Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis

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Therefore, the search for environmentally friendly renewable energy sources is crucial for achieving sustainability. Biomass energy is gaining global attention as a renewable energy option, particularly through the process of hydrothermal liquefaction, which converts wet biomass into bio-crude oil. Methods Hydrothermal liquefaction is a complex process that is challenging to explain, leading to research on machine learning models for this process. These models aim to predict values and investigate the impact of variables on the hydrothermal liquefaction process. These models aim to predict values and investigate the impact of variables on the hydrothermal liquefaction process. However, the development of machine learning in hydrothermal liquefaction is still limited due to its novelty and the time required for comprehensive study. Thus, the objective of this study was to analyze relevant publications in the Scopus database, focusing on indexed ML and HTL keywords, to understand keyword associations and co-citations. Results The results reveal an increasing trend in the study of ML in the HTL process, with a growing interest from various countries. Conclusion Notably, China currently holds the largest share of ML research in HTL processes, with most published works falling within the field of engineering. The keyword “liquefaction” emerges as the most popular term in these publications. 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F1000Research 2025, 13 :1131 ( https://doi.org/10.12688/f1000research.156514.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. Close Copy Citation Details Export Export Citation Sciwheel EndNote Ref. Manager Bibtex ProCite Sente EXPORT Select a format first Track Share ▬ ✚ Research Article Revised Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] Tossapon Katongtung https://orcid.org/0000-0001-9612-1883 1,2 , Somboon Sukpancharoen 3 , Sakprayut Sinthupinyo 4 , Nakorn Tippayawong 1 Tossapon Katongtung https://orcid.org/0000-0001-9612-1883 1,2 , Somboon Sukpancharoen 3 , Sakprayut Sinthupinyo 4 , Nakorn Tippayawong 1 PUBLISHED 11 Mar 2025 Author details Author details 1 Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand 2 Graduate PhD Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand 3 Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, 40002, Thailand 4 Siam Research and Innovation Co., Ltd, Bangkok, Thailand Tossapon Katongtung Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Validation, Visualization, Writing – Original Draft Preparation Somboon Sukpancharoen Roles: Formal Analysis, Writing – Review & Editing Sakprayut Sinthupinyo Roles: Formal Analysis, Writing – Review & Editing Nakorn Tippayawong Roles: Conceptualization, Formal Analysis, Funding Acquisition, Supervision, Writing – Review & Editing OPEN PEER REVIEW DETAILS REVIEWER STATUS This article is included in the Energy gateway. Abstract Background Energy shortages and global warming have been significant issues throughout history. Therefore, the search for environmentally friendly renewable energy sources is crucial for achieving sustainability. Biomass energy is gaining global attention as a renewable energy option, particularly through the process of hydrothermal liquefaction, which converts wet biomass into bio-crude oil. Methods Hydrothermal liquefaction is a complex process that is challenging to explain, leading to research on machine learning models for this process. These models aim to predict values and investigate the impact of variables on the hydrothermal liquefaction process. These models aim to predict values and investigate the impact of variables on the hydrothermal liquefaction process. However, the development of machine learning in hydrothermal liquefaction is still limited due to its novelty and the time required for comprehensive study. Thus, the objective of this study was to analyze relevant publications in the Scopus database, focusing on indexed ML and HTL keywords, to understand keyword associations and co-citations. Results The results reveal an increasing trend in the study of ML in the HTL process, with a growing interest from various countries. Conclusion Notably, China currently holds the largest share of ML research in HTL processes, with most published works falling within the field of engineering. The keyword “liquefaction” emerges as the most popular term in these publications. READ ALL READ LESS Keywords AI, Clean energy, Data analytics, Climate action, bibliometric analysis Corresponding Author(s) Nakorn Tippayawong ( [email protected] ) Close Corresponding author: Nakorn Tippayawong Competing interests: No competing interests were disclosed. Grant information: This work was partially supported by National Research Council of Thailand (NRCT) contract no. N42A671047, SCG Cement Co., Ltd. and Chiang Mai University (CMU) contract no. cmu/eet/2567. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Copyright: © 2025 Katongtung T et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. How to cite: Katongtung T, Sukpancharoen S, Sinthupinyo S and Tippayawong N. Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :1131 ( https://doi.org/10.12688/f1000research.156514.3 ) First published: 04 Oct 2024, 13 :1131 ( https://doi.org/10.12688/f1000research.156514.1 ) Latest published: 11 Mar 2025, 13 :1131 ( https://doi.org/10.12688/f1000research.156514.3 ) Revised Amendments from Version 2 The author has revised the manuscript based on the suggestions of both reviewers and has uploaded a reply sheet detailing the responses to the system. The abstract and introduction have been updated in accordance with the reviewers' suggestions The author has revised the manuscript based on the suggestions of both reviewers and has uploaded a reply sheet detailing the responses to the system. The abstract and introduction have been updated in accordance with the reviewers' suggestions See the authors' detailed response to the review by Venu Babu Borugadda See the authors' detailed response to the review by Lili Qian See the authors' detailed response to the review by Muntasir Shahabuddin and Andrew Charlebois READ REVIEWER RESPONSES Introduction Currently, numerous countries worldwide are actively seeking alternative energy sources to replace depleted resources and ensure environmental preservation amidst the era of global warming. 1 – 3 Biomass stands as a prominent alternative in the realm of renewable energy, referred to as biomass energy. 4 Biomass energy refers to the energy derived from various organic materials that serve as natural energy sources. These materials encompass a wide range of substances, including organic waste, agricultural residues, industrial byproducts, manure, and fuel crops such as rice husks, rice straw, bagasse, sugar cane leaves and shoots, wood, wood chips, fibers, palm kernel shells, cassava residue, corn cobs, husks, coconut shells, and more. The conversion of biomass into usable energy involves processes such as fermentation, combustion, hydrothermal treatment, or other methodologies, which transform biomass into heat or gas for energy utilization. 5 , 6 Additionally, there exists a biomass processing method known as hydrothermal liquefaction, which converts wet biomass into bio-crude oil. This process involves subjecting the biomass to high-temperature and high-pressure conditions in a liquid water environment, resulting in the production of bio-crude oil. 7 , 8 Hydrothermal liquefaction (HTL) is a thermal depolymerization process that facilitates the conversion of wet biomass and other macromolecules into crude oil, commonly known as bio-crude oil. This transformation occurs under medium temperature (180-400 °C) and high-pressure (5-30 MPa) conditions, enabling the efficient conversion of the biomass feedstock. 9 , 10 Bio-crude oil exhibits a high energy density, characterized by a higher heating value ranging from 33.8 to 36.9 MJ/kg. It typically contains 5-20 wt% oxygen, along with renewable chemicals. HHV are critical in HTL as they measure the energy content of bio-crude, determining its quality and efficiency as a fuel. Bio-crude with higher HHV is suitable for applications like direct combustion for heat and power, upgrading into transportation fuels (diesel, gasoline, jet fuel), and as a feedstock for biorefineries. It can also be used in marine and heavy industries, blended with fossil fuels to reduce emissions, or converted into biochar for additional benefits. HHV ensure bio-crude’s economic viability and its role as a sustainable energy source. 11 The study of various variables and their effects under HTL processing conditions is challenging and intricate. Consequently, researchers worldwide have shown keen interest in investigating the behavior of variables within the HTL environment. Machine learning has emerged as the most popular technology to address such difficult and complex problems efficiently. It offers promising avenues for understanding and optimizing HTL processes through data-driven analysis and predictive modeling. Machine Learning (ML) can be considered as the learning component of a machine. It serves as the foundational element of Artificial Intelligence (AI), that enables AI systems to acquire knowledge and exhibit intelligent behavior. AI utilizes ML techniques to develop and enhance its intelligent capabilities, allowing it to learn from data, recognize patterns, make predictions, and adapt to new information or situations. In essence, ML forms an integral part of the AI framework, enabling the creation and manifestation of intelligence in AI systems. 12 , 13 Indeed, ML is commonly associated with the learning models of artificial intelligence. ML involves programming AI systems to learn from data. Once programmed, the machine leverages the available data to train and refine its own intelligence through practice and iterative processes. ML has found extensive applications across various domains, including medicine, education, economy, and engineering. In medicine, it is used for diagnostics, personalized treatments, and drug discovery. In education, it aids in adaptive learning and intelligent tutoring systems. In the economy, it assists with data analysis, forecasting, and fraud detection. In engineering, it facilitates automation, optimization, and predictive maintenance. The widespread adoption of ML in both research and everyday life has opened up new possibilities and opportunities for leveraging data-driven intelligence in solving complex problems and making informed decisions. 13 – 18 Moreover, ML is extensively applied in the field of energy to predict and establish correlations among variables in highly complex processes that cannot be easily explained through conventional means. For instance, a study by Onsree and Tippayawong (2020) demonstrated the development of a ML application for predicting yields of solid products obtained from biomass torrefaction. This model achieved a remarkable accuracy with an R2 value of approximately 0.9 with a root mean squared error (RMSE) value of approximately 0.07. Furthermore, the application also shed light on the interrelationships between variables within the torrefaction process, providing valuable insights into the underlying mechanisms. Such applications of ML in energy-related research not only enhance predictive capabilities but also contribute to a deeper understanding of complex processes and their underlying dynamics. 19 Additionally, Phromphithak et al. (2021) conducted a study where they employed ML techniques to predict the production of cellulose-rich materials during biomass pretreatment using ionic liquid solvents. The developed model exhibited a high accuracy, with an R2 value of 0.94 with an RMSE value of approximately 0.22. Moreover, the study successfully elucidated the behavior of ionic liquids and their impact on the ML-based pretreatment process. Ionic liquid (IL) pretreatment enhances lignocellulosic biomass conversion by dissolving lignin, reducing cellulose crystallinity, and improving accessibility for enzymatic or catalytic processes. ILs enable selective dissolution, efficient cellulose isolation, and recycling, offering a sustainable pretreatment method. Machine learning further aids in predicting outcomes and understanding IL effects on biomass. 20 In a study by Prasertpong et al. (2022), the researchers investigated the synergistic effects observed during the co-pyrolysis of biomass and plastic waste using ML techniques. The aim of the study was to gain insights into the variables that influence the co-pyrolysis process. By employing machine learning, the researchers were able to uncover and comprehend the intricate relationships and interactions between these variables, shedding light on the synergistic effects observed during the co-pyrolysis process. This study highlights the potential of ML in providing valuable insights and understanding complex processes involving biomass and plastic waste co-pyrolysis. 21 Furthermore, there have been studies that utilize ML in the context of HTL processes. In a previous research endeavor, an ML model was developed for predicting biocrude yields and higher heating values obtained from HTL of wet biomass and waste materials. The model achieved a high precision with an R 2 value of nearly 0.9 with normalized RMSE of 0.16. Additionally, it elucidated the relationships among variables that directly and indirectly impact the HTL process. This study showcases the potential of ML in accurately predicting and comprehending the complex interplay of variables in HTL processes, contributing to a deeper understanding of the factors influencing HTL and enabling more efficient and effective utilization of wet biomass and waste for biocrude production. 22 There are other studies related to ML in HTL processes a number of similar. 23 , 24 Indeed, the development of ML in the field of HTL is relatively new and holds great potential for further study and exploration in the future. Consequently, the collection and comprehensive search for research pertaining to the development of ML in the HTL process become crucial and should be given significant emphasis. This endeavor would facilitate the identification of existing studies, trends, and advancements in the application of ML techniques specifically for HTL, thereby laying the groundwork for future research and advancements in this domain. By focusing on this area of research, researchers can deepen their understanding of the potential of ML in HTL and further contribute to its development and utilization in the pursuit of sustainable and efficient energy production. Bibliometric analysis involves a systematic statistical examination of research data extracted from extensive databases, aiming to evaluate various aspects of scholarly work. This analysis serves as a valuable tool for measuring research quality, including the productivity and impact of individual researchers and institutions. By employing bibliometric methods, researchers can quantitatively assess factors such as publication output, citation counts, collaboration networks, journal rankings, and other bibliographic indicators. These analyses provide valuable insights into the performance and influence of researchers and institutions within specific fields of study, contributing to the evaluation and comparison of research outcomes across various domains. 25 Bibliometric analysis is a research methodology that emphasizes quantitative and statistical investigations. It enables the description and analysis of various aspects of scholarly publications or literature, such as the type of publication, the countries, institutions, and authors associated with the research. By conducting long-term studies, bibliometric analysis allows researchers to identify trends in the growth or decline of research within specific fields. Consequently, bibliometric data serves as a valuable foundation for monitoring scientific and technological research, providing insights into the progress and development of knowledge in various domains. 26 Bibliographic analysis typically involves selecting a set of keywords to search relevant literature databases. These keywords are chosen based on the topic and title of the articles to retrieve information related to the conceptual framework and writing style within a specific field of study. Electronic bibliographic databases offer vast opportunities for efficient and rapid access to a wide range of scholarly resources. By utilizing these databases, researchers can benefit from an extensive collection of literature and gain quicker access to relevant information for their studies. This enables more efficient literature review and analysis, aiding in the advancement of research in a particular area. 27 The application of the bibliometric method in the HTL field remains limited. Similar study of bibliometrics of HTL and ML was recently reported by Qian et al. 28 The review explores the application of ML in optimizing HTL for biomass conversion. Using bibliometric analysis tools, the study identifies research trends, key contributors, and future directions from 2020 to 2024. ML techniques are highlighted as effective for predicting product yields and optimizing HTL conditions, addressing challenges related to complex reaction pathways and feedstock variability. The paper emphasizes the growing role of ML in advancing HTL efficiency and sustainability. This review contributes to scientific and research growth by integrating ML with HTL to optimize biomass conversion. Through bibliometric analysis, it identifies key research trends, gaps, and future directions, fostering collaboration among researchers and institutions. By leveraging ML-based predictive models, the study enhances process efficiency, reducing trial-and-error experimentation. Additionally, its insights support industrial applications and sustainable energy development, making it a valuable resource for advancing both scientific knowledge and practical implementation in renewable energy research. The objective of this study was to perform a bibliographic analysis using publications related to ML and HTL keywords indexed in the Scopus database. The analysis employed a quantitative methodology to examine the bibliography of published articles in the field. To accomplish this objective, the study utilized VOSviewer version 1.6.20 , a bibliometric analysis tool, for conducting the bibliographic analysis. VOSviewer enables visualizations and quantitative analyses of bibliographic data, facilitating the exploration of relationships, trends, and patterns within the literature. 29 , 30 Based on the available literature review, this work was among the first bibliometric studies conducted to evaluate research trends specifically in the development of ML within the context of HTL. As the research on HTL-based ML development is still in its early stages and relatively limited at present, this study fills an important knowledge gap in the field. The outcomes of this analysis will contribute significantly to the formulation of a comprehensive research plan by identifying potential future research directions and fostering collaborative relationships among researchers in this emerging field. Methods Bibliometrics is a research methodology that involves the analysis of quantitative and statistical data. It is commonly used to examine various relationships, such as the connections between authors, the relationship between a research subject and an author’s work, scholarly works themselves, scholarly citations, and citation tracking. By employing bibliometric techniques, researchers can gain insights into the patterns and dynamics of scholarly communication, collaboration, and impact within a particular field or discipline. It provides a systematic approach to understanding the scholarly landscape and can be utilized to assess research productivity, influence, and trends. 31 Bibliometric analysis offers a distinct perspective that complements more comprehensive analyses. It enables researchers to categorize and analyze extensive amounts of data derived from research conducted over a specific timeframe. By employing quantitative techniques, bibliometric analysis can help mitigate or minimize researcher bias, unlike systematic reviews that often rely on qualitative methods. The objectivity of bibliometric analysis reduces the potential for interpretation bias introduced by scholars from diverse academic backgrounds. This allows for a more objective and standardized assessment of research output, impact, and trends, providing valuable insights into the scholarly landscape. 29 , 32 In this study, bibliographic analysis was employed to investigate and validate the prevailing trends in the research on ML development within HTL processes. The Scopus database was selected for this analysis due to its widespread usage, reliability, and comprehensive coverage across various disciplines. Scopus is known for its extensive collection of scholarly literature, making it a suitable choice for conducting bibliometric studies across diverse research areas. By utilizing the Scopus database, the study could effectively assess and examine the current state of ML development in HTL processes and gain valuable insights into the research landscape in this domain. 33 , 34 Indeed, the databases available in Scopus offer access to a vast number of documents, providing a wealth of information for bibliometric analysis. Scopus is renowned for its comprehensive coverage of scholarly literature, encompassing a wide range of disciplines and research fields. This extensive coverage ensures that researchers can access a substantial volume of documents relevant to their study. Furthermore, Scopus provides robust citation information, including citation counts and citation networks, which can be crucial for analyzing the impact and influence of research articles. The availability of comprehensive document and citation data in Scopus enhance the accuracy and reliability of bibliometric analyses and contribute to a more comprehensive understanding of research trends and dynamics. 35 Scopus is a comprehensive database that provides researchers with a range of resources for exploring and evaluating scholarly publications, patents, clinical trials, and policy documents. In this study, the Scopus database was utilized to conduct the bibliographic analysis. Keywords were employed to search for relevant publications based on their publication name, abstract, and author-provided keywords. The search criteria encompassed a set of specific keywords related to “Machine Learning,” “Deep Learning,” “Neural Network,” “Artificial Intelligence,” “ML,” “DL,” “NN,” and “AI,” as well as keywords related to “Hydrothermal Liquefaction,” “Liquefaction,” and “HTL.” The subject areas covered in this study include energy, biomass, engineering, computer science, chemistry, biology, and the environment. The search keywords were set to include publications from the year 1955 up to June 2023, with data downloaded on June 21, 2023. The inclusion criteria for this study encompassed original articles published in the English language. These specific parameters and criteria were employed to ensure a focused and comprehensive analysis of the relevant literature in the field of ML development in HTL processes. Figure 1 shows the search technique used in this study to identify appropriate articles from the Scopus database. The complete bibliography data was downloaded in.csv format from the Scopus database. VOSviewer was used in bibliometric analysis in this work. Figure 1. Flow chart of the search approach. Results Main data and annual publication growth Table 1 provides an overview of the number of documents related to ML in the HTL process, categorized by year of publication from 1955 to 2023. The table shows the number of documents found for each year, indicating the volume of research conducted in this area. Figure 2 illustrates the trend in the number of documents related to ML in the HTL process per year. The graph indicates a consistent increase in the number of publications since 2003, with a significant surge observed from 2018 onwards. Notably, in 2018, 2019, 2020, 2021, and 2022, the number of documents were 63, 100, 105, 132, and 158, respectively. This trend suggests a growing interest and popularity in the study of ML in the HTL process, particularly in recent years. The findings from Figure 2 indicate that ML in the HTL process has garnered substantial attention and continues to gain momentum, highlighting its significance and potential for further advancements in research and applications. Table 1. Main information. Description Results Information about data Time span 1955-2023 Documents 1,362 Citation 17,068 H-index 69 References 33,076 Document type Article 1,148 Conference Paper 155 Conference Review 20 Book Chapter 17 Review 17 Book 3 Data Paper 2 Figure 2. Document by year. (via Scopus on July 1, 2023). Top 10 in various fields Based on the analysis of search results related to ML in the HTL process, it was possible to rank the top 10 in terms of subject area, author name, source title, and country. Engineering emerged as the most published subject area, with 350 publications, as depicted in Figure 3 . Among the authors, Samui, P, stood out with the highest number of publications on ML in HTL processes, totaling 20 publications, as shown in Figure 4 . The most frequently published source title was “Bioresource Technology,” with a notable 43 publications, as illustrated in Figure 5 . China emerged as the country with the highest number of publications on the studied topic, with a total of 407 publications, as depicted in Figure 6 . Figure 3. Published subject area. Figure 4. Authors with the most publications related to ML in the HTL process. Figure 5. Source titles related to ML in the HTL process. Figure 6. Countries with the most publications related to ML in the HTL process. From Figure 6 , it can be seen that China ranks first in publications on ML in HTL processes, which has more than twice that of the second place. Figure 6 reveals a diverse range of disciplines showing interest in studying ML in HTL processes. Engineering emerges as the most engaged subject, constituting 13.5% or 350 publications. Following closely behind are Earth and Planetary Sciences and Energy, accounting for 10.5% or 272 publications. This demonstrates a shift towards adapting to human needs in environmental preservation and exploring clean energy sources through HTL. The application of ML as a tool enables time and resource savings in experimental endeavors, reinforcing its significance in this field. 22 Bibliometric analysis The bibliometric analysis in this study utilized the VOSviewer program to construct cluster maps and identify relationships within the database. The cluster map construction involved three aspects: co-citation correlation, keyword correlation, and published countries. Figure 7 displays the cluster map based on co-authorship authors, revealing connections among various author groups. A total of 38 author groups were identified. The analysis indicated that Zhang Y. had the highest number of links, with 21 links and 506 citations. However, it was observed that the number of links did not have a significant impact on citations. Li H.I. and Goh A.T.C. had 20 and 6 links, respectively, but garnered 1048 and 877 citations, respectively. Figure 7. Cluster map based on the co-authorship authors (via Scopus on July 1, 2023). Figure 8 represents the cluster map based on the co-occurrence of all keywords. It revealed the existence of 5 keyword groups, with a predominant focus on HTL process-related keywords. Interestingly, ML keywords did not form a substantial cluster, potentially due to their relatively lower number compared to the keywords associated with the HTL process chain. The top 5 most common keywords were liquefaction, article, human, soil liquefaction, and male, with frequencies of 590, 308, 219, 203, and 173, respectively. The keyword ML appeared in the 6th position, specifically in the form of “neural networks,” with a frequency of 156. Figure 8. Cluster map based on the co-occurrence all keywords (via Scopus on July 1, 2023). Figure 9 depicts the cluster map based on co-authorship countries, revealing 13 distinct groupings of countries. Among them, China emerged with the highest number of links, totaling 6,048 citations and 94 links. The United States ranked second in terms of links, accumulating 5,767 citations and 71 links. India secured the third position in links, with 2,193 citations and 39 links. Figure 9. Cluster map based on the co-authorship countries (via Scopus on July 1, 2023). Conclusion The bibliometric analysis conducted in this study revealed a continuous increase in publications related to ML in HTL processes from 1955 to 2023. It was found that China has been the leading country in terms of publishing research in this area. The “Bioresource Technology” journal emerged as the most popular choice for publishing articles on ML in the HTL process. The field of engineering, particularly with a focus on the keyword “Liquefaction,” garnered the most interest among researchers. These findings provide valuable insights and can serve as a guide for future studies on ML in HTL processes, offering a comprehensive overview of the current state of research in this domain. Ethical approval The dataset described in this article does not involve any human subjects, animal experiments, or data collected from social media platforms. Author roles Katongtung T: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Validation, Visualization, Writing – Original Draft Preparation; Sukpancharoen S: Formal Analysis, Writing – Review & Editing; Sinthupinyo S: Formal Analysis, Writing – Review & Editing; Tippayawong N: Conceptualization, Supervision, Funding Acquisition, Formal Analysis, Writing – Review & Editing. Data availability Mendeley Data: Dataset for: Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis, DOI: 10.17632/st4xh92wm2.1 . 36 Data are available under the terms of the Creative Commons Attribution 4.0 International license (CC-BY 4.0). Acknowledgements The first author wishes to thank the Teaching and Research Assistant Scholarships from the CMU Graduate School and the NRCT Research & Researcher for Industry program, Thailand. References 1. 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Publisher Full Text Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 04 Oct 2024 ADD YOUR COMMENT Comment Author details Author details 1 Department of Mechanical Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand 2 Graduate PhD Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand 3 Department of Agricultural Engineering, Faculty of Engineering, Khon Kaen University, Khon Kaen, 40002, Thailand 4 Siam Research and Innovation Co., Ltd, Bangkok, Thailand Tossapon Katongtung Roles: Conceptualization, Data Curation, Formal Analysis, Investigation, Methodology, Project Administration, Validation, Visualization, Writing – Original Draft Preparation Somboon Sukpancharoen Roles: Formal Analysis, Writing – Review & Editing Sakprayut Sinthupinyo Roles: Formal Analysis, Writing – Review & Editing Nakorn Tippayawong Roles: Conceptualization, Formal Analysis, Funding Acquisition, Supervision, Writing – Review & Editing Competing interests No competing interests were disclosed. Grant information This work was partially supported by National Research Council of Thailand (NRCT) contract no. N42A671047, SCG Cement Co., Ltd. and Chiang Mai University (CMU) contract no. cmu/eet/2567. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. Article Versions (3) version 3 Revised Published: 11 Mar 2025, 13:1131 https://doi.org/10.12688/f1000research.156514.3 version 2 Revised Published: 02 Jan 2025, 13:1131 https://doi.org/10.12688/f1000research.156514.2 version 1 Published: 04 Oct 2024, 13:1131 https://doi.org/10.12688/f1000research.156514.1 Copyright © 2025 Katongtung T et al . This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Download Export To Sciwheel Bibtex EndNote ProCite Ref. Manager (RIS) Sente metrics Views Downloads F1000Research - - PubMed Central info_outline Data from PMC are received and updated monthly. - - Citations open_in_new 0 open_in_new 0 open_in_new SEE MORE DETAILS CITE how to cite this article Katongtung T, Sukpancharoen S, Sinthupinyo S and Tippayawong N. Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :1131 ( https://doi.org/10.12688/f1000research.156514.3 ) NOTE: If applicable, it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS track receive updates on this article Track an article to receive email alerts on any updates to this article. TRACK THIS ARTICLE Share Open Peer Review Current Reviewer Status: ? Key to Reviewer Statuses VIEW HIDE Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Version 3 VERSION 3 PUBLISHED 11 Mar 2025 Revised Views 0 Cite How to cite this report: Kooh MRR. Reviewer Report For: Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :1131 ( https://doi.org/10.5256/f1000research.177841.r376870 ) The direct URL for this report is: https://f1000research.com/articles/13-1131/v3#referee-response-376870 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 07 May 2025 Muhammad Raziq Rahimi Kooh , Universiti Brunei Darussalam, Darussalam, Brunei Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.177841.r376870 This work focuses on the bibliographic evaluation of hydrothermal liquefaction using Scopus data. Please see the specific comment below for details. Minor error (A) ensure all the acronyms are defined on their first appearance. (B) Ensure ... Continue reading READ ALL This work focuses on the bibliographic evaluation of hydrothermal liquefaction using Scopus data. Please see the specific comment below for details. Minor error (A) ensure all the acronyms are defined on their first appearance. (B) Ensure all the symbols, such as R2 are formatted correctly. Introduction. The section related to ionic liquids does not seem relevant. The table description and figure’s caption should be informative. The lack of discussion is a major concern. While specific countries, journals or authors may have the most HTL publications, what insights can you draw from these? What does all this data mean? Apart from ML, which is a current trend for all domains of science, are there any other specific insights that are obtained from the bibliographic analysis? Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? No Competing Interests: No competing interests were disclosed. Reviewer Expertise: Machine learning I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Kooh MRR. Reviewer Report For: Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :1131 ( https://doi.org/10.5256/f1000research.177841.r376870 ) The direct URL for this report is: https://f1000research.com/articles/13-1131/v3#referee-response-376870 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Version 2 VERSION 2 PUBLISHED 02 Jan 2025 Revised Views 0 Cite How to cite this report: Qian L. Reviewer Report For: Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :1131 ( https://doi.org/10.5256/f1000research.176096.r355299 ) The direct URL for this report is: https://f1000research.com/articles/13-1131/v2#referee-response-355299 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 09 Jan 2025 Lili Qian , Jiangsu University, Zhenjiang, Jiangsu, China Approved VIEWS 0 https://doi.org/10.5256/f1000research.176096.r355299 This manuscript ... Continue reading READ ALL This manuscript can be accepted. Competing Interests: No competing interests were disclosed. Reviewer Expertise: My research focused on hydrothermal liquefaction and machine learning. We submitted a similar paper titled "A Review on Machine Learning-Aided Hydrothermal Liquefaction Based on Bibliometric Analysis" on August this year. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Qian L. Reviewer Report For: Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :1131 ( https://doi.org/10.5256/f1000research.176096.r355299 ) The direct URL for this report is: https://f1000research.com/articles/13-1131/v2#referee-response-355299 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Respond or Comment COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Borugadda VB. Reviewer Report For: Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :1131 ( https://doi.org/10.5256/f1000research.176096.r341351 ) The direct URL for this report is: https://f1000research.com/articles/13-1131/v2#referee-response-341351 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 03 Jan 2025 Venu Babu Borugadda , University of Saskatchewan, Saskatoon, Canada Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.176096.r341351 1. Are there a similar papers in the literature with bibliometric analysis ? Please report some with references and explain how these papers are contributing for the scientific/research growth ? 2. What is the take home message from this ... Continue reading READ ALL 1. Are there a similar papers in the literature with bibliometric analysis ? Please report some with references and explain how these papers are contributing for the scientific/research growth ? 2. What is the take home message from this bibliometric analysis on HTL and ML? 3. What are the key challenges of this type of work ? 4. Besides scopus what are the other potential search engines to make bibliometric analysis ? 5. Please mention the tables and figures after they are referred to in the text. Many tables and figures are reported before they are being referred. 6. Fig 3, how ML and HTL are related to the Computer Science, Earth and planetary, Medicine and biochemistry? Please justify or remove them using more filters. 7. Fig.4, please mention the references of top 10 authors in the reference section. 8. Page 8 last paragraph, first sentence, it's figure 6, not 3. 9. Fig.8, why human, soil liquefaction, male keywords are considered ? Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Methodology and key question raised in comments 1-5 I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Borugadda VB. Reviewer Report For: Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :1131 ( https://doi.org/10.5256/f1000research.176096.r341351 ) The direct URL for this report is: https://f1000research.com/articles/13-1131/v2#referee-response-341351 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 05 Feb 2025 Tossapon katongtung , Graduate PhD Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand 05 Feb 2025 Author Response Revised, as suggested. Competing Interests: No competing interests were disclosed. Revised, as suggested. Revised, as suggested. Competing Interests: No competing interests were disclosed. Close Report a concern Author Response 11 Mar 2025 Tossapon katongtung , Graduate PhD Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand 11 Mar 2025 Author Response 1.Revised, as suggested. please refer to the Introduction section.Revised, as suggested. please refer to the Introduction section. 2.As the research on HTL-based ML development is still in its early stages ... Continue reading 1.Revised, as suggested. please refer to the Introduction section.Revised, as suggested. please refer to the Introduction section. 2.As the research on HTL-based ML development is still in its early stages and relatively limited at present, this study fills an important knowledge gap in the field. The outcomes of this analysis will contribute significantly to the formulation of a comprehensive research plan by identifying potential future research directions and fostering collaborative relationships among researchers in this emerging field. 3.The limitations of this type of research include the validity of the data and the selection of relevant data for presentation. 4.The Web of Science, Scopus, Dimensions, and PubMed databases can be used to perform bibliometric analyses using VOSviewer. 5.Revised, as suggested. 6.This information was obtained from the Scopus database and has not been manipulated, as the dataset was preserved in its original form. 7.It is not feasible to cite all of the publications in this ranking, as the list includes a substantial number of entries—for example, the top ranking alone comprises 20 publications on the topic. Moreover, citations are unnecessary here because the discussion is based on quantitative variables 8.Revised, as suggested. 9.This information was obtained from the Scopus database and has not been manipulated, as the dataset was preserved in its original form. 1.Revised, as suggested. please refer to the Introduction section.Revised, as suggested. please refer to the Introduction section. 2.As the research on HTL-based ML development is still in its early stages and relatively limited at present, this study fills an important knowledge gap in the field. The outcomes of this analysis will contribute significantly to the formulation of a comprehensive research plan by identifying potential future research directions and fostering collaborative relationships among researchers in this emerging field. 3.The limitations of this type of research include the validity of the data and the selection of relevant data for presentation. 4.The Web of Science, Scopus, Dimensions, and PubMed databases can be used to perform bibliometric analyses using VOSviewer. 5.Revised, as suggested. 6.This information was obtained from the Scopus database and has not been manipulated, as the dataset was preserved in its original form. 7.It is not feasible to cite all of the publications in this ranking, as the list includes a substantial number of entries—for example, the top ranking alone comprises 20 publications on the topic. Moreover, citations are unnecessary here because the discussion is based on quantitative variables 8.Revised, as suggested. 9.This information was obtained from the Scopus database and has not been manipulated, as the dataset was preserved in its original form. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 05 Feb 2025 Tossapon katongtung , Graduate PhD Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand 05 Feb 2025 Author Response Revised, as suggested. Competing Interests: No competing interests were disclosed. Revised, as suggested. Revised, as suggested. Competing Interests: No competing interests were disclosed. Close Report a concern Author Response 11 Mar 2025 Tossapon katongtung , Graduate PhD Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand 11 Mar 2025 Author Response 1.Revised, as suggested. please refer to the Introduction section.Revised, as suggested. please refer to the Introduction section. 2.As the research on HTL-based ML development is still in its early stages ... Continue reading 1.Revised, as suggested. please refer to the Introduction section.Revised, as suggested. please refer to the Introduction section. 2.As the research on HTL-based ML development is still in its early stages and relatively limited at present, this study fills an important knowledge gap in the field. The outcomes of this analysis will contribute significantly to the formulation of a comprehensive research plan by identifying potential future research directions and fostering collaborative relationships among researchers in this emerging field. 3.The limitations of this type of research include the validity of the data and the selection of relevant data for presentation. 4.The Web of Science, Scopus, Dimensions, and PubMed databases can be used to perform bibliometric analyses using VOSviewer. 5.Revised, as suggested. 6.This information was obtained from the Scopus database and has not been manipulated, as the dataset was preserved in its original form. 7.It is not feasible to cite all of the publications in this ranking, as the list includes a substantial number of entries—for example, the top ranking alone comprises 20 publications on the topic. Moreover, citations are unnecessary here because the discussion is based on quantitative variables 8.Revised, as suggested. 9.This information was obtained from the Scopus database and has not been manipulated, as the dataset was preserved in its original form. 1.Revised, as suggested. please refer to the Introduction section.Revised, as suggested. please refer to the Introduction section. 2.As the research on HTL-based ML development is still in its early stages and relatively limited at present, this study fills an important knowledge gap in the field. The outcomes of this analysis will contribute significantly to the formulation of a comprehensive research plan by identifying potential future research directions and fostering collaborative relationships among researchers in this emerging field. 3.The limitations of this type of research include the validity of the data and the selection of relevant data for presentation. 4.The Web of Science, Scopus, Dimensions, and PubMed databases can be used to perform bibliometric analyses using VOSviewer. 5.Revised, as suggested. 6.This information was obtained from the Scopus database and has not been manipulated, as the dataset was preserved in its original form. 7.It is not feasible to cite all of the publications in this ranking, as the list includes a substantial number of entries—for example, the top ranking alone comprises 20 publications on the topic. Moreover, citations are unnecessary here because the discussion is based on quantitative variables 8.Revised, as suggested. 9.This information was obtained from the Scopus database and has not been manipulated, as the dataset was preserved in its original form. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Version 1 VERSION 1 PUBLISHED 04 Oct 2024 Views 0 Cite How to cite this report: Shahabuddin M and Charlebois A. Reviewer Report For: Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :1131 ( https://doi.org/10.5256/f1000research.171837.r341356 ) The direct URL for this report is: https://f1000research.com/articles/13-1131/v1#referee-response-341356 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 16 Dec 2024 Muntasir Shahabuddin , Chemical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA Andrew Charlebois , Chemical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA Not Approved VIEWS 0 https://doi.org/10.5256/f1000research.171837.r341356 Manuscript Title: Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis Overview: The reviewer would like to thank the editors of F1000 for the opportunity to provide feedback on the manuscript “Current scenario of machine ... Continue reading READ ALL Manuscript Title: Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis Overview: The reviewer would like to thank the editors of F1000 for the opportunity to provide feedback on the manuscript “Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis”. This study by Katongtung et al. leverages bibliometric analysis to collect and analyze publications relating to machine learning on hydrothermal liquefaction systems in the corpus of existing literature. Using a Scopus search, the authors uncovered the most productive authors in the field, and geopolitical regions with the greatest academic output. While the conclusions of this study (in which Chinese and US scientists tend to be the most productive in this area from a raw studies-published perspective) are useful, the manuscript may benefit from reformatting of figures for better clarity of the data being communicated, and for reconsideration of how each figure contributes to the overall narrative conclusion of the work. In light of the following comments, the reviewer recommends that this work is revised and resubmitted after major revisions, or reconsidered for indexing at another time. Major Comments: This paper should do a better job to introduce and motivate the problem/question it is trying to solve. The way the information is presented makes the analysis seem very exploratory and not directed. This leads to some of the figures, figure 3 for example, to seem disconnected from the overall story The work’s motivation reads well and is well cited. I would recommend quantifying the conditions HTL occurs at (i.e. the exact pressures and temperature ranges rather than qualitative descriptions of “medium” and “high” temperature and pressure respectively. I would also recommend discussing why the higher heating values are useful – the authors discuss “various applications” but may consider discussing the exact applications (for example in aviation fuels) which may bolster the narrative. In the third paragraph, in which an ML model and AI as a whole are referred to vaguely as “the learning component of a machine” or in comparison to a “brain” may risk unsubstantiated analogies – rather than exhibiting “intelligent behavior” many applications of machine learning in HTL are rather used as fitting tools that can predict metrics that are empirically useful. I would recommend discussing ML and AI in a more objective way. Overall, the description of ML and AI in the third introduction paragraph risk miscommunicating how ML and AI models work and often self contradict – for example, the authors refer to ML as not programs, but in the same sentence, refer to humans programming the ML. This kind of confusion warrants a more mechanistic description of ML and AI. The reviewer appreciates the well cited and discussed summary of ML use in the introduction. However, many of the cited works span a broad range of use cases – for example, HTL pretreatments, yield prediction, and specific feedstock compositions are discussed, but the reviewer worries that this may be casting too wide of a net to focus on the nature of this study. The authors may stylistically disagree as needed. Keyword choice may benefit from some honing – the terms may be too broad to reach the correct audience for what is a relatively niche intersectional field. The reviewer recommends specifying some of the keywords – even including HTL seems like great choice. Overall, while the authors are able to clearly communicate how the work was performed, the results and the specific conclusions to be garnered from each figure could be better communicated. The reviewer struggles to find specific conclusions or a line of narrative information that the figures tie into. This poises the paper as primarily exploratory rather than providing the incisive synthesis of information that may be interpreted from the data in the figures. I highly recommend visualizing some of this useful data in a manner that a reader can derive obvious conclusions the authors wish to communicate (for example for the graph/node plot, greater spacing between the nodes may aid clarity). Similarly, I would recommend poising a stronger line of thought tying each point (i.e. each figure) together. Other Comments: R 2 vs MAE, RMSE, error metrics In paragraph 4 of the introduction, R 2 is used to refer to both accuracy and precision when referring to machine learning models. While R 2 is an empirically useful metric to quantify the correlation between two variables or goodness of fit, it does not describe accuracy or precision. When talking about ML models, metrics that communicate the applied usefulness or “error bounds” of a model may be more useful, and are widely used throughout ML in HTL. Some examples include RMSE (root mean squared error) or MAE (mean absolute error). R 2 can be used to describe the performance of the model (how close to a straight line does the parody plot of the model create) Regarding figure clarity, the reviewer suggests using colors of higher contrast – they are currently set up to be in the form of a heat map, where they are similar to each other but change down the list. This implies a relationship between the different items on the figures that may not exist (and if it does it could be helpful to clarify it!) Use of more distinct colors may rectify this Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: Techno-economics, hydrothermal liquefaction, machine learning, electrochemical engineering, reactor engineering We confirm that we have read this submission and believe that we have an appropriate level of expertise to state that we do not consider it to be of an acceptable scientific standard, for reasons outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Shahabuddin M and Charlebois A. Reviewer Report For: Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :1131 ( https://doi.org/10.5256/f1000research.171837.r341356 ) The direct URL for this report is: https://f1000research.com/articles/13-1131/v1#referee-response-341356 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 17 Dec 2024 Tossapon katongtung , Graduate PhD Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand 17 Dec 2024 Author Response Thank you for your advice, I will revise the manuscript as per the instructions and resubmit it through the system. Competing Interests: No competing interests were disclosed. Thank you for your advice, I will revise the manuscript as per the instructions and resubmit it through the system. Thank you for your advice, I will revise the manuscript as per the instructions and resubmit it through the system. Competing Interests: No competing interests were disclosed. Close Report a concern Author Response 02 Jan 2025 Tossapon katongtung , Graduate PhD Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand 02 Jan 2025 Author Response 1. This paper should do a better job to introduce and motivate the problem/question it is trying to solve. The way the information is presented makes the analysis seem very exploratory ... Continue reading 1. This paper should do a better job to introduce and motivate the problem/question it is trying to solve. The way the information is presented makes the analysis seem very exploratory and not directed. This leads to some of the figures, figure 3 for example, to seem disconnected from the overall story = Thank you for the suggestion; however, we believe Figure 3 remains important as it highlights the most relevant study on this subject and helps readers identify gaps in the research within this field. 2.The work’s motivation reads well and is well cited. I would recommend quantifying the conditions HTL occurs at (i.e. the exact pressures and temperature ranges rather than qualitative descriptions of “medium” and “high” temperature and pressure respectively. I would also recommend discussing why the higher heating values are useful – the authors discuss “various applications” but may consider discussing the exact applications (for example in aviation fuels) which may bolster the narrative. = Revised, as suggested. please refer to lines 57-63 in Introduction section. 3. In the third paragraph, in which an ML model and AI as a whole are referred to vaguely as “the learning component of a machine” or in comparison to a “brain” may risk unsubstantiated analogies – rather than exhibiting “intelligent behavior” many applications of machine learning in HTL are rather used as fitting tools that can predict metrics that are empirically useful. I would recommend discussing ML and AI in a more objective way. Overall, the description of ML and AI in the third introduction paragraph risk miscommunicating how ML and AI models work and often self contradict – for example, the authors refer to ML as not programs, but in the same sentence, refer to humans programming the ML. This kind of confusion warrants a more mechanistic description of ML and AI. = Revised, as suggested. The text that may have caused confusion for readers has been removed. 4. The reviewer appreciates the well cited and discussed summary of ML use in the introduction. However, many of the cited works span a broad range of use cases – for example, HTL pretreatments, yield prediction, and specific feedstock compositions are discussed, but the reviewer worries that this may be casting too wide of a net to focus on the nature of this study. The authors may stylistically disagree as needed. = Based on the explanation provided in the introduction, the authors believe that presenting examples of research in various forms and across a wide range will help readers gain a clearer understanding of the overall scope of ML. Therefore, the author suggests retaining all the original sentences in the manuscript. 5.Keyword choice may benefit from some honing – the terms may be too broad to reach the correct audience for what is a relatively niche intersectional field. The reviewer recommends specifying some of the keywords – even including HTL seems like great choice. = Revised, as suggested. please refer to the red text in the Keywords section. 6. Overall, while the authors are able to clearly communicate how the work was performed, the results and the specific conclusions to be garnered from each figure could be better communicated. The reviewer struggles to find specific conclusions or a line of narrative information that the figures tie into. This poises the paper as primarily exploratory rather than providing the incisive synthesis of information that may be interpreted from the data in the figures. I highly recommend visualizing some of this useful data in a manner that a reader can derive obvious conclusions the authors wish to communicate (for example for the graph/node plot, greater spacing between the nodes may aid clarity). Similarly, I would recommend poising a stronger line of thought tying each point (i.e. each figure) together. = The bibliometric analysis in this study aims to provide an overview and identify trends in related research, serving as a guide for readers pursuing further studies on this topic. The authors believe that the statistical data and related information have been presented comprehensively. 7. In paragraph 4 of the introduction, R 2 is used to refer to both accuracy and precision when referring to machine learning models. While R 2 is an empirically useful metric to quantify the correlation between two variables or goodness of fit, it does not describe accuracy or precision. When talking about ML models, metrics that communicate the applied usefulness or “error bounds” of a model may be more useful, and are widely used throughout ML in HTL. Some examples include RMSE (root mean squared error) or MAE (mean absolute error). R 2 can be used to describe the performance of the model (how close to a straight line does the parody plot of the model create) = Revised, as suggested. please refer to the RED text in lines 88-89, 96 and 113-114. 8. Regarding figure clarity, the reviewer suggests using colors of higher contrast – they are currently set up to be in the form of a heat map, where they are similar to each other but change down the list. This implies a relationship between the different items on the figures that may not exist (and if it does it could be helpful to clarify it!) Use of more distinct colors may rectify this = Thank you for the advice. However, the authors intended for the images in the work to follow the same color theme. Additionally, the colors in the original image were chosen to represent intensity, where stronger colors indicate greater values and lighter colors indicate lower values, similar to contouring. 1. This paper should do a better job to introduce and motivate the problem/question it is trying to solve. The way the information is presented makes the analysis seem very exploratory and not directed. This leads to some of the figures, figure 3 for example, to seem disconnected from the overall story = Thank you for the suggestion; however, we believe Figure 3 remains important as it highlights the most relevant study on this subject and helps readers identify gaps in the research within this field. 2.The work’s motivation reads well and is well cited. I would recommend quantifying the conditions HTL occurs at (i.e. the exact pressures and temperature ranges rather than qualitative descriptions of “medium” and “high” temperature and pressure respectively. I would also recommend discussing why the higher heating values are useful – the authors discuss “various applications” but may consider discussing the exact applications (for example in aviation fuels) which may bolster the narrative. = Revised, as suggested. please refer to lines 57-63 in Introduction section. 3. In the third paragraph, in which an ML model and AI as a whole are referred to vaguely as “the learning component of a machine” or in comparison to a “brain” may risk unsubstantiated analogies – rather than exhibiting “intelligent behavior” many applications of machine learning in HTL are rather used as fitting tools that can predict metrics that are empirically useful. I would recommend discussing ML and AI in a more objective way. Overall, the description of ML and AI in the third introduction paragraph risk miscommunicating how ML and AI models work and often self contradict – for example, the authors refer to ML as not programs, but in the same sentence, refer to humans programming the ML. This kind of confusion warrants a more mechanistic description of ML and AI. = Revised, as suggested. The text that may have caused confusion for readers has been removed. 4. The reviewer appreciates the well cited and discussed summary of ML use in the introduction. However, many of the cited works span a broad range of use cases – for example, HTL pretreatments, yield prediction, and specific feedstock compositions are discussed, but the reviewer worries that this may be casting too wide of a net to focus on the nature of this study. The authors may stylistically disagree as needed. = Based on the explanation provided in the introduction, the authors believe that presenting examples of research in various forms and across a wide range will help readers gain a clearer understanding of the overall scope of ML. Therefore, the author suggests retaining all the original sentences in the manuscript. 5.Keyword choice may benefit from some honing – the terms may be too broad to reach the correct audience for what is a relatively niche intersectional field. The reviewer recommends specifying some of the keywords – even including HTL seems like great choice. = Revised, as suggested. please refer to the red text in the Keywords section. 6. Overall, while the authors are able to clearly communicate how the work was performed, the results and the specific conclusions to be garnered from each figure could be better communicated. The reviewer struggles to find specific conclusions or a line of narrative information that the figures tie into. This poises the paper as primarily exploratory rather than providing the incisive synthesis of information that may be interpreted from the data in the figures. I highly recommend visualizing some of this useful data in a manner that a reader can derive obvious conclusions the authors wish to communicate (for example for the graph/node plot, greater spacing between the nodes may aid clarity). Similarly, I would recommend poising a stronger line of thought tying each point (i.e. each figure) together. = The bibliometric analysis in this study aims to provide an overview and identify trends in related research, serving as a guide for readers pursuing further studies on this topic. The authors believe that the statistical data and related information have been presented comprehensively. 7. In paragraph 4 of the introduction, R 2 is used to refer to both accuracy and precision when referring to machine learning models. While R 2 is an empirically useful metric to quantify the correlation between two variables or goodness of fit, it does not describe accuracy or precision. When talking about ML models, metrics that communicate the applied usefulness or “error bounds” of a model may be more useful, and are widely used throughout ML in HTL. Some examples include RMSE (root mean squared error) or MAE (mean absolute error). R 2 can be used to describe the performance of the model (how close to a straight line does the parody plot of the model create) = Revised, as suggested. please refer to the RED text in lines 88-89, 96 and 113-114. 8. Regarding figure clarity, the reviewer suggests using colors of higher contrast – they are currently set up to be in the form of a heat map, where they are similar to each other but change down the list. This implies a relationship between the different items on the figures that may not exist (and if it does it could be helpful to clarify it!) Use of more distinct colors may rectify this = Thank you for the advice. However, the authors intended for the images in the work to follow the same color theme. Additionally, the colors in the original image were chosen to represent intensity, where stronger colors indicate greater values and lighter colors indicate lower values, similar to contouring. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 17 Dec 2024 Tossapon katongtung , Graduate PhD Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand 17 Dec 2024 Author Response Thank you for your advice, I will revise the manuscript as per the instructions and resubmit it through the system. Competing Interests: No competing interests were disclosed. Thank you for your advice, I will revise the manuscript as per the instructions and resubmit it through the system. Thank you for your advice, I will revise the manuscript as per the instructions and resubmit it through the system. Competing Interests: No competing interests were disclosed. Close Report a concern Author Response 02 Jan 2025 Tossapon katongtung , Graduate PhD Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand 02 Jan 2025 Author Response 1. This paper should do a better job to introduce and motivate the problem/question it is trying to solve. The way the information is presented makes the analysis seem very exploratory ... Continue reading 1. This paper should do a better job to introduce and motivate the problem/question it is trying to solve. The way the information is presented makes the analysis seem very exploratory and not directed. This leads to some of the figures, figure 3 for example, to seem disconnected from the overall story = Thank you for the suggestion; however, we believe Figure 3 remains important as it highlights the most relevant study on this subject and helps readers identify gaps in the research within this field. 2.The work’s motivation reads well and is well cited. I would recommend quantifying the conditions HTL occurs at (i.e. the exact pressures and temperature ranges rather than qualitative descriptions of “medium” and “high” temperature and pressure respectively. I would also recommend discussing why the higher heating values are useful – the authors discuss “various applications” but may consider discussing the exact applications (for example in aviation fuels) which may bolster the narrative. = Revised, as suggested. please refer to lines 57-63 in Introduction section. 3. In the third paragraph, in which an ML model and AI as a whole are referred to vaguely as “the learning component of a machine” or in comparison to a “brain” may risk unsubstantiated analogies – rather than exhibiting “intelligent behavior” many applications of machine learning in HTL are rather used as fitting tools that can predict metrics that are empirically useful. I would recommend discussing ML and AI in a more objective way. Overall, the description of ML and AI in the third introduction paragraph risk miscommunicating how ML and AI models work and often self contradict – for example, the authors refer to ML as not programs, but in the same sentence, refer to humans programming the ML. This kind of confusion warrants a more mechanistic description of ML and AI. = Revised, as suggested. The text that may have caused confusion for readers has been removed. 4. The reviewer appreciates the well cited and discussed summary of ML use in the introduction. However, many of the cited works span a broad range of use cases – for example, HTL pretreatments, yield prediction, and specific feedstock compositions are discussed, but the reviewer worries that this may be casting too wide of a net to focus on the nature of this study. The authors may stylistically disagree as needed. = Based on the explanation provided in the introduction, the authors believe that presenting examples of research in various forms and across a wide range will help readers gain a clearer understanding of the overall scope of ML. Therefore, the author suggests retaining all the original sentences in the manuscript. 5.Keyword choice may benefit from some honing – the terms may be too broad to reach the correct audience for what is a relatively niche intersectional field. The reviewer recommends specifying some of the keywords – even including HTL seems like great choice. = Revised, as suggested. please refer to the red text in the Keywords section. 6. Overall, while the authors are able to clearly communicate how the work was performed, the results and the specific conclusions to be garnered from each figure could be better communicated. The reviewer struggles to find specific conclusions or a line of narrative information that the figures tie into. This poises the paper as primarily exploratory rather than providing the incisive synthesis of information that may be interpreted from the data in the figures. I highly recommend visualizing some of this useful data in a manner that a reader can derive obvious conclusions the authors wish to communicate (for example for the graph/node plot, greater spacing between the nodes may aid clarity). Similarly, I would recommend poising a stronger line of thought tying each point (i.e. each figure) together. = The bibliometric analysis in this study aims to provide an overview and identify trends in related research, serving as a guide for readers pursuing further studies on this topic. The authors believe that the statistical data and related information have been presented comprehensively. 7. In paragraph 4 of the introduction, R 2 is used to refer to both accuracy and precision when referring to machine learning models. While R 2 is an empirically useful metric to quantify the correlation between two variables or goodness of fit, it does not describe accuracy or precision. When talking about ML models, metrics that communicate the applied usefulness or “error bounds” of a model may be more useful, and are widely used throughout ML in HTL. Some examples include RMSE (root mean squared error) or MAE (mean absolute error). R 2 can be used to describe the performance of the model (how close to a straight line does the parody plot of the model create) = Revised, as suggested. please refer to the RED text in lines 88-89, 96 and 113-114. 8. Regarding figure clarity, the reviewer suggests using colors of higher contrast – they are currently set up to be in the form of a heat map, where they are similar to each other but change down the list. This implies a relationship between the different items on the figures that may not exist (and if it does it could be helpful to clarify it!) Use of more distinct colors may rectify this = Thank you for the advice. However, the authors intended for the images in the work to follow the same color theme. Additionally, the colors in the original image were chosen to represent intensity, where stronger colors indicate greater values and lighter colors indicate lower values, similar to contouring. 1. This paper should do a better job to introduce and motivate the problem/question it is trying to solve. The way the information is presented makes the analysis seem very exploratory and not directed. This leads to some of the figures, figure 3 for example, to seem disconnected from the overall story = Thank you for the suggestion; however, we believe Figure 3 remains important as it highlights the most relevant study on this subject and helps readers identify gaps in the research within this field. 2.The work’s motivation reads well and is well cited. I would recommend quantifying the conditions HTL occurs at (i.e. the exact pressures and temperature ranges rather than qualitative descriptions of “medium” and “high” temperature and pressure respectively. I would also recommend discussing why the higher heating values are useful – the authors discuss “various applications” but may consider discussing the exact applications (for example in aviation fuels) which may bolster the narrative. = Revised, as suggested. please refer to lines 57-63 in Introduction section. 3. In the third paragraph, in which an ML model and AI as a whole are referred to vaguely as “the learning component of a machine” or in comparison to a “brain” may risk unsubstantiated analogies – rather than exhibiting “intelligent behavior” many applications of machine learning in HTL are rather used as fitting tools that can predict metrics that are empirically useful. I would recommend discussing ML and AI in a more objective way. Overall, the description of ML and AI in the third introduction paragraph risk miscommunicating how ML and AI models work and often self contradict – for example, the authors refer to ML as not programs, but in the same sentence, refer to humans programming the ML. This kind of confusion warrants a more mechanistic description of ML and AI. = Revised, as suggested. The text that may have caused confusion for readers has been removed. 4. The reviewer appreciates the well cited and discussed summary of ML use in the introduction. However, many of the cited works span a broad range of use cases – for example, HTL pretreatments, yield prediction, and specific feedstock compositions are discussed, but the reviewer worries that this may be casting too wide of a net to focus on the nature of this study. The authors may stylistically disagree as needed. = Based on the explanation provided in the introduction, the authors believe that presenting examples of research in various forms and across a wide range will help readers gain a clearer understanding of the overall scope of ML. Therefore, the author suggests retaining all the original sentences in the manuscript. 5.Keyword choice may benefit from some honing – the terms may be too broad to reach the correct audience for what is a relatively niche intersectional field. The reviewer recommends specifying some of the keywords – even including HTL seems like great choice. = Revised, as suggested. please refer to the red text in the Keywords section. 6. Overall, while the authors are able to clearly communicate how the work was performed, the results and the specific conclusions to be garnered from each figure could be better communicated. The reviewer struggles to find specific conclusions or a line of narrative information that the figures tie into. This poises the paper as primarily exploratory rather than providing the incisive synthesis of information that may be interpreted from the data in the figures. I highly recommend visualizing some of this useful data in a manner that a reader can derive obvious conclusions the authors wish to communicate (for example for the graph/node plot, greater spacing between the nodes may aid clarity). Similarly, I would recommend poising a stronger line of thought tying each point (i.e. each figure) together. = The bibliometric analysis in this study aims to provide an overview and identify trends in related research, serving as a guide for readers pursuing further studies on this topic. The authors believe that the statistical data and related information have been presented comprehensively. 7. In paragraph 4 of the introduction, R 2 is used to refer to both accuracy and precision when referring to machine learning models. While R 2 is an empirically useful metric to quantify the correlation between two variables or goodness of fit, it does not describe accuracy or precision. When talking about ML models, metrics that communicate the applied usefulness or “error bounds” of a model may be more useful, and are widely used throughout ML in HTL. Some examples include RMSE (root mean squared error) or MAE (mean absolute error). R 2 can be used to describe the performance of the model (how close to a straight line does the parody plot of the model create) = Revised, as suggested. please refer to the RED text in lines 88-89, 96 and 113-114. 8. Regarding figure clarity, the reviewer suggests using colors of higher contrast – they are currently set up to be in the form of a heat map, where they are similar to each other but change down the list. This implies a relationship between the different items on the figures that may not exist (and if it does it could be helpful to clarify it!) Use of more distinct colors may rectify this = Thank you for the advice. However, the authors intended for the images in the work to follow the same color theme. Additionally, the colors in the original image were chosen to represent intensity, where stronger colors indicate greater values and lighter colors indicate lower values, similar to contouring. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Views 0 Cite How to cite this report: Qian L. Reviewer Report For: Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :1131 ( https://doi.org/10.5256/f1000research.171837.r330069 ) The direct URL for this report is: https://f1000research.com/articles/13-1131/v1#referee-response-330069 NOTE: it is important to ensure the information in square brackets after the title is included in this citation. Close Copy Citation Details Reviewer Report 24 Oct 2024 Lili Qian , Jiangsu University, Zhenjiang, Jiangsu, China Approved with Reservations VIEWS 0 https://doi.org/10.5256/f1000research.171837.r330069 This manuscript performed bibliometric analysis of machine learning applications to hydrothermal liquefaction. However, similar studies have been reported and the following issues should be addressed. A major revision is needed before the indexing. 1 Background in abstract: It is ... Continue reading READ ALL This manuscript performed bibliometric analysis of machine learning applications to hydrothermal liquefaction. However, similar studies have been reported and the following issues should be addressed. A major revision is needed before the indexing. 1 Background in abstract: It is important to emphasize HTL's focus on wet biomass. 2 Methods in abstract: machine learning should be introduced. 3 Second paragraph in introduction: Machine learning has emerged as the most popular technology to address such difficult and complex problems efficiently. What specifically do “such difficult and complex problems” refer to? 4 Fourth paragraph in introduction: There is too much information about ionic liquids. 5 Fifth paragraph in introduction: The application of the bibliometric method in the HTL field has not been introduced, nor has its application in the ML field. 6 “Based on the available literature review, this study represents the first bibliographic analysis conducted to evaluate research trends specifically in the development of ML within the context of HTL.”. It is not the first bibliographic study on ML-based HTL. similar studies have been reported. What is the novelty of the manuscript? 7 Methods: The first paragraph in methods is the introduction of bibliometrics. Move it to the introduction part. 8 Figure 8: why “article” is a keyword? 9 No reference in this year. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests: No competing interests were disclosed. Reviewer Expertise: My research focused on hydrothermal liquefaction and machine learning. We submitted a similar paper titled "A Review on Machine Learning-Aided Hydrothermal Liquefaction Based on Bibliometric Analysis" on August this year. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. Close READ LESS CITE CITE HOW TO CITE THIS REPORT Qian L. Reviewer Report For: Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :1131 ( https://doi.org/10.5256/f1000research.171837.r330069 ) The direct URL for this report is: https://f1000research.com/articles/13-1131/v1#referee-response-330069 NOTE: it is important to ensure the information in square brackets after the title is included in all citations of this article. COPY CITATION DETAILS Report a concern Author Response 26 Oct 2024 Tossapon katongtung , Graduate PhD Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand 26 Oct 2024 Author Response Thank you for your advice. I can answer the following questions you raised. 1.Background in abstract: It is important to emphasize HTL's focus on wet biomass. Response: Revised, as suggested. ... Continue reading Thank you for your advice. I can answer the following questions you raised. 1.Background in abstract: It is important to emphasize HTL's focus on wet biomass. Response: Revised, as suggested. 2.Methods in abstract: machine learning should be introduced. Response: Revised, as suggested. 3 Second paragraph in introduction: Machine learning has emerged as the most popular technology to address such difficult and complex problems efficiently. What specifically do “such difficult and complex problems” refer to? Response: This sentence refers to applying ML to processes that are difficult to explain, in this case HTL processes, as in the paragraph, and I believe the following sentence answers this question. “It offers promising avenues for understanding and optimizing HTL processes through data-driven analysis and predictive modeling.” If the reader wishes to elaborate on this, I have added some sentences to the manuscript. 4 Fourth paragraph in introduction: There is too much information about ionic liquids. Response: Revised, as suggested. 5 Fifth paragraph in introduction: The application of the bibliometric method in the HTL field has not been introduced, nor has its application in the ML field. Response: In this sentence, I am confident that I wrote it correctly at that time, because I had been studying this issue since June 2023 and had tried to submit it to a journal for publication but did not receive a response, so I withdrew it and submitted it to F1000research in September 2024. I have kept all my submissions from June 2023. If you want to see them, I can send them to you. 6 “Based on the available literature review, this study represents the first bibliographic analysis conducted to evaluate research trends specifically in the development of ML within the context of HTL.”. It is not the first bibliographic study on ML-based HTL. similar studies have been reported. What is the novelty of the manuscript? Response: I think we have similar ideas on how to work on this, but I must say that at the time I wrote this study, there was no research on this subject before. In this sentence, I am confident that I wrote it correctly at that time, because I had been studying this issue since June 2023 and had tried to submit it to a journal for publication but did not receive a response, so I withdrew it and submitted it to F1000research in September 2024. I have kept all my submissions from June 2023. If you want to see them, I can send them to you. I have submitted Case Studies in Chemical and Environmental Engineering Manuscript Number: CSCEE-D-23-00310 on June 30, 2023. 7 Methods: The first paragraph in methods is the introduction of bibliometrics. Move it to the introduction part. Response: I think this sentence should remain in its original place and should not be changed. 8 Figure 8: why “article” is a keyword? Response: I collected data from Scopus database and did not add any data to the dataset. 9 No reference in this year. Response: I did this study before 2024 and I gave a clear timeline in the manuscript. Thank you for your advice. I can answer the following questions you raised. 1.Background in abstract: It is important to emphasize HTL's focus on wet biomass. Response: Revised, as suggested. 2.Methods in abstract: machine learning should be introduced. Response: Revised, as suggested. 3 Second paragraph in introduction: Machine learning has emerged as the most popular technology to address such difficult and complex problems efficiently. What specifically do “such difficult and complex problems” refer to? Response: This sentence refers to applying ML to processes that are difficult to explain, in this case HTL processes, as in the paragraph, and I believe the following sentence answers this question. “It offers promising avenues for understanding and optimizing HTL processes through data-driven analysis and predictive modeling.” If the reader wishes to elaborate on this, I have added some sentences to the manuscript. 4 Fourth paragraph in introduction: There is too much information about ionic liquids. Response: Revised, as suggested. 5 Fifth paragraph in introduction: The application of the bibliometric method in the HTL field has not been introduced, nor has its application in the ML field. Response: In this sentence, I am confident that I wrote it correctly at that time, because I had been studying this issue since June 2023 and had tried to submit it to a journal for publication but did not receive a response, so I withdrew it and submitted it to F1000research in September 2024. I have kept all my submissions from June 2023. If you want to see them, I can send them to you. 6 “Based on the available literature review, this study represents the first bibliographic analysis conducted to evaluate research trends specifically in the development of ML within the context of HTL.”. It is not the first bibliographic study on ML-based HTL. similar studies have been reported. What is the novelty of the manuscript? Response: I think we have similar ideas on how to work on this, but I must say that at the time I wrote this study, there was no research on this subject before. In this sentence, I am confident that I wrote it correctly at that time, because I had been studying this issue since June 2023 and had tried to submit it to a journal for publication but did not receive a response, so I withdrew it and submitted it to F1000research in September 2024. I have kept all my submissions from June 2023. If you want to see them, I can send them to you. I have submitted Case Studies in Chemical and Environmental Engineering Manuscript Number: CSCEE-D-23-00310 on June 30, 2023. 7 Methods: The first paragraph in methods is the introduction of bibliometrics. Move it to the introduction part. Response: I think this sentence should remain in its original place and should not be changed. 8 Figure 8: why “article” is a keyword? Response: I collected data from Scopus database and did not add any data to the dataset. 9 No reference in this year. Response: I did this study before 2024 and I gave a clear timeline in the manuscript. Competing Interests: No competing interests were disclosed. Close Report a concern Respond or Comment COMMENTS ON THIS REPORT Author Response 26 Oct 2024 Tossapon katongtung , Graduate PhD Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand 26 Oct 2024 Author Response Thank you for your advice. I can answer the following questions you raised. 1.Background in abstract: It is important to emphasize HTL's focus on wet biomass. Response: Revised, as suggested. ... Continue reading Thank you for your advice. I can answer the following questions you raised. 1.Background in abstract: It is important to emphasize HTL's focus on wet biomass. Response: Revised, as suggested. 2.Methods in abstract: machine learning should be introduced. Response: Revised, as suggested. 3 Second paragraph in introduction: Machine learning has emerged as the most popular technology to address such difficult and complex problems efficiently. What specifically do “such difficult and complex problems” refer to? Response: This sentence refers to applying ML to processes that are difficult to explain, in this case HTL processes, as in the paragraph, and I believe the following sentence answers this question. “It offers promising avenues for understanding and optimizing HTL processes through data-driven analysis and predictive modeling.” If the reader wishes to elaborate on this, I have added some sentences to the manuscript. 4 Fourth paragraph in introduction: There is too much information about ionic liquids. Response: Revised, as suggested. 5 Fifth paragraph in introduction: The application of the bibliometric method in the HTL field has not been introduced, nor has its application in the ML field. Response: In this sentence, I am confident that I wrote it correctly at that time, because I had been studying this issue since June 2023 and had tried to submit it to a journal for publication but did not receive a response, so I withdrew it and submitted it to F1000research in September 2024. I have kept all my submissions from June 2023. If you want to see them, I can send them to you. 6 “Based on the available literature review, this study represents the first bibliographic analysis conducted to evaluate research trends specifically in the development of ML within the context of HTL.”. It is not the first bibliographic study on ML-based HTL. similar studies have been reported. What is the novelty of the manuscript? Response: I think we have similar ideas on how to work on this, but I must say that at the time I wrote this study, there was no research on this subject before. In this sentence, I am confident that I wrote it correctly at that time, because I had been studying this issue since June 2023 and had tried to submit it to a journal for publication but did not receive a response, so I withdrew it and submitted it to F1000research in September 2024. I have kept all my submissions from June 2023. If you want to see them, I can send them to you. I have submitted Case Studies in Chemical and Environmental Engineering Manuscript Number: CSCEE-D-23-00310 on June 30, 2023. 7 Methods: The first paragraph in methods is the introduction of bibliometrics. Move it to the introduction part. Response: I think this sentence should remain in its original place and should not be changed. 8 Figure 8: why “article” is a keyword? Response: I collected data from Scopus database and did not add any data to the dataset. 9 No reference in this year. Response: I did this study before 2024 and I gave a clear timeline in the manuscript. Thank you for your advice. I can answer the following questions you raised. 1.Background in abstract: It is important to emphasize HTL's focus on wet biomass. Response: Revised, as suggested. 2.Methods in abstract: machine learning should be introduced. Response: Revised, as suggested. 3 Second paragraph in introduction: Machine learning has emerged as the most popular technology to address such difficult and complex problems efficiently. What specifically do “such difficult and complex problems” refer to? Response: This sentence refers to applying ML to processes that are difficult to explain, in this case HTL processes, as in the paragraph, and I believe the following sentence answers this question. “It offers promising avenues for understanding and optimizing HTL processes through data-driven analysis and predictive modeling.” If the reader wishes to elaborate on this, I have added some sentences to the manuscript. 4 Fourth paragraph in introduction: There is too much information about ionic liquids. Response: Revised, as suggested. 5 Fifth paragraph in introduction: The application of the bibliometric method in the HTL field has not been introduced, nor has its application in the ML field. Response: In this sentence, I am confident that I wrote it correctly at that time, because I had been studying this issue since June 2023 and had tried to submit it to a journal for publication but did not receive a response, so I withdrew it and submitted it to F1000research in September 2024. I have kept all my submissions from June 2023. If you want to see them, I can send them to you. 6 “Based on the available literature review, this study represents the first bibliographic analysis conducted to evaluate research trends specifically in the development of ML within the context of HTL.”. It is not the first bibliographic study on ML-based HTL. similar studies have been reported. What is the novelty of the manuscript? Response: I think we have similar ideas on how to work on this, but I must say that at the time I wrote this study, there was no research on this subject before. In this sentence, I am confident that I wrote it correctly at that time, because I had been studying this issue since June 2023 and had tried to submit it to a journal for publication but did not receive a response, so I withdrew it and submitted it to F1000research in September 2024. I have kept all my submissions from June 2023. If you want to see them, I can send them to you. I have submitted Case Studies in Chemical and Environmental Engineering Manuscript Number: CSCEE-D-23-00310 on June 30, 2023. 7 Methods: The first paragraph in methods is the introduction of bibliometrics. Move it to the introduction part. Response: I think this sentence should remain in its original place and should not be changed. 8 Figure 8: why “article” is a keyword? Response: I collected data from Scopus database and did not add any data to the dataset. 9 No reference in this year. Response: I did this study before 2024 and I gave a clear timeline in the manuscript. Competing Interests: No competing interests were disclosed. Close Report a concern COMMENT ON THIS REPORT Comments on this article Comments (0) Version 3 VERSION 3 PUBLISHED 04 Oct 2024 ADD YOUR COMMENT Comment keyboard_arrow_left keyboard_arrow_right Open Peer Review Reviewer Status info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Reviewer Reports Invited Reviewers 1 2 3 4 Version 3 (revision) 11 Mar 25 read Version 2 (revision) 02 Jan 25 read read Version 1 04 Oct 24 read read Lili Qian , Jiangsu University, Zhenjiang, China Muntasir Shahabuddin , Worcester Polytechnic Institute, Worcester, USA Andrew Charlebois , Worcester Polytechnic Institute, Worcester, USA Venu Babu Borugadda , University of Saskatchewan, Saskatoon, Canada Muhammad Raziq Rahimi Kooh , Universiti Brunei Darussalam, Darussalam, Brunei Comments on this article All Comments (0) Add a comment Sign up for content alerts Sign Up You are now signed up to receive this alert Browse by related subjects keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Kooh M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 07 May 2025 | for Version 3 Muhammad Raziq Rahimi Kooh , Universiti Brunei Darussalam, Darussalam, Brunei 0 Views copyright © 2025 Kooh M. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This work focuses on the bibliographic evaluation of hydrothermal liquefaction using Scopus data. Please see the specific comment below for details. Minor error (A) ensure all the acronyms are defined on their first appearance. (B) Ensure all the symbols, such as R2 are formatted correctly. Introduction. The section related to ionic liquids does not seem relevant. The table description and figure’s caption should be informative. The lack of discussion is a major concern. While specific countries, journals or authors may have the most HTL publications, what insights can you draw from these? What does all this data mean? Apart from ML, which is a current trend for all domains of science, are there any other specific insights that are obtained from the bibliographic analysis? Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? No Competing Interests No competing interests were disclosed. Reviewer Expertise Machine learning I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (0) Kooh MRR. Peer Review Report For: Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :1131 ( https://doi.org/10.5256/f1000research.177841.r376870) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1131/v3#referee-response-376870 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Qian L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 09 Jan 2025 | for Version 2 Lili Qian , Jiangsu University, Zhenjiang, Jiangsu, China 0 Views copyright © 2025 Qian L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (0) Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This manuscript can be accepted. Competing Interests No competing interests were disclosed. Reviewer Expertise My research focused on hydrothermal liquefaction and machine learning. We submitted a similar paper titled "A Review on Machine Learning-Aided Hydrothermal Liquefaction Based on Bibliometric Analysis" on August this year. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard. reply Respond to this report Responses (0) Qian L. Peer Review Report For: Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :1131 ( https://doi.org/10.5256/f1000research.176096.r355299) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1131/v2#referee-response-355299 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2025 Borugadda V. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 03 Jan 2025 | for Version 2 Venu Babu Borugadda , University of Saskatchewan, Saskatoon, Canada 0 Views copyright © 2025 Borugadda V. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (2) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions 1. Are there a similar papers in the literature with bibliometric analysis ? Please report some with references and explain how these papers are contributing for the scientific/research growth ? 2. What is the take home message from this bibliometric analysis on HTL and ML? 3. What are the key challenges of this type of work ? 4. Besides scopus what are the other potential search engines to make bibliometric analysis ? 5. Please mention the tables and figures after they are referred to in the text. Many tables and figures are reported before they are being referred. 6. Fig 3, how ML and HTL are related to the Computer Science, Earth and planetary, Medicine and biochemistry? Please justify or remove them using more filters. 7. Fig.4, please mention the references of top 10 authors in the reference section. 8. Page 8 last paragraph, first sentence, it's figure 6, not 3. 9. Fig.8, why human, soil liquefaction, male keywords are considered ? Is the work clearly and accurately presented and does it cite the current literature? Yes Is the study design appropriate and is the work technically sound? Yes Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Partly Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Methodology and key question raised in comments 1-5 I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (2) Author Response 05 Feb 2025 Tossapon katongtung, Graduate PhD Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand Revised, as suggested. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Author Response 11 Mar 2025 Tossapon katongtung, Graduate PhD Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand 1.Revised, as suggested. please refer to the Introduction section.Revised, as suggested. please refer to the Introduction section. 2.As the research on HTL-based ML development is still in its early stages and relatively limited at present, this study fills an important knowledge gap in the field. The outcomes of this analysis will contribute significantly to the formulation of a comprehensive research plan by identifying potential future research directions and fostering collaborative relationships among researchers in this emerging field. 3.The limitations of this type of research include the validity of the data and the selection of relevant data for presentation. 4.The Web of Science, Scopus, Dimensions, and PubMed databases can be used to perform bibliometric analyses using VOSviewer. 5.Revised, as suggested. 6.This information was obtained from the Scopus database and has not been manipulated, as the dataset was preserved in its original form. 7.It is not feasible to cite all of the publications in this ranking, as the list includes a substantial number of entries—for example, the top ranking alone comprises 20 publications on the topic. Moreover, citations are unnecessary here because the discussion is based on quantitative variables 8.Revised, as suggested. 9.This information was obtained from the Scopus database and has not been manipulated, as the dataset was preserved in its original form. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Borugadda VB. Peer Review Report For: Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :1131 ( https://doi.org/10.5256/f1000research.176096.r341351) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1131/v2#referee-response-341351 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Shahabuddin M et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 16 Dec 2024 | for Version 1 Muntasir Shahabuddin , Chemical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA Andrew Charlebois , Chemical Engineering, Worcester Polytechnic Institute, Worcester, MA, USA 0 Views copyright © 2024 Shahabuddin M et al. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (2) Not Approved info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions Manuscript Title: Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis Overview: The reviewer would like to thank the editors of F1000 for the opportunity to provide feedback on the manuscript “Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis”. This study by Katongtung et al. leverages bibliometric analysis to collect and analyze publications relating to machine learning on hydrothermal liquefaction systems in the corpus of existing literature. Using a Scopus search, the authors uncovered the most productive authors in the field, and geopolitical regions with the greatest academic output. While the conclusions of this study (in which Chinese and US scientists tend to be the most productive in this area from a raw studies-published perspective) are useful, the manuscript may benefit from reformatting of figures for better clarity of the data being communicated, and for reconsideration of how each figure contributes to the overall narrative conclusion of the work. In light of the following comments, the reviewer recommends that this work is revised and resubmitted after major revisions, or reconsidered for indexing at another time. Major Comments: This paper should do a better job to introduce and motivate the problem/question it is trying to solve. The way the information is presented makes the analysis seem very exploratory and not directed. This leads to some of the figures, figure 3 for example, to seem disconnected from the overall story The work’s motivation reads well and is well cited. I would recommend quantifying the conditions HTL occurs at (i.e. the exact pressures and temperature ranges rather than qualitative descriptions of “medium” and “high” temperature and pressure respectively. I would also recommend discussing why the higher heating values are useful – the authors discuss “various applications” but may consider discussing the exact applications (for example in aviation fuels) which may bolster the narrative. In the third paragraph, in which an ML model and AI as a whole are referred to vaguely as “the learning component of a machine” or in comparison to a “brain” may risk unsubstantiated analogies – rather than exhibiting “intelligent behavior” many applications of machine learning in HTL are rather used as fitting tools that can predict metrics that are empirically useful. I would recommend discussing ML and AI in a more objective way. Overall, the description of ML and AI in the third introduction paragraph risk miscommunicating how ML and AI models work and often self contradict – for example, the authors refer to ML as not programs, but in the same sentence, refer to humans programming the ML. This kind of confusion warrants a more mechanistic description of ML and AI. The reviewer appreciates the well cited and discussed summary of ML use in the introduction. However, many of the cited works span a broad range of use cases – for example, HTL pretreatments, yield prediction, and specific feedstock compositions are discussed, but the reviewer worries that this may be casting too wide of a net to focus on the nature of this study. The authors may stylistically disagree as needed. Keyword choice may benefit from some honing – the terms may be too broad to reach the correct audience for what is a relatively niche intersectional field. The reviewer recommends specifying some of the keywords – even including HTL seems like great choice. Overall, while the authors are able to clearly communicate how the work was performed, the results and the specific conclusions to be garnered from each figure could be better communicated. The reviewer struggles to find specific conclusions or a line of narrative information that the figures tie into. This poises the paper as primarily exploratory rather than providing the incisive synthesis of information that may be interpreted from the data in the figures. I highly recommend visualizing some of this useful data in a manner that a reader can derive obvious conclusions the authors wish to communicate (for example for the graph/node plot, greater spacing between the nodes may aid clarity). Similarly, I would recommend poising a stronger line of thought tying each point (i.e. each figure) together. Other Comments: R 2 vs MAE, RMSE, error metrics In paragraph 4 of the introduction, R 2 is used to refer to both accuracy and precision when referring to machine learning models. While R 2 is an empirically useful metric to quantify the correlation between two variables or goodness of fit, it does not describe accuracy or precision. When talking about ML models, metrics that communicate the applied usefulness or “error bounds” of a model may be more useful, and are widely used throughout ML in HTL. Some examples include RMSE (root mean squared error) or MAE (mean absolute error). R 2 can be used to describe the performance of the model (how close to a straight line does the parody plot of the model create) Regarding figure clarity, the reviewer suggests using colors of higher contrast – they are currently set up to be in the form of a heat map, where they are similar to each other but change down the list. This implies a relationship between the different items on the figures that may not exist (and if it does it could be helpful to clarify it!) Use of more distinct colors may rectify this Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? No Are sufficient details of methods and analysis provided to allow replication by others? Yes If applicable, is the statistical analysis and its interpretation appropriate? Not applicable Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise Techno-economics, hydrothermal liquefaction, machine learning, electrochemical engineering, reactor engineering We confirm that we have read this submission and believe that we have an appropriate level of expertise to state that we do not consider it to be of an acceptable scientific standard, for reasons outlined above. reply Respond to this report Responses (2) Author Response 17 Dec 2024 Tossapon katongtung, Graduate PhD Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand Thank you for your advice, I will revise the manuscript as per the instructions and resubmit it through the system. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Author Response 02 Jan 2025 Tossapon katongtung, Graduate PhD Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand 1. This paper should do a better job to introduce and motivate the problem/question it is trying to solve. The way the information is presented makes the analysis seem very exploratory and not directed. This leads to some of the figures, figure 3 for example, to seem disconnected from the overall story = Thank you for the suggestion; however, we believe Figure 3 remains important as it highlights the most relevant study on this subject and helps readers identify gaps in the research within this field. 2.The work’s motivation reads well and is well cited. I would recommend quantifying the conditions HTL occurs at (i.e. the exact pressures and temperature ranges rather than qualitative descriptions of “medium” and “high” temperature and pressure respectively. I would also recommend discussing why the higher heating values are useful – the authors discuss “various applications” but may consider discussing the exact applications (for example in aviation fuels) which may bolster the narrative. = Revised, as suggested. please refer to lines 57-63 in Introduction section. 3. In the third paragraph, in which an ML model and AI as a whole are referred to vaguely as “the learning component of a machine” or in comparison to a “brain” may risk unsubstantiated analogies – rather than exhibiting “intelligent behavior” many applications of machine learning in HTL are rather used as fitting tools that can predict metrics that are empirically useful. I would recommend discussing ML and AI in a more objective way. Overall, the description of ML and AI in the third introduction paragraph risk miscommunicating how ML and AI models work and often self contradict – for example, the authors refer to ML as not programs, but in the same sentence, refer to humans programming the ML. This kind of confusion warrants a more mechanistic description of ML and AI. = Revised, as suggested. The text that may have caused confusion for readers has been removed. 4. The reviewer appreciates the well cited and discussed summary of ML use in the introduction. However, many of the cited works span a broad range of use cases – for example, HTL pretreatments, yield prediction, and specific feedstock compositions are discussed, but the reviewer worries that this may be casting too wide of a net to focus on the nature of this study. The authors may stylistically disagree as needed. = Based on the explanation provided in the introduction, the authors believe that presenting examples of research in various forms and across a wide range will help readers gain a clearer understanding of the overall scope of ML. Therefore, the author suggests retaining all the original sentences in the manuscript. 5.Keyword choice may benefit from some honing – the terms may be too broad to reach the correct audience for what is a relatively niche intersectional field. The reviewer recommends specifying some of the keywords – even including HTL seems like great choice. = Revised, as suggested. please refer to the red text in the Keywords section. 6. Overall, while the authors are able to clearly communicate how the work was performed, the results and the specific conclusions to be garnered from each figure could be better communicated. The reviewer struggles to find specific conclusions or a line of narrative information that the figures tie into. This poises the paper as primarily exploratory rather than providing the incisive synthesis of information that may be interpreted from the data in the figures. I highly recommend visualizing some of this useful data in a manner that a reader can derive obvious conclusions the authors wish to communicate (for example for the graph/node plot, greater spacing between the nodes may aid clarity). Similarly, I would recommend poising a stronger line of thought tying each point (i.e. each figure) together. = The bibliometric analysis in this study aims to provide an overview and identify trends in related research, serving as a guide for readers pursuing further studies on this topic. The authors believe that the statistical data and related information have been presented comprehensively. 7. In paragraph 4 of the introduction, R 2 is used to refer to both accuracy and precision when referring to machine learning models. While R 2 is an empirically useful metric to quantify the correlation between two variables or goodness of fit, it does not describe accuracy or precision. When talking about ML models, metrics that communicate the applied usefulness or “error bounds” of a model may be more useful, and are widely used throughout ML in HTL. Some examples include RMSE (root mean squared error) or MAE (mean absolute error). R 2 can be used to describe the performance of the model (how close to a straight line does the parody plot of the model create) = Revised, as suggested. please refer to the RED text in lines 88-89, 96 and 113-114. 8. Regarding figure clarity, the reviewer suggests using colors of higher contrast – they are currently set up to be in the form of a heat map, where they are similar to each other but change down the list. This implies a relationship between the different items on the figures that may not exist (and if it does it could be helpful to clarify it!) Use of more distinct colors may rectify this = Thank you for the advice. However, the authors intended for the images in the work to follow the same color theme. Additionally, the colors in the original image were chosen to represent intensity, where stronger colors indicate greater values and lighter colors indicate lower values, similar to contouring. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Shahabuddin M and Charlebois A. Peer Review Report For: Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :1131 ( https://doi.org/10.5256/f1000research.171837.r341356) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1131/v1#referee-response-341356 keyboard_arrow_left Back to all reports Reviewer Report 0 Views copyright © 2024 Qian L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. 24 Oct 2024 | for Version 1 Lili Qian , Jiangsu University, Zhenjiang, Jiangsu, China 0 Views copyright © 2024 Qian L. This is an open access peer review report distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. format_quote Cite this report speaker_notes Responses (1) Approved With Reservations info_outline Alongside their report, reviewers assign a status to the article: Approved The paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. Not approved Fundamental flaws in the paper seriously undermine the findings and conclusions This manuscript performed bibliometric analysis of machine learning applications to hydrothermal liquefaction. However, similar studies have been reported and the following issues should be addressed. A major revision is needed before the indexing. 1 Background in abstract: It is important to emphasize HTL's focus on wet biomass. 2 Methods in abstract: machine learning should be introduced. 3 Second paragraph in introduction: Machine learning has emerged as the most popular technology to address such difficult and complex problems efficiently. What specifically do “such difficult and complex problems” refer to? 4 Fourth paragraph in introduction: There is too much information about ionic liquids. 5 Fifth paragraph in introduction: The application of the bibliometric method in the HTL field has not been introduced, nor has its application in the ML field. 6 “Based on the available literature review, this study represents the first bibliographic analysis conducted to evaluate research trends specifically in the development of ML within the context of HTL.”. It is not the first bibliographic study on ML-based HTL. similar studies have been reported. What is the novelty of the manuscript? 7 Methods: The first paragraph in methods is the introduction of bibliometrics. Move it to the introduction part. 8 Figure 8: why “article” is a keyword? 9 No reference in this year. Is the work clearly and accurately presented and does it cite the current literature? Partly Is the study design appropriate and is the work technically sound? Partly Are sufficient details of methods and analysis provided to allow replication by others? Partly If applicable, is the statistical analysis and its interpretation appropriate? Partly Are all the source data underlying the results available to ensure full reproducibility? Yes Are the conclusions drawn adequately supported by the results? Yes Competing Interests No competing interests were disclosed. Reviewer Expertise My research focused on hydrothermal liquefaction and machine learning. We submitted a similar paper titled "A Review on Machine Learning-Aided Hydrothermal Liquefaction Based on Bibliometric Analysis" on August this year. I confirm that I have read this submission and believe that I have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however I have significant reservations, as outlined above. reply Respond to this report Responses (1) Author Response 26 Oct 2024 Tossapon katongtung, Graduate PhD Program in Energy Engineering, Faculty of Engineering, Chiang Mai University, Chiang Mai, Thailand Thank you for your advice. I can answer the following questions you raised. 1.Background in abstract: It is important to emphasize HTL's focus on wet biomass. Response: Revised, as suggested. 2.Methods in abstract: machine learning should be introduced. Response: Revised, as suggested. 3 Second paragraph in introduction: Machine learning has emerged as the most popular technology to address such difficult and complex problems efficiently. What specifically do “such difficult and complex problems” refer to? Response: This sentence refers to applying ML to processes that are difficult to explain, in this case HTL processes, as in the paragraph, and I believe the following sentence answers this question. “It offers promising avenues for understanding and optimizing HTL processes through data-driven analysis and predictive modeling.” If the reader wishes to elaborate on this, I have added some sentences to the manuscript. 4 Fourth paragraph in introduction: There is too much information about ionic liquids. Response: Revised, as suggested. 5 Fifth paragraph in introduction: The application of the bibliometric method in the HTL field has not been introduced, nor has its application in the ML field. Response: In this sentence, I am confident that I wrote it correctly at that time, because I had been studying this issue since June 2023 and had tried to submit it to a journal for publication but did not receive a response, so I withdrew it and submitted it to F1000research in September 2024. I have kept all my submissions from June 2023. If you want to see them, I can send them to you. 6 “Based on the available literature review, this study represents the first bibliographic analysis conducted to evaluate research trends specifically in the development of ML within the context of HTL.”. It is not the first bibliographic study on ML-based HTL. similar studies have been reported. What is the novelty of the manuscript? Response: I think we have similar ideas on how to work on this, but I must say that at the time I wrote this study, there was no research on this subject before. In this sentence, I am confident that I wrote it correctly at that time, because I had been studying this issue since June 2023 and had tried to submit it to a journal for publication but did not receive a response, so I withdrew it and submitted it to F1000research in September 2024. I have kept all my submissions from June 2023. If you want to see them, I can send them to you. I have submitted Case Studies in Chemical and Environmental Engineering Manuscript Number: CSCEE-D-23-00310 on June 30, 2023. 7 Methods: The first paragraph in methods is the introduction of bibliometrics. Move it to the introduction part. Response: I think this sentence should remain in its original place and should not be changed. 8 Figure 8: why “article” is a keyword? Response: I collected data from Scopus database and did not add any data to the dataset. 9 No reference in this year. Response: I did this study before 2024 and I gave a clear timeline in the manuscript. View more View less Competing Interests No competing interests were disclosed. reply Respond Report a concern Qian L. Peer Review Report For: Current scenario of machine learning applications to hydrothermal liquefaction via bibliometric analysis [version 3; peer review: 1 approved, 2 approved with reservations, 1 not approved] . F1000Research 2025, 13 :1131 ( https://doi.org/10.5256/f1000research.171837.r330069) NOTE: it is important to ensure the information in square brackets after the title is included in this citation. The direct URL for this report is: https://f1000research.com/articles/13-1131/v1#referee-response-330069 Alongside their report, reviewers assign a status to the article: Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit. 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