 , 75 ] align with global sustainability efforts by investigating the use of plant-based and biodegradable insulating fluids. These studies highlight the environmental benefits of natural esters, such as reduced carbon footprint, biodegradability, and safety, while demonstrating comparable or superior dielectric properties to mineral oils. Finally, comprehensive review studies [ 4 , 7 , 8 , 11 ] play a critical role in this cluster. These highly cited reviews consolidate experimental findings, theoretical advancements, and emerging trends in transformer oil research. By synthesizing data across multiple studies, they provide clear insights into existing challenges and highlight future research directions, ensuring their continued relevance and impact. In summary, Cluster 0 reflects a balance between traditional research themes and emerging innovations. The interconnected focus on dielectric strength, nanofluid enhancements, and sustainable insulating fluids highlights a comprehensive approach to advancing transformer technology. These studies have received significant short-term attention and demonstrate the critical need to improve reliability, efficiency, and environmental sustainability in transformer insulation systems. (iii) Most Influential Publications in These Domains The document burst analysis reveals a clear trajectory of emerging research topics, with older works being succeeded by newer, impactful studies. The article by [ 26 ] stands out as an early foundational work that gained widespread attention from 2013 to 2017. This study focused on enhancing transformer oil properties using Fe₃O₄ nanoparticles, demonstrating a significant improvement in breakdown voltage under power frequency and lightning impulse conditions. By aligning with the global trend of replacing mineral oils with eco-friendly alternatives, such as vegetable oils, it addressed pressing concerns about biodegradability, safety, and performance, thus cementing its relevance. Following this foundational work, the research landscape evolved to encompass broader advancements. [ 42 ] emphasized the development of AI-based techniques for Dissolved Gas Analysis (DGA), a critical diagnostic tool for transformer health monitoring. This shift toward intelligent systems reflects the increasing demand for reliable and automated fault detection methods. Similarly, [ 41 ] highlighted the role of TiO₂ nanoparticles in enhancing transformer oil's dielectric strength, emphasizing nanotechnology's potential to improve breakdown voltage and stability, which are directly linked to system reliability and longevity. Complementing these advancements, [ 44 ] explored the interplay between nanoparticle conductivity, permittivity, and insulation performance, offering both experimental insights and theoretical frameworks for optimizing nanofluids. Together, these articles showcase a cohesive evolution in transformer research, from focusing on performance improvement to incorporating resilience and advanced diagnostics. This convergence of diagnostics, materials science, and intelligent systems not only addresses critical challenges but also provides a roadmap for integrating cutting-edge technologies into transformer applications, ensuring reliability and sustainability in the energy sector Conclusion This scientometric analysis systematically maps the research landscape of transformer oil studies from 1970 to 2024, identifying key trends, influential topics, and impactful contributions. The review reveals that dielectric strength, Density Functional Theory (DFT) studies, and power transformer diagnostics are dominant themes driving innovation in transformer oil technology. These topics highlight the growing emphasis on sustainability, reliability, and advanced diagnostic capabilities in modern energy systems. The main topic of this paper underscores the critical role of transformer oils in maintaining power system efficiency and reliability. Descriptive analysis highlights the geographical distribution of transformer oil research, with significant contributions from countries such as India, China, and the United States, reflecting their strong focus on energy infrastructure and innovation. Over time, the volume of publications has increased significantly, particularly in the last decade, coinciding with a global shift toward sustainable and environmentally friendly insulating fluids. This trend mirrors the rising regulatory pressures and advancements in material science, further accelerating the development of bio-based and nano-enhanced transformer oils. Recent advancements in bio-based and nano-enhanced insulating fluids have addressed both performance limitations and environmental concerns. These developments align with the global energy sector's shift toward sustainable solutions, driven by stricter regulations and the need for modernized power infrastructure. The dual focus on enhancing dielectric properties and ensuring environmental compatibility reflects the transformative potential of these innovations. The compiled clusters from the co-citation analysis contribute significantly to the understanding of this research area. For instance, Cluster 0 (Dielectric Strength) emphasizes foundational studies and advancements in nanofluids, demonstrating their role in enhancing dielectric properties. Cluster 1 (DFT Studies) highlights the integration of computational techniques for improved dissolved gas analysis (DGA), advancing transformer fault diagnostics. Cluster 2 (Power Transformer) focuses on real-time monitoring and AI-driven diagnostics, showcasing the convergence of technology and material innovation. Together, these clusters provide a cohesive narrative of how research has evolved, addressing challenges in transformer insulation and diagnostics. The data for this scientometric analysis was obtained from the Web of Science Core Collection (WOSCC), a widely recognized database for its comprehensive and high-quality indexing of scientific and social science publications. A total of 2,801 publications were analyzed across various journals, disciplines, and regions, providing a robust foundation for identifying key trends, influential themes, and seminal works in transformer oil research. While the use of WOSCC ensured access to a broad and reliable dataset, it is acknowledged that reliance on a single database may introduce a degree of publication bias. WOSCC is particularly noted for its higher publication standards and its expansive coverage compared to other datasets [ 76 , 77 ]. To further strengthen future bibliometric studies, researchers should consider employing more rigorous keyword selection and manual data reviews to minimize the inclusion of irrelevant studies [ 78 ]. Expanding the analysis by incorporating additional databases, such as Scopus and PubMed, could help identify missing critical information and provide a more comprehensive perspective on transformer oil research. This approach would enable comparative analyses across datasets, ensuring a more holistic understanding of the research landscape. Co-citation analysis played a pivotal role in shaping this paper by identifying relationships between publications and clustering them into thematic groups. This method enabled the visualization of intellectual structures within the research field, highlighting connections between traditional and emerging topics. For transformer oil research, co-citation analysis not only mapped the evolution of key themes but also pinpointed impactful studies that bridge disciplines such as material science, computational chemistry, and electrical engineering. In conclusion, this scientometric review emphasizes the transformative potential of integrating advanced diagnostics, sustainable materials, and innovative technologies into transformer oil systems. It provides a comprehensive analysis of the research landscape of transformer insulating oils, examining trends over time, journal contributions, co-citations, prominent authors, countries, institutions, keywords, and references. The scientometric approach employed in this study contributes to the body of knowledge by offering a holistic view of transformer oil research. By synthesizing historical trends and identifying emerging research directions, this paper presents a roadmap for future studies, ensuring continued progress in addressing challenges related to energy reliability and sustainability. This study serves as a valuable resource for academia, industry professionals, and policymakers, offering insights into the overall trends, current status, and potential research questions in the field. Through this comprehensive analysis, a deeper understanding is provided of how transformer oil research is evolving to meet the demands of modern power systems. Limitation and Recommendation This study acknowledges several limitations, including the exclusive use of the Web of Science (WOS) database, which may have led to the omission of relevant works indexed in Scopus or Google Scholar. Additionally, reliance on specific keywords and the inclusion of only English-language publications may have introduced selection bias. To strengthen future research, broader database coverage and manual screening are recommended. Furthermore, based on bibliometric trends and cluster analysis, future directions include promoting international collaborations, especially among leading countries such as China and India, and encouraging interdisciplinary efforts that connect material science, nanotechnology, and smart grid systems. Long-term performance evaluations of green insulating oils, standardized testing methods, and the expanded use of scientometric tools are also vital to enhance data reliability and support strategic research planning. Declarations Declaration of interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Author Contribution Conceptualization, Data Analysis and Manuscript Writing - original draft: **Dzhahirul Zamri** . Software, Research Methodology and Writing - review and editing: **Mohd Iqbal Mohd Noor** . Conceptualization and Formal Analysis: **N. H. Nik Ali** . 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Nik","lastName":"Ali","suffix":""},{"id":550103942,"identity":"36faa418-504e-42b1-930c-712ab72312d3","order_by":3,"name":"Noor Khairin Mohd","email":"","orcid":"","institution":"Malaysian Palm Oil Board (MPOB)","correspondingAuthor":false,"prefix":"","firstName":"Noor","middleName":"Khairin","lastName":"Mohd","suffix":""},{"id":550103948,"identity":"b6ba818c-e1b9-4285-84df-ad8976015433","order_by":4,"name":"Mohammad. A. Hamdan","email":"","orcid":"","institution":"Applied Science Private University","correspondingAuthor":false,"prefix":"","firstName":"Mohammad.","middleName":"A.","lastName":"Hamdan","suffix":""},{"id":550103951,"identity":"e7e7a413-67b2-4298-8206-e3d958122c43","order_by":5,"name":"M. H. Mamat","email":"","orcid":"","institution":"NANO-Electronic Centre (NET), Universiti Teknologi MARA","correspondingAuthor":false,"prefix":"","firstName":"M.","middleName":"H.","lastName":"Mamat","suffix":""}],"badges":[],"createdAt":"2025-11-11 04:23:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8082286/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8082286/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":96818090,"identity":"655b8663-88bc-4bb8-89f6-37a68b6dc14d","added_by":"auto","created_at":"2025-11-26 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14:10:35","extension":"png","order_by":18,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":36324,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8082286/v1/70e22529be81f09adade49e6.png"},{"id":96818098,"identity":"eaff43cf-69be-4676-ae1a-51bce34edd30","added_by":"auto","created_at":"2025-11-26 11:32:36","extension":"xml","order_by":19,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":271656,"visible":true,"origin":"","legend":"","description":"","filename":"11d6d0f8bdd24bd2bff03a77480524371structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8082286/v1/c1752cc0f5adc7b485869a21.xml"},{"id":96818099,"identity":"1801ba23-287b-4d46-9929-64d4b80aace8","added_by":"auto","created_at":"2025-11-26 11:32:36","extension":"html","order_by":20,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":286436,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8082286/v1/8496bcf5f1fc30b746c6c7d8.html"},{"id":96818079,"identity":"ceb2c43b-2314-4b66-b678-0fd282d306e6","added_by":"auto","created_at":"2025-11-26 11:32:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":37477,"visible":true,"origin":"","legend":"\u003cp\u003eMethodological framework\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8082286/v1/d1dafc4b7b03ebe4621445d0.png"},{"id":96917384,"identity":"71885244-3337-4698-bf29-cf75bbdb8714","added_by":"auto","created_at":"2025-11-27 14:09:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":11204,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of research articles regarding transformer oil published annually since 1970\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8082286/v1/c838138d5212a5252aeb2fa4.png"},{"id":96918319,"identity":"908b093d-74f6-41a4-b10a-f59fad425a50","added_by":"auto","created_at":"2025-11-27 14:11:43","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":183120,"visible":true,"origin":"","legend":"\u003cp\u003eNumber of publications produced from countries around the world. Dark blue represents the highest total number of publications, whereas lighter shades represent fewer publications\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-8082286/v1/b585acfb621cbb0735da7a1e.jpeg"},{"id":96818086,"identity":"3909f228-4ae7-4c86-b942-df4de83bfdef","added_by":"auto","created_at":"2025-11-26 11:32:36","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":8025,"visible":true,"origin":"","legend":"\u003cp\u003eThe dataset for oil insulating transformer research reveals significant contributions from various academician\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8082286/v1/972022b996d8070c6670acfe.png"},{"id":96818083,"identity":"f8d5cdf0-38ac-4c81-9956-d93366771690","added_by":"auto","created_at":"2025-11-26 11:32:36","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":14638,"visible":true,"origin":"","legend":"\u003cp\u003eThe dataset for transformer oil research includes publications from a variety of academic journals\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8082286/v1/5196c834bb7e49ad72222eaa.png"},{"id":96918683,"identity":"df25b858-bcbf-4be1-b767-918bd147f658","added_by":"auto","created_at":"2025-11-27 14:12:20","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":326255,"visible":true,"origin":"","legend":"\u003cp\u003eDual map overlay on insulating oil transformer research. Points represent literature, ellipses represent groups of subject matter, and directional lines represent the connection between subjects. Each unique discipline is represented by a different colour\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8082286/v1/5a5e8ef603cee1d50e4d3b0d.png"},{"id":96818082,"identity":"98269c45-b843-485f-a7ea-974cd043ce04","added_by":"auto","created_at":"2025-11-26 11:32:36","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":272917,"visible":true,"origin":"","legend":"\u003cp\u003eDocument Co-citation Analysis on insulating transformer oil research\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8082286/v1/279af794b76b21d1366c13df.png"},{"id":96916942,"identity":"252b41a7-763e-4c5b-9f18-9a1d69d5833d","added_by":"auto","created_at":"2025-11-27 14:09:05","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":213988,"visible":true,"origin":"","legend":"\u003cp\u003eSummary of 12 identified document cluster lifetimes (solid lines). Cluster labels were generated from CiteSpace. Circle size corresponds to cluster size (i.e. number of publications)\u003c/p\u003e","description":"","filename":"floatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8082286/v1/cc6db889411c492494fc2824.png"},{"id":100734846,"identity":"b6ddfb7b-e228-45b8-bd29-cbd21825a609","added_by":"auto","created_at":"2026-01-20 22:18:24","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2513358,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8082286/v1/bcc0bcab-9653-43d0-8089-493ebfeeb755.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Global Trends and Insight in Transformer Oil Insulation Research: A Scientometric Perspective","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTransformer oil is essential for ensuring the reliability and efficiency of high-voltage transformers, which are crucial components in power transmission and distribution networks. Traditionally, mineral oil has been the most commonly used insulating and cooling medium due to its favorable dielectric properties and thermal performance. However, concerns regarding environmental impact and fire safety have prompted the exploration of alternative oils, such as synthetic esters and natural ester-based oils, which offer superior biodegradability and fire resistance. In recent years, transformer oils have gained increased attention not only for their insulating capabilities but also for their potential to extend transformer lifespan and reduce maintenance costs. While these technical advancements are well-documented, there has been limited focus on mapping the evolution of transformer oil research from a scientometric perspective. Understanding the structure of this knowledge domain is essential to uncovering key contributors, collaborations, and emerging themes. Bibliometric tools such as CiteSpace offer a unique opportunity to visualize citation networks, intellectual turning points, and thematic clusters\u0026mdash;helping researchers identify how the field has progressed over time.\u003c/p\u003e\u003cp\u003eDespite the crucial role of transformer oil in maintaining transformer performance over time, the industry faces several challenges. The growing demand for higher-voltage transformers, the integration of renewable energy sources into the power grid, and the push for environmental sustainability have intensified the need for more advanced insulation technologies. Additionally, the evolution of research in this field has been shaped by the ability to track emerging trends, identify intellectual turning points, and visualize knowledge domains. As highlighted by [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e] tools such as CiteSpace have proven instrumental in uncovering pivotal developments and transient patterns in scientific literature, aiding researchers in understanding the progression of transformer oil studies. Furthermore, the predictive effects of structural variation on research impact, as discussed by [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], provide a framework for analyzing how novel approaches\u0026mdash;such as the use of nanoparticles in transformer oil\u0026mdash;may influence future advancements. These bibliometric tools enable a deeper understanding of the interdisciplinary connections within the field and highlight opportunities for innovation.\u003c/p\u003e\u003cp\u003eSeveral bibliometric and review studies have synthesized transformer insulating oil-specific topics, including reviews on oil-based nanofluids as next-generation insulation [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], green nanofluids for transformer applications [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], vegetable-based nanofluids for green transformer insulation [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], plant-based insulating fluids for transformers [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], the impacts of nanotechnology on liquid insulation [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], natural esters and nanofluids as environmentally friendly alternatives to mineral oil [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], recent progress and challenges in transformer oil nanofluid development [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], synthetic ester liquids for transformer applications [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], the application of nanomaterials in transformer oil [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], flash point improvement of mineral oil using nanoparticles [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] and natural esters for green transformers [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Other studies have focused on advancements in nanomaterials for environmental remediation [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and even explored the impact of COVID-19 on the energy sector [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. While several reviews have focused on the physicochemical performance of transformer oils and the integration of nanoparticles, these studies typically lack a bibliometric analysis of research patterns, influential works, and thematic evolutions over time as shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eTherefore, this study aims to conduct a comprehensive bibliometric and scientometric analysis of transformer oil research using CiteSpace, focusing on trends from 1970 to 2024. By examining patterns in publication output, co-authorship networks, and emerging clusters, this paper seeks to provide a data-driven perspective on the evolution of transformer oil insulation research. Scientometric analysis can provide insights into the dynamics and connections between journals, authors, and papers in transformer oil research, based on standards such as the growth of knowledge over time, links between subject areas, and intellectual turning points within a subject [\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. This gap underscores the importance of a bibliometric analysis to uncover leading contributors and track emerging themes within the field. Science mapping techniques, such as those reviewed by [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], provide valuable insights into the structure and dynamics of scientific fields. These methods enable the identification of emerging themes, intellectual turning points, and collaborative networks, offering a comprehensive view of the evolution of transformer oil research. Such tools not only reveal knowledge gaps but also guide future research directions, ensuring the alignment of scientific efforts with industry demands and global challenges. A bibliometric analysis offers a comprehensive approach to mapping the landscape of electrical insulator oil research, providing quantitative insights into publication patterns, citation networks, and influential contributors [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. By analyzing bibliometric data, researchers can identify prolific authors, prominent journals, and the geographical distribution of research efforts. This method also highlights the interdisciplinary nature of the field, illustrating how the research intersects with disciplines such as materials science, nanotechnology, and environmental engineering. Additionally, bibliometric analysis can uncover emerging topics and gaps in the literature, guiding future research and informing policy and practice [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo date, no comprehensive bibliometric study using tools like CiteSpace has been performed on transformer oil insulation research. Existing studies have either focused on broader electrical insulation fields or lacked a science mapping approach. This study fills that gap by analyzing 2,801 publications and identifying research clusters, authorship patterns, and emerging research trends. This paper aims to reveal the descriptive and scientometric analysis conducted on transformer oil research to elucidate the evolution and current state of the field. This work contributes to the field by offering a structured visualization of global research trends, identifying influential authors and institutions, and revealing thematic developments. In doing so, it supports researchers, policymakers, and industry stakeholders in navigating the current research landscape and shaping future directions in transformer oil insulation studies. The findings will contribute to a deeper understanding of the role of transformer fluid in enhancing transformer performance and offer valuable insights for researchers, practitioners, and policymakers seeking to improve the design and implementation of these crucial materials in electrical systems.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSummary of literature review\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAuthor(s)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYear\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFocus Area\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMethod\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eKey Findings\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eGap Identified\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS. N. Suhaimi et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOil-based nanofluids for transformer applications\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNarrative Review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHighlighted nanofluids' potential to enhance dielectric properties\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLacked detailed bibliometric/scientometric trend analysis\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS. O. Oparanti et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGreen nanofluids for transformer insulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNarrative Review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEmphasized sustainability and biodegradability of natural ester-based nanofluids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo in-depth mapping of author/country collaboration or research hotspots\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA. Siddique et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eVegetable-based nanofluids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSystematic Review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eReviewed cost-effective and biodegradable options as future green resources\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLacked visualization of research evolution or influential networks\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZ. Shen et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e30-year development of plant-based fluids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eCritical Review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIdentified long-term development trends and future potential\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDid not address the emerging impact of nanoparticles in insulation systems\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM. Rafiq et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNanotech impact on transformer insulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNarrative Review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDiscussed electrical and thermal property enhancements\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo bibliometric validation of emerging trends or knowledge clusters\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJ. Jacob et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNatural ester and nanofluids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNarrative Review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCombined nanotechnology with bio-based fluids for eco-friendly solutions\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDid not benchmark the topic using CiteSpace or science mapping tools\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eD. Amin et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProgress on nanofluid properties\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNarrative Review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDetailed thermal and electrical improvements in nanofluids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo structural analysis on topic co-occurrence or cluster evolution\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP. Rozga et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSynthetic ester liquids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNarrative Review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eEvaluated synthetic esters for transformer use\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo linkage to nanoparticle integration or scientometric trend evaluation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eM. Rafiq et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTransformer oil nanofluid properties\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNarrative Review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eFocused on electrical, thermal, and physicochemical property enhancement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eBibliometric evolution and leading contributors not covered\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKhoirudin et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eFlash point improvement using nanoparticles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNarrative Review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDemonstrated reduced fire risk in mineral oils with nanoparticle doping\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eScientometric development trajectory not visualized\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS. O. Oparanti et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChallenges in natural ester serviceability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNarrative Review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eProposed keys to enhance serviceability and performance\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo use of bibliometric tools to track solution-oriented research trajectories\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eN. Asghar et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNanomaterials in environmental remediation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSystematic Review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eIncluded bibliometric analysis of synthesis routes and materials\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNot specific to transformer oil applications\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eS. R. Arsad et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2023\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCOVID-19 and AI impact in the energy sector\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAnalytical Review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAssessed pre- and post-pandemic trends and AI roles in energy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eIndirectly relevant\u0026mdash;no specific focus on transformer insulation\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eThis study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGlobal transformer oil research\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eScientometric Review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUsed CiteSpace to identify top authors, countries, clusters\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eFills gap in science mapping\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThis study employs a dual approach, integrating descriptive and scientometric analyses to systematically examine transformer oil research trends, key contributors, and major themes from 1970 to 2024, utilizing data extracted from the Web of Science Core Collection (WOSCC) database, as outlined in the study's flowchart as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003eFor text processing, we extracted data from .\u003cb\u003etxt\u003c/b\u003e files to gather information from manuscript titles, research abstracts, author-provided keywords, and Keywords Plus, which facilitated the creation of a robust dataset for descriptive analysis. Co-citation references, sourced from the WOSCC database, provided additional metadata, forming the basis for our scientometric evaluation. To achieve a comprehensive search, we employed the \u0026ldquo;TS\u0026rdquo; (topic search) function in the Web of Science, focusing on titles, abstracts, keywords, and author information. The search phrase used was \u0026ldquo;TS= ((\u0026ldquo;Transformer oil\u0026rdquo;) OR (\u0026ldquo;Transformer Fluid\u0026rdquo;) OR (\u0026ldquo;Transformer Liquid\u0026rdquo;) OR (\u0026ldquo;Insulating Oil\u0026rdquo;) OR (\u0026ldquo;Electrical Insulator Oil\u0026rdquo;))\u0026rdquo;. This strategic selection ensured a thorough yet manageable scope, reducing complexity that might arise from including every scientific term and avoiding confusion with irrelevant entries.\u003c/p\u003e\u003cp\u003eTo further enhance the validity of the study, non-English articles were excluded to minimize language-related biases. The search was limited to titles, keywords, and abstracts since the WOSCC database did not offer access to full-text articles. Despite this limitation, the selected scope enabled us to effectively capture and analyze relevant literature, while minimizing redundancy in article titles, keywords, or abstracts within the metadata. The methodological framework, rooted in scientometric analysis and a carefully constructed search strategy, provides a solid foundation for visualizing the development and research trends in transformer oil literature, highlighting the strengths and thoroughness of the approach adopted in this study\u003c/p\u003e\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData Analysis\u003c/h2\u003e\u003cp\u003eThe data analysis for insulating transformer oil research was divided into two main segments: descriptive analysis and scientometric analysis [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eDescriptive Analysis\u003c/h3\u003e\n\u003cp\u003eThe descriptive analysis focused on the annual number of publications, the journals, authors and countries involved in the research. It provided insights into the geographical distribution and highlighting trends over time. This segment aimed to identify key contributors and the evolution of research interest in transformer oil.\u003c/p\u003e\n\u003ch3\u003eScientometric Analysis\u003c/h3\u003e\n\u003cp\u003eThe scientometric analysis utilized CiteSpace 6.3 for Windows for visualization and knowledge graph analysis, following methodologies outlined by [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] and [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. This tool facilitated the creation of bibliometric networks through various analyses:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDual-Map Overlay Analysis\u003c/b\u003e: This method visually categorized the literature into cited and citing journals, mapping the relationships between them. The sizes of the ovals in the map represent the volume of publications and citation counts, respectively, while the thickness of the lines indicates citation frequency across disciplines. This analysis provided a visual representation of how insulating transformer oil research is interconnected across various scientific fields.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eDocument Co-Citation Analysis (DCA)\u003c/b\u003e: This analysis identified instances where two sources were simultaneously cited in a paper, providing metrics such as burstiness (indicating sudden increases in citations), centrality (highlighting influential articles), and sigma (a composite measure of centrality and burstiness to assess the novelty and impact of research). Articles with centrality values over 1 were deemed crucial, acting as connectors within the network. The clusters in the DCA were determined using the Log-Likelihood Ratio (LLR), ensuring optimal cluster uniqueness and coverage. The network structure was visualized through timeline and cluster views, aiding in understanding the evolution and interconnections within the research domain. The quality and coherence of the clusters were evaluated using the Modularity Q index and the Silhouette Metric, which assess the reliability and homogeneity of the clusters, respectively.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eBy integrating both descriptive and scientometric analyses, this study provides a detailed and nuanced understanding of the current state and historical development of transformer oil research, identifying key trends, influential works, and potential future directions [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eIn total, the search outlined in the methodology section retrieved 2,801 publications related to insulating transformer oil research. These publications boast an h-index of 69. Collectively, these studies have been cited 42,210 times, resulting in an average citation rate of 15.07 citations per article. The dataset for insulating transformer oil research spans from 1970 to 2024.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eAs shown in the Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the first publication about electrical insulating oil or transformer oil research emerged in 1970. From that point until around 2010, research activity in this area increased gradually, maintaining a low profile with the annual number of publications rarely exceeding 50. However, the trend significantly shifted after 2010, with a steep increase in research output. This sudden rise can be attributed to various factors, including the global emphasis on renewable energy integration, environmental concerns over the use of traditional mineral oils, and the search for alternative, more sustainable transformer oils such as natural and synthetic esters [\u003cspan additionalcitationids=\"CR27\" citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe graph clearly demonstrates that from 2010 onward, the number of publications surged dramatically, peaking at around 250 publications in 2023. The heightened interest in transformer oil research can be explained by the following developments in the field:\u003c/p\u003e\u003cp\u003e\u003col\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eThe Shift Towards Renewable Energy Systems\u003c/b\u003e: The growing need for reliable insulation materials in power transformers that can support high voltage levels required for integrating renewable energy sources has become a key focus area. Researchers have explored transformer oil modifications to enhance their dielectric and thermal performance under such new operating conditions [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eNanotechnology Integration\u003c/b\u003e: The incorporation of nanoparticles into transformer oils has gained traction as a method to boost breakdown strength, thermal conductivity, and overall stability of transformer fluids, leading to improved efficiency and longevity of transformers. This aspect has drawn substantial research interest, as reflected in numerous studies published over the past decade [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003cspan\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEnvironmental Concerns and Safety Regulations\u003c/b\u003e: Stringent environmental regulations and the need for more sustainable transformer fluids have led to a shift from conventional mineral oils to biodegradable and less flammable natural esters and synthetic oils [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. These efforts have been driven by the focus on green technology and safety standards, further boosting research in this area.\u003c/p\u003e\u003c/li\u003e\u003c/span\u003e\u003c/ol\u003e\u003c/p\u003e\u003cp\u003eIn the period between 2010 and 2024, the year 2023 stands out as the peak in terms of publication output, with the number of papers nearing 250. This peak could be associated with an increased global focus on energy efficiency, digitalization in transformer monitoring, and the advancement of smart grid technologies. The COVID-19 pandemic also played a role, as it disrupted global energy systems and prompted a reevaluation of energy infrastructure resilience, including the use of more advanced insulation materials in transformers to cope with fluctuating loads and extreme weather events [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eOverall, this trend suggests a growing recognition of the importance of transformer oil research, not only in ensuring reliable power system performance but also in addressing emerging challenges in energy transmission and distribution. It highlights the dynamic and evolving nature of the field, reinforcing the validity of conducting further in-depth scientometric studies to track developments and identify future research directions in transformer oil technologies.\u003c/p\u003e\u003cp\u003eThe dataset for transformer oil research spans across multiple countries, reflecting a global interest in the field. The geographical distribution of publications related to transformer oil research is visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The map chart uses a blue color scale to represent the number of records contributed by each country, ranging from 1 to 975 publications.\u003c/p\u003e\u003cp\u003eThe knowledge mapping investigation, conducted using CiteSpace, highlights significant contributions from various countries worldwide. The People's Republic of China leads as the largest contributor with 975 records, accounting for approximately 34.81% of the total 2,801 publications. This underscores China's prominent role in advancing transformer oil research. Following China, India ranks second with 289 publications (10.32%), showcasing substantial research efforts.\u003c/p\u003e\u003cp\u003eThe United States comes third, contributing 197 publications (7.03%), emphasizing its active participation in the global research community. Japan and Russia also make notable contributions with 146 (5.21%) and 150 (5.36%) publications, respectively. Other countries such as South Korea (86, 3.07%), Malaysia (85, 3.04%), Iran (83, 2.96%), and Poland (80, 2.86%) also reflect robust research engagement in this field.\u003c/p\u003e\u003cp\u003eEuropean nations, including England (81, 2.89%), France (60, 2.14%), and Germany (34, 1.21%), further demonstrate the widespread international focus on transformer oil research. These contributions highlight the global interest and cooperative efforts to enhance transformer insulation technologies, which are critical for energy infrastructure development.\u003c/p\u003e\u003cp\u003eIn conclusion, the geographical distribution, as illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, reveals China's dominant role alongside significant contributions from India, the United States, Japan, and Russia, among others. This international collaboration signifies the shared goal of improving transformer oil technology for better energy efficiency and reliability.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePublication Affiliations\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAffiliations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRecord Count\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCountry\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChongqing University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e240\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eState Grid Corporation of China\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e132\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBeijing, China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNorth China Electric Power University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e106\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eBeijing, China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eXi An Jiaotong University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e100\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eXi,An, China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEgyptian Knowledge Bank EKB\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e96\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEgypt\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eSouthwest University China\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e80\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChina\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChina Southern Power Grid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e74\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGuangzhou, China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIndia Institute of Technology System\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eIndia\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRussian Academy of Sciences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e69\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMoscow, Russia\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGuangxi University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNanning, Guangxi China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eOver the study period, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e highlights the top ten affiliations that have significantly contributed to transformer oil research. The data clearly shows that Chinese institutions dominate the field, followed by contributions from Egypt, India, and Russia. Chongqing University leads the list with 240 publications, establishing its position as a major research hub in this domain. The State Grid Corporation of China, headquartered in Beijing and recognized globally for its expertise in electricity transmission, smart grids, and renewable energy integration, ranks second with 132 publications, reflecting its substantial impact despite its specialized focus.\u003c/p\u003e\u003cp\u003eSeveral other institutions exhibit comparable publication output, with between 70 and 100 records. These include Xi An Jiaotong University (China), the Egyptian Knowledge Bank (EKB), Southwest University (China), China Southern Power Grid (Guangzhou, China), and the Indian Institutes of Technology (India). The diversity of these affiliations underscores the international nature of transformer oil research, with academic and industry-driven institutions from various regions contributing meaningfully to the field. At the lower end of the top ten, the Russian Academy of Sciences (69 publications) and Guangxi University (60 publications) demonstrate that, while smaller in output, their inclusion signals broad global engagement in this area of study.\u003c/p\u003e\u003cp\u003eFrom a scientometric perspective, identifying the most productive affiliations provides valuable insights into the geographical distribution of research activity and the leading contributors to scientific advancement in transformer oil technology. This information is crucial for mapping key research centers, fostering international collaborations, guiding funding strategies, and recognizing the institutions that shape innovation and discourse in this field.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eAuthors Productivity\u003c/h3\u003e\n\u003cp\u003eAs depicted in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, the majority of the top 10 authors are actively engaged in electrical power system engineering and transformer oil research. Leading the list is Kopcansky Peter, with 43 publications, whose work focuses on magnetic nanoparticles, liquid crystals, and biomedicine at the Institute of Experimental Physics, Slovak Academy of Sciences in Kosice, Slovakia. Following closely is Li Chengrong from the North China Electric Power University (NCEPU), who has published 40 papers, specializing in partial discharge, insulation, and transformer technologies. In third place is Rajnak Michal, with 36 publications, primarily researching physics, colloids, magnetism, ferrofluids, and dielectric liquids.\u003c/p\u003e\u003cp\u003eIn addition to these top contributors, several other researchers are approaching 35 publications, reflecting their ongoing contributions to transformer oil studies. The fewest publications among the top ten belong to Zahn Markus, affiliated with the Massachusetts Institute of Technology (MIT) School of Engineering. He has authored 28 papers, primarily in nanotechnology, transformers, and high voltage engineering. Although MIT\u0026rsquo;s researcher has fewer publications, achieving over 20 publications is still a notable accomplishment in this field.\u003c/p\u003e\u003cp\u003eIn summary, all the top ten authors have demonstrated significant expertise in transformer and insulation technology research, with each having published more than 20 papers. This demonstrates that the study of transformer oil and related technologies is an area of growing interest, as researchers from various countries continue to explore and contribute to this field.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eList of top ten authors with their publication\u0026rsquo;s details.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResearcher Profiles\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eRecords Count\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eResearch Areas\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAffiliations\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCountries\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003etang, chao\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEngineering,\u0026nbsp;Physics,\u003c/p\u003e\u003cp\u003eBiochemistry \u0026amp; Molecular\u0026nbsp;Materials Science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eHong Kong University of Science \u0026amp; Technology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBeijing, Peoples R China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eChen, Weigen\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEngineering,\u0026nbsp;Materials Science,\u0026nbsp;Physics,\u0026nbsp;Energy \u0026amp; Fuels\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChongqing University\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eChongqing, Peoples R China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eliu, yiran\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEngineering,\u0026nbsp;Computer Science, Physics \u0026amp; Material Science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInstitute of High Energy Physics, CAS.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eJapan\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZhang, Chaohai\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e30\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTransformer, Renewable Energy Technologies \u0026amp; Power Quality\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNanjing University of Aeronautics \u0026amp; Astronautics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNanjing, China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLi, Chengrong\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e40\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePartial Discharge, Insulation \u0026amp; Transformer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNorth China Electric Power University, NCEPU\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eBeijing, Peoples R China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLiu, Jiefeng\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eElectrical \u0026amp; Electronics Engineering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eGuangxi University School of Electrical Engineering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eNanning, Peoples R China\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKopcansky, Peter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e43\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMagnetic Nanoparticles, Liquid Crystals \u0026amp; Biomedicine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInstitute of Experimental Physics, Slovak Academy of Sciences, Kosice Slovakia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSlovakia\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eZahn,Markus\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNanotechnology, Transformer \u0026amp; High Voltage Engineering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMassachusetts Institute of Technology (MIT) School of Engineering\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eUSA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCimbala, Roman\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eEngineering, Physics, Chemistry \u0026amp; Material Science\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eTechnical University of Kosice\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSlovakia\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRajnak, Michal\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e36\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePhysics, Colloids, Magnetism, Ferrofluids \u0026amp; Dielectric Liquids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInstitute of Experimental Physics SAS, Kosice Slovakia\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eSlovakia\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eOut of 2,801 publications in the WOSCC database between 1970 and 2024, Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e highlights the top ten journals based on the number of publications on transformer oil research. The leading journal, IEEE Transactions on Dielectric and Electrical Insulation, accounts for 15.39% of the total publications, making it the most significant contributor in this field. Following this, the Energies journal and IET Science Measurement \u0026amp; Technology rank second and third, contributing 3.00% and 2.04% of the total publications, respectively.\u003c/p\u003e\u003cp\u003eIt is evident from Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e that research on transformer oil insulating topics spans a wide range of journals, including both high-ranked Q1 journals and lower-ranked Q3/Q4 journals. Among the Q1 journals, Journal of Molecular Liquids boasts the highest Journal Impact Factor (JIF) in 2023 at 4.7, reflecting its strong influence in the scientific community. Other notable Q1 journals include IEEE Transactions on Power Delivery (JIF\u0026thinsp;=\u0026thinsp;3.3) and IEEE Access (JIF\u0026thinsp;=\u0026thinsp;3.0).\u003c/p\u003e\u003cp\u003eIn the Q3 category, the Energies journal has a JIF of 2.4, highlighting its growing relevance in energy-related research. Additionally, IET Science Measurement \u0026amp; Technology (JIF\u0026thinsp;=\u0026thinsp;1.4, Q3-Q4) and Journal of Electrostatics (JIF\u0026thinsp;=\u0026thinsp;1.7, Q3) demonstrate how impactful transformer oil research can extend across different quartiles.\u003c/p\u003e\u003cp\u003eThe citation trends for insulating transformer oil research have shown a steady increase over the years, reflecting the growing academic and industrial interest in this domain. This upward trajectory underscores the importance of transformer oil research in advancing energy efficiency, sustainability, and reliability, particularly in light of global efforts to enhance electrical insulation technologies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eNumber of publications from the top ten journals published between 1970 and 2024\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"8\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePublication Titles\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003e5 Year Impact Factor\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQuartile JIF\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eJIF 2023\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQuartile JCI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eJCI 2023\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eRecord Count\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003e% Out of 2801\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJournal Of Physics D Applied Physics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJournal of Molecular Liquids\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e5.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ1 \u0026ndash; Q2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e4.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.20\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e38\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.357\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJournal of Electrostatics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.47\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e32\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.142\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIET Science Measurement \u0026amp; Technology\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.31\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.035\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIET Generation Transmission \u0026amp; Distribution\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ2 \u0026ndash; Q3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.714\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIEEE Transactions on Power Delivery\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ1 \u0026ndash; Q2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ1 \u0026ndash; Q2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e1.07\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e34\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.214\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e*IEEE Transactions on Electrical Insulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ2 \u0026ndash; Q3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.571\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIEEE Transactions on Dielectric and Electrical Insulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ2 \u0026ndash; Q3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e431\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e15.387\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eIEEE Access\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ1 \u0026ndash; Q2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ1 \u0026ndash; Q2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.87\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.856\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEnergies\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eQ3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e0.46\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e84\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.999\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"8\"\u003e*JIF\u0026thinsp;=\u0026thinsp;Journal Impact Factor, JCI\u0026thinsp;=\u0026thinsp;Journal Citation Indicator\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eScientometric\u003c/h2\u003e\u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\u003ch2\u003eDual Map Overlay\u003c/h2\u003e\u003cp\u003eThe dual-map overlay visualization effectively illustrates the interdisciplinary dynamics of insulating oil transformer research, mapping the relationships between citing and cited journals. As visualized in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, journals citing others are displayed on the left, while those being cited are positioned on the right. Citation links bridge these two sides, revealing the sources referenced by the citing journals. By examining the trajectories of these links, interdisciplinary influences can be discerned; for instance, a shift in trajectory indicates that research in one field has been significantly influenced by studies from another domain.\u003c/p\u003e\u003cp\u003eThe citing map (left) predominantly represents journals focused on applied disciplines like electrical engineering, material science, and applied physics, underscoring the technical objectives of transformer oil research\u0026mdash;primarily improving electrical and thermal properties. Meanwhile, the cited map (right) highlights foundational fields such as molecular physics, chemistry, and computational science, which contribute essential theoretical insights into chemical interactions, molecular behavior, and synthesis techniques for insulating fluids and nanomaterials.\u003c/p\u003e\u003cp\u003eThe directional lines linking the two maps underscore a symbiotic relationship between theoretical research and practical applications, showcasing how advancements in transformer oil technology rely heavily on interdisciplinary collaboration. The visualization emphasizes the convergence of engineering practices with foundational sciences, reinforcing the need for continued interdisciplinary efforts to innovate in transformer insulation systems, particularly with the incorporation of nano-enhanced oils. This comprehensive depiction affirms the vital role of cross-field knowledge integration in advancing insulating oil technology.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\n\u003ch3\u003eDocument Co-citation Analysis\u003c/h3\u003e\n\u003cp\u003eDocument Co-citation Analysis (DCA) explores the frequency with which various documents are co-cited by later studies, providing insights into the interconnectedness and influence within a specific field. For transformer oil research, this analysis uses data from the Web of Science (WoS) database, revealing a complex network comprising 1,725 nodes and 3,508 links. This setup indicates a broad and interconnected field, characterized by frequent references across diverse studies.\u003c/p\u003e\u003cp\u003eThe network analysis reveals a high silhouette score of 0.0.9575, suggesting substantial thematic commonality among the articles within each cluster. This score highlights the cohesion of topics, where research themes are not only related but also tightly integrated, with consistent citation patterns across various studies. Additionally, a harmonic mean of 0.9521 points to the network's internal coherence, signifying a balanced spread of citations among the papers. Furthermore, the low network density score of 0.0024 indicates a specialized research network. This low density suggests that direct citation connections between all papers are uncommon, reflecting the specialized and diverse nature of insulating transformer oil research.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e provides data on top highly influential articles related to insulating transformer oil research, focusing on three metrics: Degree, Centrality, and Sigma. The Degree metric indicates how many other articles directly reference or are referenced by these studies. Figure\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e show the list of authors with a centrality score greater than or equal to 0.1, with 4 total important authors categorized as authors for co-citation analysis. The top three most central authors based on centrality score were Bakar et al., Liao et al., and Okabe et al., with centrality scores of 0.04, 0.05 and 0.04 respectively. However, based on the Sigma score, the top author is only Bakar et al., from Curtin University, Western Australia with an exceptional sigma score to 1.6 showing that he is the most influential author in transformer oil research in the world.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTop highly influential articles\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eArticle\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDegree\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCentrality\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSigma\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Bakar et al., 2014)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.61\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Liao et al., 2011)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e(Okabe et al., 2013)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.04\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1.32\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eBakar et al. (2014): Their paper provides a comprehensive review of Dissolved Gas Analysis (DGA), emphasizing its importance in diagnosing faults in transformers. The article's high centrality score (0.04) and outstanding Sigma score (1.61) reflect its foundational contribution, as DGA is critical for transformer maintenance. Their integration of AI and modern statistical tools further showcases their role in advancing transformer diagnostics, making them a key reference in this domain.\u003c/p\u003e\u003cp\u003eLiao et al. (2011): This article compares natural ester and mineral oil-based transformer insulation. It highlights natural ester's environmental advantages but notes areas for improvement in dielectric properties. With a centrality score of 0.05, it demonstrates the significant impact on understanding the potential of environmentally friendly transformer oils. This aligns with the growing interest in sustainable alternatives within transformer oil research.\u003c/p\u003e\u003cp\u003eOkabe et al. (2013): Their work on aging degradation in oil-immersed transformers provides valuable insights into the lifespan and reliability of transformer insulation. The centrality score of 0.04 highlights their contribution to real-world applications, particularly in understanding how operational data can inform better maintenance and diagnostics.\u003c/p\u003e\u003cp\u003eThe high influence of these authors, as evidenced by the metrics (Degree, Centrality, and Sigma), directly correlates with their innovative contributions to transformer oil research. Their studies have shaped both the theoretical and practical understanding of insulating materials and diagnostics, making them indispensable references in the field.\u003c/p\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003eCluster Analysis\u003c/h2\u003e\u003cp\u003eThe Document Co-citation Analysis (DCA) of transformer oil research has identified 12 distinct co-citation clusters, ranked by their sizes, with the largest cluster designated as #0. In this analysis, the size of the circles within each cluster represents the influence of the publications, with larger circles indicating higher citation rates. This effectively highlights the key papers that have significantly impacted their fields. Additionally, the lines extending from each cluster illustrate the duration or \"lifetime\" of the cluster, providing insights into the development and evolution of research themes over time.\u003c/p\u003e\u003cp\u003eThe size of each cluster is directly proportional to the number of publications it contains, with six out of the twelve clusters including more than fifty publications each. Figure\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e illustrates the top 12 clusters, displayed horizontally with cluster labels positioned on the right. Additionally, Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e depicts the network of all the articles. The clusters are numbered and ranked by size, beginning with #0 as the largest cluster and #86 as the smallest. The size of each circle represents the magnitude of a publication's influence, where larger circles correspond to greater impact. This highlights significant engagement within these thematic areas, with Cluster #0 emerging as the most prominent based on the number of publications.\u003c/p\u003e\u003cp\u003eThe red rings surrounding the circles indicate the \"bursts\" of articles, revealing when bursts occurred and their relative strength. Furthermore, the silhouette scores for these clusters range from 0.919 to 0.998, reflecting a high level of homogeneity within each cluster's publications. Silhouette scores measure the consistency of objects within their own cluster compared to other clusters, with values above 0.900 demonstrating strong internal coherence.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe clusters were named using four different methods: (i) Latent Semantic Indexing (LSI), (ii) Term Frequency\u0026ndash;Inverted Document Frequency (TF-IDF), (iii) log-likelihood ratio (LLR), and (iv) Mutual Information (MI). The Log-Likelihood Ratio (LLR) is generally considered superior to Mutual Information (MI) and Latent Semantic Indexing (LSI) due to its higher discriminative power and statistical robustness. LLR identifies terms that are uniquely and significantly associated with a specific cluster, enabling more precise and representative labeling [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Unlike MI, which often highlights frequently occurring terms that may appear across multiple clusters\u0026mdash;resulting in vague or generic labels\u0026mdash;LLR emphasizes the distinctiveness of terms, thereby enhancing the clarity and specificity of each cluster's thematic identity. Similarly, while LSI is valuable for uncovering latent semantic structures through dimensionality reduction techniques, it tends to produce abstract or less interpretable terms that are not optimal for direct cluster labeling. LLR, by contrast, utilizes a statistical framework that evaluates the likelihood of a term\u0026rsquo;s association with a cluster beyond random chance, making it particularly effective for generating meaningful and contextually grounded descriptors [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. As such, LLR is preferred in scientometric studies that prioritize clarity, specificity, and analytical rigor in the interpretation of knowledge domains. Following the approach outlined in [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e],this paper focuses on the clusters identified using the log-likelihood ratio (LLR), as the outputs of the other methods occasionally produced less coherent results. Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e provides a comprehensive overview of research clusters, characterized by Cluster ID, Size, Silhouette score, Label (LSI), Label (LLR), Label (MI) and Average Year. The analysis identified the most influential clusters, which are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e. The clusters range in size from 4 to 176 articles, with silhouette scores indicating the cohesion within clusters, where a score of 1 denotes perfect cohesion. The clusters represent diverse research themes, with Cluster 0 (Dielectric Strength) being the largest (176 publications, average year 2017), highlighting its longstanding importance in transformer insulation studies. Cluster 1 (DFT Study), while smaller (101 publications), focuses on emerging computational methods like Density Functional Theory (average year 2020) with a high silhouette score (0.970), indicating strong thematic coherence. Notable older themes include Cluster 7 (Copper Sulfide Deposition) with an average year of 2008 and Cluster 18 (Hydrothermal Synthesis) from 2009, which emphasize historical research trends. Conversely, newer themes like Cluster 8 (Health Index) and Cluster 86 (Alternative Liquid Dielectric) reflect contemporary innovations in transformer diagnostics and sustainable materials, both with an average year of 2020. This distribution highlights the field's evolution, balancing foundational studies with modern advancements.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eThe 12 major clusters that emerged from Document Co-citation Analysis. Size represents the number of publications in a cluster, and silhouette score indicates levels of homogeneity. Labels were derived from log likelihood ratios (LLR)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"7\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCluster ID\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eSize\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eSilhouette\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLabel (LSI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eLabel (LLR)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eLabel (MI)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eAverage Year\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e176\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.930\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOxidation stability\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDielectric Strength\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eInsulating oil-based nanofluid (4.22)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e101\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.970\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDFT method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eDFT Study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eSensing substrate (0.52)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.976\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eOver-sampling technique\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePower Transformer\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eWavelength modulation (1.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.994\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eDispersion behavior\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eRecent Progress\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTurbulent electroconvection (0.35)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2011\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.955\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eThermal aging\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eMixed Dielectric Fluid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eAlternative insulating liquid (0.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2013\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.919\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eMultilayer sensor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eVarious Characteristics\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMultilayer sensor (0.12)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2010\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSuppressive mechanism\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eCopper Sulfide Deposition\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTransformer oil (0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2008\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e37\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.951\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eConcentration prediction\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHealth Index\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eMultilayer sensor (0.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e11\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e28\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.943\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eSmart life prediction approach\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePrecise Measurement\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eDiffusion mechanism (0.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2018\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e12\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.995\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eElectro-insulating fluid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eElectro-Insulating Fluid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTransformer oil (0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e18\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e19\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.986\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eStructural characterization\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eHydrothermal Synthesis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTransformer oil (0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2009\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e86\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.998\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eVisual domain\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003eAlternative Liquid Dielectric\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eTransformer oil (0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eThe most cited papers in the field shows in Table\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e. The citation statistics were collected from WoS database. The journal is a mixed source from mainstream business economics journals through field journals. The top three articles by [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] have been very heavily cited, with more than 20 citations. The results indicated that [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] received the highest citation count which is published in the most recognized journal, \u003cem\u003eApplied Surface Science\u003c/em\u003e. The study titled \u0026ldquo;Pd-doped MoS₂ monolayer: A promising candidate for DGA in transformer oil based on DFT method\u0026rdquo; stands out as the most cited work among the top 10 publications in Cluster #1. Despite Cluster #1 having fewer publications than Cluster #0, it focuses on cutting-edge methods like DFT (Density Functional Theory), which has significant practical relevance. This emphasis on innovative approaches has made studies in this cluster highly influential in both academic and industrial circles.\u003c/p\u003e\u003cp\u003eArticle citation is an indicator that shows the impact of a study in its research field. Based on previous study, the direction of one research field is associated with its frequently cited articles [\u003cspan additionalcitationids=\"CR38\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. H. Cui, X. Zhang, G. Zhang and J. Tang [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] study uses Density Functional Theory (DFT) to explore Pd-doped MoS\u003csub\u003e2\u003c/sub\u003e monolayers as advanced materials for dissolved gas analysis (DGA). Its high citation count likely stems from its innovative approach to diagnosing transformer oil degradation, combining computational methods with practical implications in improving transformer reliability. The focus on nanomaterials for DGA highlights the study's relevance to emerging trends in diagnostics for energy systems. H. Cui, D. Chen, Y. Zhang and X. Zhang [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] similar to the first study, this research emphasizes DFT to evaluate Pd-decorated MoSe\u003csub\u003e2\u003c/sub\u003e monolayers for gas sensing applications in transformer oil. Its high impact is due to its alignment with the ongoing interest in nano-engineered solutions for precise and efficient fault detection in transformers. The theoretical insights into gas adsorption mechanisms contribute to both material science and transformer diagnostics. Z. Shen, F. Wang, Z. Wang and J. Li [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] This comprehensive review of plant-based insulating fluids reflects a growing interest in sustainable alternatives to mineral oils. The high citation rate is likely due to its coverage of technological advancements, environmental benefits, and challenges in implementing plant-based fluids. The article's relevance is amplified by the global push towards greener technologies in the energy sector.\u003c/p\u003e\u003cp\u003eIn summary, the high citation counts of these articles are due to their cutting-edge focus on nanotechnology for transformer diagnostics [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and sustainable insulating fluids [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Their combination of theoretical rigor, practical applications, and alignment with current research trends makes them highly influential in the transformer oil research domain.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTop 10 highest-cited articles in the transformer oil research theme\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"6\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTitle of the article\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAuthors\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eCluster ID (explanation)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePublication year\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003eTotal citation\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eReference\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePd-doped MoS\u003csub\u003e2\u003c/sub\u003e monolayer: A promising candidate for DGA in transformer oil based on DFT method\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH. Cui, X. Zhang, G. Zhang and J. Tang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (novel method and material)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDissolved gas analysis in transformer oil using Pd catalyst decorated MoSe\u003csub\u003e2\u003c/sub\u003e monolayer: A first-principles theory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH. Cui, D. Chen, Y. Zhang and X. Zhang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (novel method and material)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA critical review of plant-based insulating fluids for transformer: 30-year development\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eZ. Shen, F. Wang, Z. Wang and J. Li\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (insulation properties)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e35\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA DFT study of dissolved gas (C\u003csub\u003e2\u003c/sub\u003eH\u003csub\u003e2\u003c/sub\u003e, H\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e) detection in oil on CuO-modified BNNT\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eX. He, Y. Gui, J. Xie, X. Liu, Q. Wang and C. Tang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (novel method and material)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e33\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDispersion Behavior and Breakdown Strength of Transformer Oil Filled with TiO\u003csub\u003e2\u003c/sub\u003e Nanoparticles\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eE. Atiya, D.-E. Mansour, R. Khattab and A. Azmy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (insulation properties)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e29\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eA review of dissolved gas analysis measurement and interpretation techniques\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eN. A. Bakar, A. Abu-Siada and S. Islam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (conventional transformer oil)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e27\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdsorption and sensing of CO and C\u003csub\u003e2\u003c/sub\u003eH\u003csub\u003e2\u003c/sub\u003e by S-defected SnS\u003csub\u003e2\u003c/sub\u003e monolayer for DGA in transformer oil: A DFT study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eH. Cui, P. Jia, X. Peng and P. Li\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1 (novel method and material)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEffects of conductivity and permittivity of nanoparticle on transformer oil insulation performance: experiment and theory\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eW. Sima, J. Shi, Q. Yang, S. Huang and X. Cao\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e0 (insulation properties)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDissolved gas analysis evaluation in electric power transformers using conventional methods a review\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eJ. Faiz and M. Soleimani\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e2 (conventional transformer oil)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e26\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEffect of semiconductive nanoparticles on insulating performances of transformer oil\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eY. Du, Y. Lv, C. Li, M. Chen, Y. Zhong, J. Zhou, et al.\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e3 (older articles)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab8\" class=\"InternalRef\"\u003e8\u003c/span\u003e provides a comprehensive overview of most cited article in the top 3 research clusters. Cluster #0 is the largest cluster in this analysis, comprising 176 articles with a silhouette value of 0.930, indicating clear differentiation from other clusters. Labelled as \u0026ldquo;dielectric strength\u0026rdquo; through the Log-Likelihood Ratio (LLR) method, this cluster emphasizes improving transformer insulation performance, focusing on enhancing the dielectric properties of insulating fluids. In this cluster, [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] were identified as the most influential publications. It includes studies on plant-based insulating fluids, nanoparticle-enhanced transformer oils, and alternative dielectric fluids, reflecting a broad spectrum of approaches to address both technical and environmental challenges.\u003c/p\u003e\u003cp\u003eKey articles in this cluster, such as [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] review of plant-based insulating fluids and [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] work on TiO\u003csub\u003e2\u003c/sub\u003e nanoparticle-enhanced transformer oil, illustrate the diversity and relevance of research. These studies influence academia and industry by highlighting sustainable solutions, advancing material science, and addressing performance optimization in transformer insulation systems. The focus on bridging experimental results, theoretical modeling, and practical applications makes this cluster pivotal in shaping the future of transformer technology.\u003c/p\u003e\u003cp\u003eCluster #1 is the second-largest cluster, comprising 101 articles with a high silhouette value of 0.970, labeled as \u0026ldquo;DFT study.\u0026rdquo; This cluster gained prominence through the application of Density Functional Theory (DFT) in addressing challenges related to transformer oil diagnostics. The most influential publications, such as [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e] focus on leveraging advanced nanomaterials as sensors for dissolved gas analysis (DGA). These studies introduced novel methods for detecting critical fault gases, including C\u003csub\u003e2\u003c/sub\u003eH\u003csub\u003e2\u003c/sub\u003e, H\u003csub\u003e2\u003c/sub\u003e, and CO, by utilizing DFT to predict material behavior at the atomic scale. In particular, publications [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] emphasize the catalytic and adsorption properties of Pd-doped materials, showcasing their potential for enhanced gas detection in transformer diagnostics. These articles collectively demonstrate innovative approaches involving nanomaterial design, adsorption behavior, and catalytic properties. They align in their methodology (DFT) and objectives (improving transformer oil fault detection), forming a cohesive narrative within the cluster and highlighting their collective impact on advancing transformer diagnostics.\u003c/p\u003e\u003cp\u003eCluster #2 is the third-largest cluster, consisting of 70 articles with a silhouette value of 0.976, indicating clear differences from other clusters in the network. It is labeled as \u0026ldquo;Power Transformer\u0026rdquo; by the Log-Likelihood Ratio (LLR) test method. This cluster emphasizes advancements in dissolved gas analysis (DGA), focusing on fault diagnosis and condition monitoring in power transformers. The most influential articles in this cluster include [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e], which highlight the importance of innovative diagnostic approaches.\u003c/p\u003e\u003cp\u003eResearch within this cluster discusses the evolution of DGA techniques, starting from traditional measurement and interpretation methods to cutting-edge applications like advanced sensors and artificial intelligence. Articles [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] provide comprehensive reviews of DGA methods, stressing the importance of real-time monitoring and precision. Meanwhile, [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] explore novel approaches such as Pd-decorated ZnO nanorod sensors and genetic algorithms for optimizing diagnostic accuracy. Collectively, this cluster underscores the need for continuous innovation to enhance transformer reliability, extend service life, and reduce downtime, making DGA a cornerstone of transformer maintenance strategies.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab8\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 8\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMost cited article of the top 3 cluster that emerged from Document Co-citation Analysis\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eCluster\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eTitle (Journal Published)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e\u003cp\u003eMost cited article\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003eRef\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAuthor\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePublication Year\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eCluster #0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA critical review of plant-based insulating fluids for transformer: 30-year development (\u003cem\u003eRenewable and Sustainable Energy Reviews\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eZ. Shen, F. Wang, Z. Wang and J. Li\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDispersion Behavior and Breakdown Strength of Transformer Oil Filled with TiO\u003csub\u003e2\u003c/sub\u003e Nanoparticles (\u003cem\u003eIEEE Transactions on Dielectrics and Electrical Insulation\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eE. Atiya, D.-E. Mansour, R. Khattab and A. Azmy\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEffects of conductivity and permittivity of nanoparticle on transformer oil insulation performance: experiment and theory (\u003cem\u003eIEEE Transactions on Dielectrics and Electrical Insulation\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eW. Sima, J. Shi, Q. Yang, S. Huang and X. Cao\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAlternative Dielectric Fluids for Transformer Insulation System: Progress, Challenges, and Future Prospects (\u003cem\u003eIEEE Access\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eU. M. Rao, I. Fofana, T. Jaya, E. M. Rodriguez-Celis, J. Jalbert, and P. Picher\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEffect of nanoparticles on transformer oil breakdown strength: experiment and theory (\u003cem\u003eIET Science, Measurement \u0026amp; Technology\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eM. E. Ibrahim, A. M. Abd-Elhady, and M. A. Izzularab\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003eCluster #1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003ePd-doped MoS\u003csub\u003e2\u003c/sub\u003e monolayer: A promising candidate for DGA in transformer oil based on DFT method (\u003cem\u003eApplied Surface Science\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eH. Cui, X. Zhang, G. Zhang and J. Tang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDissolved gas analysis in transformer oil using Pd catalyst decorated MoSe\u003csub\u003e2\u003c/sub\u003e monolayer: A first-principles theory (\u003cem\u003eSustainable Materials and Technologies\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eH. Cui, D. Chen, Y. Zhang and X. Zhang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eA DFT study of dissolved gas (C\u003csub\u003e2\u003c/sub\u003eH\u003csub\u003e2\u003c/sub\u003e, H\u003csub\u003e2\u003c/sub\u003e, CH\u003csub\u003e4\u003c/sub\u003e) detection in oil on CuO-modified BNNT (\u003cem\u003eApplied Surface Science\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eX. He, Y. Gui, J. Xie, X. Liu, Q. Wang and C. Tang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdsorption and sensing of CO and C\u003csub\u003e2\u003c/sub\u003eH\u003csub\u003e2\u003c/sub\u003e by S-defected SnS\u003csub\u003e2\u003c/sub\u003e monolayer for DGA in transformer oil: A DFT study (\u003cem\u003eApplied Surface Science\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eH. Cui, P. Jia, X. Peng and P. Li\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2020\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eAdsorption of SO\u003csub\u003e2\u003c/sub\u003e and NO\u003csub\u003e2\u003c/sub\u003e molecule on intrinsic and Pd-doped HfSe\u003csub\u003e2\u003c/sub\u003e monolayer: A first-principles study (\u003cem\u003eApplied Surface Science\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eH. Cui, P. Jia, and X. 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Soleimani\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDissolved Gas Analysis Equipment for Online Monitoring of Transformer Oil: A Review (\u003cem\u003eSensors\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eS. Bustamante, M. Manana, A. Arroyo, P. Castro, A. Laso, and R. Martinez\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDissolved hydrogen gas analysis in transformer oil using Pd catalyst decorated on ZnO nanorod array (\u003cem\u003eSensors and Actuators B: Chemical\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA. S. M. I. Uddin, U. Yaqoob, and G.-S. Chung\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOptimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine (\u003cem\u003eIEEE Transactions on Dielectrics and Electrical Insulation\u003c/em\u003e)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eJ. Li, Q. Zhang, K. Wang, J. Wang, T. Zhou, and Y. Zhang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA burst detection analysis was performed to identify the most influential or landmark publication in the field that had drawn researcher\u0026rsquo;s attention. The top 10 major bursts in citations, with the duration shows in Table\u0026nbsp;\u003cspan refid=\"Tab9\" class=\"InternalRef\"\u003e9\u003c/span\u003e. The top 3 articles with the highest burst are (N. A. Bakar, A. Abu-Siada and S. Islam, 2014; E. Atiya, D.-E. Mansour, R. Khattab, and A. Azmy, 2015; W. Sima, J. Shi, Q. Yang, S. Huang, and X. Cao, 2015). (N. A. Bakar, A. Abu-Siada and S. Islam, 2014) [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], explores the use of Dissolved Gas Analysis (DGA) for diagnosing faults in transformers by monitoring gas concentrations in insulating oil. This method is recognized for its reliability and effectiveness in detecting incipient faults, making it widely influential in both research and practical applications.\u003c/p\u003e\u003cp\u003e(E. Atiya, D.-E. Mansour, R. Khattab, and A. Azmy, 2015) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] investigates the enhancement of transformer oil insulation properties using TiO\u003csub\u003e2\u003c/sub\u003e nanoparticles. It emphasizes improvements in breakdown strength and dielectric performance, making it a significant study in the adoption of nano-dielectric fluids for modern transformers. The third articles with high burstiness is (W. Sima, J. Shi, Q. Yang, S. Huang, and X. Cao, 2015) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] provides both experimental and theoretical insights into how nanoparticle characteristics influence insulation performance. 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Islam\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2014\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eA review of dissolved gas analysis measurement and interpretation techniques\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIEEE Electrical Insulation Magazine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e11.93\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2015\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eE. Atiya, D.-E. 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Zhang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2016\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eOptimal dissolved gas ratios selected by genetic algorithm for power transformer fault diagnosis based on support vector machine\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eIEEE Transactions on Dielectrics and Electrical Insulation\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.44\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2021\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eH. Cui, G. Zhang, X. Zhang, and J. Tang\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRh-doped MoSe\u003csub\u003e2\u003c/sub\u003e as a toxic gas scavenger: a first-principles study\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNanoscale Advances\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e5.16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2019\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2024\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eJ.-C. Lee, H.-S. Seo, and Y.-J. Kim\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e2012\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eThe increased dielectric breakdown voltage of transformer oil-based nanofluids by an external magnetic field\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eInternational Journal of Thermal Sciences\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e4.72\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e\u003cp\u003e2013\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e\u003cp\u003e2017\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis paper conducted a comprehensive scientometric analysis of insulating transformer oil research to explore its evolution, thematic focus, and influential contributions over the period 1970\u0026ndash;2024. By examining publication trends, research clusters, and key works, this study provides an in-depth understanding of the field's development and its intersections with various scientific disciplines.\u003c/p\u003e\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e(i) Overall Publication Trends in Terms of Output\u003c/h2\u003e\u003cp\u003eThe publication trends demonstrate a significant and steady growth in research output, signaling increasing global interest in insulating transformer oils, particularly over the last two decades. The environmental concerns surrounding traditional mineral oils, such as their non-biodegradability and fire hazards, have further propelled interest in bio-based insulating fluids The biodegradability of insulating liquids affects how long they remain in the environment before breaking down. Mineral oils typically take between 1 and 10 years to decompose, whereas vegetable-based oils like canola oil degrade much faster, typically within 4 to 48 weeks, demonstrating their lower environmental persistence [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Biodegradable natural esters are capable of meeting the performance criteria required for insulating oils in power equipment applications [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. This surge is closely tied to advancements in nanotechnology, which have enabled the development of nano-enhanced transformer oils with superior electrical and thermal properties, as well as the push for more sustainable and high-performance insulation solutions. A review paper [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] reports that most studies observe an enhancement in the flash point of mineral oil when nanoparticles are incorporated. Studies from [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e], investigated the impact of nanocomposites and single nanoparticles on key thermal parameters such as the convective heat transfer coefficient (CHTC), Nusselt number (Nu), Grashof number (Gr), and Rayleigh number (Ra). Their findings revealed that titanium dioxide (TiO\u003csub\u003e2\u003c/sub\u003e​) nanoparticles had a more pronounced effect on enhancing both CHTC and Nu compared to (ZnFe\u003csub\u003e2\u003c/sub\u003e​O\u003csub\u003e4\u003c/sub\u003e​) nanoparticles, highlighting the superior thermal performance of TiO\u003csub\u003e2\u003c/sub\u003e​​-doped nanocomposites in transformer oil applications [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Palm-based transformer oil is recognized for its enhanced fire safety properties, exhibiting a flash point above 280\u0026deg;C and a fire point exceeding 300\u0026deg;C, both of which are considerably higher than those of mineral oil [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. This improvement suggests that nanoparticle additives can increase the thermal stability and safety margins of transformer oil-based insulating fluids. Between 2015 and 2024, several research landscapes shifted toward exploring bio-based and nano-enhanced insulating oils, a trend supported by the urgent need for environmentally friendly and efficient alternatives to mineral oil in transformers. This shift is reflected in studies like those by Zakaria et al., who investigated palm-based oils as biodegradable alternatives for power transformers in Malaysia [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, research by Peppas et al. highlighted the improved performance of nanofluids, such as those incorporating nanoparticles, when compared to traditional oils [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Moreover, Mohd et al. explored the potential applications of palm oil products as electrical insulating mediums, further supporting the move towards sustainable alternatives in transformer technology [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe IEEE Transactions on Dielectric and Electrical Insulation stands out as the leading journal in this field, contributing 15.39% of total publications. Its dominance is attributed to its niche focus on dielectric materials, its global visibility as part of the IEEE ecosystem, and its role as a trusted platform for publishing cutting-edge research in transformer insulation technology. The journal also bridges practical engineering solutions with theoretical advancements, making it a preferred venue for academics and industry practitioners alike.\u003c/p\u003e\u003cp\u003eGeographically, India and China emerge as prominent contributors to research on insulating transformer oils, driven by their substantial investments in energy infrastructure and policies that foster innovation. In India, government-backed institutions such as the Indian Institutes of Technology (IITs) and the Indian Institute of Science (IISc) play a pivotal role in advancing this field. Studies such as the investigation into thermal properties of graphene-silicone oil nanofluids [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] and the enhancement of ester-based insulating oils with nanocomposites [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] exemplify India\u0026rsquo;s commitment to applied research in this domain. These works are often carried out in collaboration with industry leaders like the Power Grid Corporation of India, underlining the importance of academic-industry partnerships in addressing national energy challenges [\u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSimilarly, China has emerged as a prominent leader in transformer insulation research, supported by substantial government funding and policies aimed at advancing technological innovations in the energy sector. Research contributions include innovative approaches to modifying insulating oils for enhanced electrical and thermal performance, such as the development of KH550-TiO\u003csub\u003e2\u003c/sub\u003e-enhanced natural ester oils [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e] and investigations into nano TiO\u003csub\u003e2\u003c/sub\u003e-modified transformer oils for extreme cold conditions [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. These efforts reflect China\u0026rsquo;s emphasis on leveraging nanotechnology to create sustainable and efficient alternatives to traditional mineral oils, supported by policies encouraging academia-industry collaboration [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan additionalcitationids=\"CR67\" citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe prioritization of transformer insulation research by both countries highlights their strategic focus on modernizing power systems to meet rising energy demands while addressing environmental sustainability. Additionally, these countries lead global efforts in nanotechnology research, which has direct applications in insulating fluids, further reinforcing their dominance in this area.\u003c/p\u003e\u003cp\u003eThe multidisciplinary nature of this research field is underscored by its wide-reaching impact across journal quartiles, ranging from Q1 to Q3, reflecting the integration of diverse fields such as electrical engineering, material science, molecular physics, and environmental science. This underscores the need for cross-disciplinary collaboration to address the challenges of modern transformer insulation systems, particularly with the incorporation of nano-enhanced and bio-based insulating fluids. Such efforts are essential for meeting both performance and sustainability goals in the energy sector.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e(ii) Central Topics/Clusters\u003c/h2\u003e\u003cp\u003eIn this scientometric review, Cluster 1, categorized as \u0026ldquo;Density Functional Theory (DFT) Study,\u0026rdquo; has emerged as a dominant and influential research area. The primary reason for its prominence lies in the ability of DFT methods to provide molecular-level insights into gas adsorption and sensing mechanisms. These studies focus on gases like C₂H₂, H₂, CH₄, SO₂, and NO₂, which are key indicators for Dissolved Gas Analysis (DGA)\u0026mdash;a critical diagnostic technique for transformer health monitoring [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. By using computational approaches, these studies uncover detailed adsorption behaviors, providing predictive and cost-effective solutions compared to experimental methods.\u003c/p\u003e\u003cp\u003eThe articles within Cluster 1 highlight the development of advanced nanomaterials to enhance gas detection sensitivity. For example, Pd-doped MoS₂ and Pd-doped HfSe₂ demonstrate the role of palladium doping in improving adsorption selectivity for fault gases like C₂H₂ and H₂ [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Meanwhile, modifications to monolayers such as MoSe₂ and SnS₂ with sulfur defects explore strategies for selective detection of gases [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Expanding beyond monolayers, CuO-modified boron nitride nanotubes (BNNTs) showcase their potential due to their high surface area and strong adsorption capability [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. These innovations provide transformative improvements for DGA, offering practical solutions to enhance transformer fault diagnosis.\u003c/p\u003e\u003cp\u003eThe practical impact of Cluster 1 is significant because transformer failures caused by undetected faults can lead to severe economic and operational consequences. DFT-based studies contribute to improving the sensitivity, reliability, and accuracy of gas detection methods, bridging the gap between theoretical research and real-world applications. By advancing materials like Pd-doped MoS₂ and CuO-BNNTs, these studies propose efficient, next-generation diagnostic systems that support better transformer maintenance and grid reliability [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAnother reason for the dominance of this cluster is its recent publication trend, with an average year of 2020. These recent studies reflect ongoing advancements in transformer oil diagnostics and computational materials research. By incorporating cutting-edge technologies and novel materials, these articles are more likely to attract attention and citations from the research community, maintaining their high influence [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. The alignment of these studies with current technological needs highlights their relevance in the energy sector.\u003c/p\u003e\u003cp\u003eMoreover, the interdisciplinary nature of DFT studies enhances their appeal to a broader audience. These studies connect key disciplines such as materials science, computational chemistry, and electrical engineering, which increases their visibility and applicability. Compared to older, more specialized clusters like Cluster 3 (average year: 2011), DFT studies remain at the forefront of transformer oil research by pushing the boundaries of innovation.\u003c/p\u003e\u003cp\u003eLast but not least, Cluster 1 stands out due to its methodological importance, practical relevance, and alignment with current research trends. The top-cited articles demonstrate the potential of DFT-based studies to revolutionize transformer fault diagnostics through advanced material design and computational precision. By addressing key challenges in DGA, these studies contribute to both academic advancements and industry applications, solidifying their influence in transformer insulation research [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe dominant research themes in transformer oil studies include dielectric strength (Cluster 0), alongside emerging trends such as Density Functional Theory (DFT) studies and power transformer advancements. Through document cluster analysis, Cluster 0 emerged as a major focus due to its relevance to improving insulation performance and overall reliability in power systems. The studies within this cluster focus on traditional but critical aspects, including the breakdown strength of transformer oils, the integration of nanotechnology, and the development of sustainable insulating fluids.\u003c/p\u003e\u003cp\u003eThe analysis identified five highly cited articles [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e] that contribute significantly to this research direction. These publications are categorized into four primary areas. First, dielectric strength studies [\u003cspan additionalcitationids=\"CR70\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e] focus on enhancing the breakdown voltage of insulating oils, which remains central to improving the efficiency and safety of transformers. Dielectric strength is a fundamental property for insulating fluids, as it directly determines their ability to withstand high voltages without failure.\u003c/p\u003e\u003cp\u003eSecond, the innovation of nanofluids has emerged as a promising trend. Studies such as [\u003cspan additionalcitationids=\"CR73\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e] explore the role of nanoparticles\u0026mdash;including TiO₂, ZnO, and hybrid nanomaterials\u0026mdash;in enhancing the electrical and thermal performance of transformer oils. By dispersing nanoparticles in traditional oils, researchers have achieved notable improvements in breakdown voltage, thermal conductivity, and dissipation factor, paving the way for nano-enhanced insulating fluids. These advancements address the limitations of conventional oils while improving transformer efficiency and performance.\u003c/p\u003e\u003cp\u003eThe third key focus within Cluster 0 is the shift toward natural esters and bio-based fluids as sustainable alternatives to mineral oil. Articles like [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e] align with global sustainability efforts by investigating the use of plant-based and biodegradable insulating fluids. These studies highlight the environmental benefits of natural esters, such as reduced carbon footprint, biodegradability, and safety, while demonstrating comparable or superior dielectric properties to mineral oils.\u003c/p\u003e\u003cp\u003eFinally, comprehensive review studies [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] play a critical role in this cluster. These highly cited reviews consolidate experimental findings, theoretical advancements, and emerging trends in transformer oil research. By synthesizing data across multiple studies, they provide clear insights into existing challenges and highlight future research directions, ensuring their continued relevance and impact.\u003c/p\u003e\u003cp\u003eIn summary, Cluster 0 reflects a balance between traditional research themes and emerging innovations. The interconnected focus on dielectric strength, nanofluid enhancements, and sustainable insulating fluids highlights a comprehensive approach to advancing transformer technology. These studies have received significant short-term attention and demonstrate the critical need to improve reliability, efficiency, and environmental sustainability in transformer insulation systems.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e(iii) Most Influential Publications in These Domains\u003c/h2\u003e\u003cp\u003eThe document burst analysis reveals a clear trajectory of emerging research topics, with older works being succeeded by newer, impactful studies. The article by [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] stands out as an early foundational work that gained widespread attention from 2013 to 2017. This study focused on enhancing transformer oil properties using Fe₃O₄ nanoparticles, demonstrating a significant improvement in breakdown voltage under power frequency and lightning impulse conditions. By aligning with the global trend of replacing mineral oils with eco-friendly alternatives, such as vegetable oils, it addressed pressing concerns about biodegradability, safety, and performance, thus cementing its relevance.\u003c/p\u003e\u003cp\u003eFollowing this foundational work, the research landscape evolved to encompass broader advancements. [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] emphasized the development of AI-based techniques for Dissolved Gas Analysis (DGA), a critical diagnostic tool for transformer health monitoring. This shift toward intelligent systems reflects the increasing demand for reliable and automated fault detection methods. Similarly, [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] highlighted the role of TiO₂ nanoparticles in enhancing transformer oil's dielectric strength, emphasizing nanotechnology's potential to improve breakdown voltage and stability, which are directly linked to system reliability and longevity. Complementing these advancements, [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e] explored the interplay between nanoparticle conductivity, permittivity, and insulation performance, offering both experimental insights and theoretical frameworks for optimizing nanofluids.\u003c/p\u003e\u003cp\u003eTogether, these articles showcase a cohesive evolution in transformer research, from focusing on performance improvement to incorporating resilience and advanced diagnostics. This convergence of diagnostics, materials science, and intelligent systems not only addresses critical challenges but also provides a roadmap for integrating cutting-edge technologies into transformer applications, ensuring reliability and sustainability in the energy sector\u003c/p\u003e\u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis scientometric analysis systematically maps the research landscape of transformer oil studies from 1970 to 2024, identifying key trends, influential topics, and impactful contributions. The review reveals that dielectric strength, Density Functional Theory (DFT) studies, and power transformer diagnostics are dominant themes driving innovation in transformer oil technology. These topics highlight the growing emphasis on sustainability, reliability, and advanced diagnostic capabilities in modern energy systems.\u003c/p\u003e\u003cp\u003eThe main topic of this paper underscores the critical role of transformer oils in maintaining power system efficiency and reliability. Descriptive analysis highlights the geographical distribution of transformer oil research, with significant contributions from countries such as India, China, and the United States, reflecting their strong focus on energy infrastructure and innovation. Over time, the volume of publications has increased significantly, particularly in the last decade, coinciding with a global shift toward sustainable and environmentally friendly insulating fluids. This trend mirrors the rising regulatory pressures and advancements in material science, further accelerating the development of bio-based and nano-enhanced transformer oils. Recent advancements in bio-based and nano-enhanced insulating fluids have addressed both performance limitations and environmental concerns. These developments align with the global energy sector's shift toward sustainable solutions, driven by stricter regulations and the need for modernized power infrastructure. The dual focus on enhancing dielectric properties and ensuring environmental compatibility reflects the transformative potential of these innovations.\u003c/p\u003e\u003cp\u003eThe compiled clusters from the co-citation analysis contribute significantly to the understanding of this research area. For instance, Cluster 0 (Dielectric Strength) emphasizes foundational studies and advancements in nanofluids, demonstrating their role in enhancing dielectric properties. Cluster 1 (DFT Studies) highlights the integration of computational techniques for improved dissolved gas analysis (DGA), advancing transformer fault diagnostics. Cluster 2 (Power Transformer) focuses on real-time monitoring and AI-driven diagnostics, showcasing the convergence of technology and material innovation. Together, these clusters provide a cohesive narrative of how research has evolved, addressing challenges in transformer insulation and diagnostics.\u003c/p\u003e\u003cp\u003eThe data for this scientometric analysis was obtained from the Web of Science Core Collection (WOSCC), a widely recognized database for its comprehensive and high-quality indexing of scientific and social science publications. A total of 2,801 publications were analyzed across various journals, disciplines, and regions, providing a robust foundation for identifying key trends, influential themes, and seminal works in transformer oil research. While the use of WOSCC ensured access to a broad and reliable dataset, it is acknowledged that reliance on a single database may introduce a degree of publication bias. WOSCC is particularly noted for its higher publication standards and its expansive coverage compared to other datasets [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo further strengthen future bibliometric studies, researchers should consider employing more rigorous keyword selection and manual data reviews to minimize the inclusion of irrelevant studies [\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Expanding the analysis by incorporating additional databases, such as Scopus and PubMed, could help identify missing critical information and provide a more comprehensive perspective on transformer oil research. This approach would enable comparative analyses across datasets, ensuring a more holistic understanding of the research landscape.\u003c/p\u003e\u003cp\u003eCo-citation analysis played a pivotal role in shaping this paper by identifying relationships between publications and clustering them into thematic groups. This method enabled the visualization of intellectual structures within the research field, highlighting connections between traditional and emerging topics. For transformer oil research, co-citation analysis not only mapped the evolution of key themes but also pinpointed impactful studies that bridge disciplines such as material science, computational chemistry, and electrical engineering.\u003c/p\u003e\u003cp\u003eIn conclusion, this scientometric review emphasizes the transformative potential of integrating advanced diagnostics, sustainable materials, and innovative technologies into transformer oil systems. It provides a comprehensive analysis of the research landscape of transformer insulating oils, examining trends over time, journal contributions, co-citations, prominent authors, countries, institutions, keywords, and references. The scientometric approach employed in this study contributes to the body of knowledge by offering a holistic view of transformer oil research. By synthesizing historical trends and identifying emerging research directions, this paper presents a roadmap for future studies, ensuring continued progress in addressing challenges related to energy reliability and sustainability.\u003c/p\u003e\u003cp\u003eThis study serves as a valuable resource for academia, industry professionals, and policymakers, offering insights into the overall trends, current status, and potential research questions in the field. Through this comprehensive analysis, a deeper understanding is provided of how transformer oil research is evolving to meet the demands of modern power systems.\u003c/p\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003eLimitation and Recommendation\u003c/h2\u003e\u003cp\u003eThis study acknowledges several limitations, including the exclusive use of the Web of Science (WOS) database, which may have led to the omission of relevant works indexed in Scopus or Google Scholar. Additionally, reliance on specific keywords and the inclusion of only English-language publications may have introduced selection bias. To strengthen future research, broader database coverage and manual screening are recommended. Furthermore, based on bibliometric trends and cluster analysis, future directions include promoting international collaborations, especially among leading countries such as China and India, and encouraging interdisciplinary efforts that connect material science, nanotechnology, and smart grid systems. Long-term performance evaluations of green insulating oils, standardized testing methods, and the expanded use of scientometric tools are also vital to enhance data reliability and support strategic research planning.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003ch2\u003eDeclaration of interest\u003c/h2\u003e\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eConceptualization, Data Analysis and Manuscript Writing - original draft: **Dzhahirul Zamri** . Software, Research Methodology and Writing - review and editing: **Mohd Iqbal Mohd Noor** . Conceptualization and Formal Analysis: **N. H. Nik Ali** . Conceptualization and Supervision: **M. H. Mamat** . Manuscript Review and Intellectual Rigor: **Noor Khairin Mohd** . 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Azra \u003cem\u003eet al.\u003c/em\u003e, \"Mapping of marine lobster research: A global outlook,\" (in English), \u003cem\u003eFrontiers in Marine Science\u003c/em\u003e, Review vol. 9, 2022-September-02 2022, doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fmars.2022.976199\u003c/span\u003e\u003cspan address=\"10.3389/fmars.2022.976199\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Scientometric, Cluster, Transformer Oil, High-voltage, Insulation Performance","lastPublishedDoi":"10.21203/rs.3.rs-8082286/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8082286/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study explores global trends and research insights in transformer oil insulation, focusing on the evolution of materials and diagnostic technologies from 1970 to 2024. Transformer oil plays a critical role in maintaining the reliability and longevity of high-voltage electrical equipment. Over the decades, research in this area has expanded significantly, covering advancements in base fluids, nano-enhancements, and diagnostic techniques. However, despite this growing body of literature, no comprehensive bibliometric study has been conducted specifically on transformer oil research. In particular, no study to date has utilized CiteSpace or similar tools to map the intellectual structure and evolving research themes in this domain. This gap presents an opportunity to provide a structured and data-driven understanding of the field\u0026rsquo;s development. To address this, the paper presents a combined descriptive and scientometric analysis of transformer oil research. The descriptive analysis highlights publication output, geographical distribution, and authorship patterns. Scientometric analysis, conducted using CiteSpace, visualizes bibliometric networks to identify influential authors, institutions, and emerging research clusters. A total of 2,801 publications were analyzed, resulting in the identification of 12 major clusters, with prominent themes including Dielectric Strength, DFT Studies, and Dissolved Gas Analysis (DGA). These clusters reveal the rise of nano-enhanced insulating fluids and advanced diagnostic techniques. By identifying key trends, contributors, and thematic focuses, this review offers a comprehensive overview of the transformer oil research landscape and provides strategic insights to guide future innovations in insulation technologies and sustainable energy systems.\u003c/p\u003e","manuscriptTitle":"Global Trends and Insight in Transformer Oil Insulation Research: A Scientometric Perspective","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-26 11:32:31","doi":"10.21203/rs.3.rs-8082286/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"12342f53-d936-4625-9e70-d833c1808b5b","owner":[],"postedDate":"November 26th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-20T21:20:35+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-26 11:32:31","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8082286","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8082286","identity":"rs-8082286","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}