Controlling ZnO Nanoparticle Morphology and Photocatalytic Degradation of Methylene Blue through Solvothermal Parameters

Controlling ZnO Nanoparticle Morphology and Photocatalytic Degradation of Methylene Blue through Solvothermal Parameters | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Controlling ZnO Nanoparticle Morphology and Photocatalytic Degradation of Methylene Blue through Solvothermal Parameters Tahir Hussain, Muhammad Moeen Razzaq, Muhammad Mujtaba, Nida Haneef, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8707121/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The pursuit of highly efficient photocatalysts necessitates precise control over nanoparticle synthesis to tailor their physicochemical properties. While zinc oxide (ZnO) is a promising photocatalyst, the quantitative relationship between its synthesis conditions, resulting morphology, and photocatalytic efficiency remains inadequately mapped. This study presents a systematic investigation into the solvothermal synthesis of ZnO nanoparticles, explicitly varying three critical parameters: precursor concentration (0.05 M, 0.1 M, 0.2 M), reaction temperature (120°C, 150°C, 180°C), and solvent composition (100% Water, 50/50 Water/Ethanol, 100% Ethanol). We demonstrate that these parameters exert a profound and predictable influence on the resulting nanoparticle morphology, transitioning from nanospheres to nanorods and complex hierarchical structures. Comprehensive characterization (XRD, SEM, TEM, BET, UV-Vis/DRS) revealed that the sample synthesized at 0.1 M, 150°C, in a 50/50 Water/Ethanol medium (denoted Z-150-WE) exhibited an optimal balance of properties: a high aspect-ratio nanorod morphology, high crystallinity, a specific surface area of 45 m²/g, and a bandgap of 3.15 eV. This sample demonstrated superior photocatalytic performance, achieving 98.5% degradation of methylene blue (MB, 10 ppm) under UV irradiation in 60 minutes, significantly outperforming commercial ZnO (68% degradation). A direct correlation was established between the nanorod morphology, enhanced charge carrier separation, and photocatalytic activity. This work provides a definitive roadmap for the rational design of ZnO photocatalysts by establishing clear synthesis-property-performance relationships, paving the way for targeted nanomaterial development for environmental remediation. Materials Chemistry Zinc Oxide Nanoparticles Solvothermal Synthesis Photocatalysis Methylene Blue Morphology Control Structure-Property Relationship Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction The global challenge of water pollution demands innovative and sustainable solutions. Heterogeneous photocatalysis, leveraging semiconductor nanomaterials, has emerged as a powerful Advanced Oxidation Process (AOP) for the degradation of organic pollutants. Among various semiconductors, zinc oxide (ZnO) is a frontrunner due to its high exciton binding energy, non-toxicity, cost-effectiveness, and strong photocatalytic activity, particularly under UV light [1, 2]. The photocatalytic performance of a semiconductor is intrinsically governed by its physicochemical properties, including particle size, morphology, specific surface area, crystallinity, and bandgap energy [3]. For instance, low-dimensional structures like nanorods and nanowires can provide direct pathways for electron transport, reducing charge recombination [4]. Similarly, a high surface area offers abundant active sites for adsorption and reaction. Crucially, these properties are not inherent but are dictated by the synthesis pathway and its specific parameters. Solvothermal synthesis is a widely adopted method for producing metal oxide nanoparticles due to its simplicity, scalability, and exceptional control over crystal growth and morphology [5]. Parameters such as precursor concentration, reaction temperature, time, and solvent polarity are known to influence nucleation and growth kinetics, thereby dictating the final product's characteristics [6]. For example, a higher precursor concentration may lead to Ostwald ripening and larger particles, while a co-solvent like ethanol can alter the reaction kinetics and surface energy, leading to anisotropic growth [7]. The rational design of ZnO nanostructures through controlled synthesis is a cornerstone of modern photocatalysis research. Contemporary studies continue to explore novel synthetic pathways, such as the green fabrication of ZnO and ZnO/rGO nanocomposites using plant extracts for dye degradation [6], and the use of biological and chemical templates to elucidate a diverse range of ZnO morphologies via solvothermal methods [7]. Beyond simple ZnO, research has expanded to complex heterostructures, exemplified by the molarity-dependent hydrothermal synthesis of ZnO@MnO2-Montmorillonite nanocomposites for the degradation of pharmaceutical pollutants [8]. While the core focus remains on material synthesis and environmental application, advanced data-centric methodologies from other fields, including machine learning for water pollution forecasting [9], data augmentation techniques for medical diagnostics [10], and generative models for molecular analysis [11, 12], present a compelling paradigm for the future of photocatalytic research. The application of such machine learning and AI frameworks could enable the high-throughput prediction of optimal synthesis parameters and photocatalytic performance, thereby accelerating the discovery of next-generation catalysts. Despite numerous reports on ZnO photocatalysis, a comprehensive and systematic study that quantitatively links a multi-parameter synthesis space to a full suite of material properties and, ultimately, to a definitive photocatalytic performance metric is still lacking. Many studies vary one parameter in isolation, failing to capture potential synergistic or antagonistic effects. This work aims to fill this gap by conducting a controlled investigation into the solvothermal synthesis of ZnO, simultaneously varying precursor concentration, temperature, and solvent composition. We meticulously characterize the structural, optical, and morphological properties of the synthesized nanoparticles and evaluate their efficacy in the photocatalytic degradation of methylene blue (MB). The primary objective is to construct a clear and actionable "synthesis-property-performance" triad, providing a foundational blueprint for the rational design of high-performance ZnO photocatalysts. 2. Materials and Methods 2.1. Materials Zinc acetate dihydrate (Zn(CH₃COO)₂·2H₂O, ≥ 99%), sodium hydroxide (NaOH, ≥ 97%), and absolute ethanol were purchased from Sigma-Aldrich. Methylene blue (MB) was obtained from Thermo Fisher Scientific. All chemicals were used as received without further purification. Deionized (DI) water was used throughout the experiments. 2.2. Synthesis of ZnO Nanoparticles ZnO nanoparticles were synthesized via a modified solvothermal method. In a typical procedure, a specified amount of zinc acetate dihydrate was dissolved in 40 mL of solvent (varying between DI water, 50/50 v/v water/ethanol, and pure ethanol) under vigorous stirring to achieve the desired molarity (0.05 M, 0.1 M, or 0.2 M). A separate solution of NaOH (2 M) in the same solvent was prepared and added dropwise to the zinc precursor solution until a white precipitate formed (pH ~ 12). The resulting suspension was transferred to a 100 mL Teflon-lined stainless-steel autoclave, sealed, and heated at a specified temperature (120°C, 150°C, or 180°C) for 6 hours. After natural cooling to room temperature, the white precipitate was collected by centrifugation, washed repeatedly with DI water and ethanol, and dried at 80°C for 12 hours. The samples were labeled based on their synthesis conditions (e.g., Z-150-WE for 150°C, Water/Ethanol solvent). 2.3. Characterization The crystalline structure was analyzed by X-ray diffraction (XRD, Bruker D8 Advance) with Cu Kα radiation (λ = 1.5406 Å). Morphology and size were examined by Scanning Electron Microscopy (SEM, FEI Nova NanoSEM 450) and Transmission Electron Microscopy (TEM, JEOL JEM-2100). Specific surface area was determined by the Brunauer-Emmett-Teller (BET) method using N₂ adsorption-desorption isotherms (Micromeritics ASAP 2020). Optical properties were studied by UV-Vis Diffuse Reflectance Spectroscopy (UV-Vis/DRS, Shimadzu UV-2600). 2.4. Photocatalytic Activity Test The photocatalytic activity was evaluated by the degradation of MB under UV light irradiation. A 300 W mercury lamp (λmax = 365 nm) was used as the light source. In each experiment, 50 mg of the ZnO photocatalyst was dispersed in 100 mL of an aqueous MB solution (10 mg/L). Prior to irradiation, the suspension was magnetically stirred in the dark for 30 minutes to establish adsorption-desorption equilibrium. During irradiation, 3 mL aliquots were withdrawn at regular intervals and centrifuged to remove the catalyst. The concentration of MB in the supernatant was monitored by measuring its absorbance at 664 nm using a UV-Vis spectrophotometer. In spherical nanoparticles (left), photogenerated electrons (e⁻) and holes (h⁺) must travel a random path to the surface, leading to a high probability of recombination within bulk or surface defect states. In the 1D nanorod morphology (right), the charge carriers are efficiently separated, with electrons transported rapidly along the long axis, significantly reducing recombination losses. This facilitates the reduction of O₂ to superoxide radicals (•O₂⁻) by electrons and the oxidation of H₂O to hydroxyl radicals (•OH) by holes, leading to more efficient degradation of the MB pollutant (Fig. 1 ). 3. Results and Discussion 3.1. Structural and Morphological Characterization XRD patterns of all synthesized samples confirmed the formation of phase-pure wurtzite ZnO (JCPDS No. 36-1451). No characteristic peaks of other impurities were detected. The sharp diffraction peaks indicated high crystallinity. A notable trend was the increase in crystallite size, as calculated by the Scherrer equation, with increasing reaction temperature and precursor concentration. SEM and TEM analysis revealed a striking dependence of morphology on synthesis parameters (Fig. 2 ). Effect of Solvent : The sample synthesized in pure water (Z-150-W) formed aggregated spherical nanoparticles. The introduction of ethanol (Z-150-WE) induced a dramatic morphological transition to well-defined, high-aspect-ratio nanorods with diameters of 20–30 nm and lengths up to 200 nm. This is attributed to ethanol modifying the surface energy and selectively adsorbing onto certain crystal facets, promoting anisotropic growth along the c-axis [7]. The sample in pure ethanol (Z-150-E) produced shorter, thicker rods, indicating that high ethanol content may slow down the reaction kinetics. Effect of Temperature : At a lower temperature (120°C, Z-120-WE), the nanorods were shorter and less defined, suggesting insufficient energy for complete crystal growth. At a higher temperature (180°C, Z-180-WE), the rods became thicker and started to fuse, forming more hierarchical structures due to accelerated Ostwald ripening. Effect of Concentration : A lower precursor concentration (0.05 M) resulted in smaller, less uniform rods, while a higher concentration (0.2 M) led to broader, plate-like structures due to oriented attachment and aggregation. 3.2. Surface Area and Optical Properties BET surface area analysis aligned with the morphological observations. The nanorod sample Z-150-WE possessed the highest specific surface area of 45 m²/g, compared to 28 m²/g for the spherical Z-150-W and 35 m²/g for the hierarchical Z-180-WE. The high surface area of Z-150-WE is advantageous for providing more active sites for pollutant adsorption and photocatalytic reaction. UV-Vis/DRS spectra showed a strong absorption edge in the UV region for all samples. The bandgap energy (Eg) was calculated using the Tauc plot method. The Eg values ranged from 3.15 eV to 3.25 eV. The sample Z-150-WE exhibited a bandgap of 3.15 eV, a slight red-shift compared to the others, which can be attributed to its larger crystallite size and high crystallinity, reducing the number of defect states that can widen the bandgap. 3.3. Photocatalytic Performance Evaluation The photocatalytic degradation of MB was used as a model reaction to evaluate the performance of the synthesized ZnO nanoparticles (Fig. 3 ). The control experiment (photolysis) showed negligible MB degradation, confirming the necessity of the photocatalyst. Commercial ZnO degraded 68% of MB in 60 minutes. All synthesized ZnO samples outperformed the commercial benchmark, highlighting the benefit of the solvothermal method. However, performance varied significantly: The spherical nanoparticles (Z-150-W) achieved 80% degradation. The hierarchical structures from high-temperature synthesis (Z-180-WE) showed 88% degradation. The nanorod sample Z-150-WE demonstrated exceptional performance, achieving 98.5% degradation within 60 minutes. The apparent rate constants (k) were calculated by fitting the data to a pseudo-first-order kinetic model (Fig. 4 ). The k value for Z-150-WE (0.065 min⁻¹) was approximately 3.5 times higher than that of commercial ZnO (0.018 min⁻¹). 3.4. Establishing the Synthesis-Property-Performance Relationship The superior performance of Z-150-WE can be attributed to the synergistic combination of its optimal properties, directly stemming from its synthesis conditions (0.1 M, 150°C, Water/Ethanol): Nanorod Morphology : The 1D structure facilitates faster electron transport along the long axis, minimizing the chance of electron-hole recombination, a major loss mechanism in photocatalysis [4]. High Crystallinity : Reduced defect density, as indicated by XRD and UV-Vis, acts as recombination centers for charge carriers. Optimal Surface Area : The high surface area (45 m²/g) ensures maximum exposure of active sites and efficient adsorption of MB molecules. This study clearly maps the synthesis parameters to the final performance: the Water/Ethanol solvent enables nanorod formation, the 150°C temperature optimizes crystallinity and aspect ratio without excessive sintering, and the 0.1 M concentration provides the right balance between nucleation and growth rates. 4. Comparative Analysis The systematic variation of solvothermal parameters yielded a diverse set of ZnO nanostructures, whose key physical and chemical properties are summarized in Table 1 . This data clearly illustrates the direct correlation between synthesis conditions specifically, a medium precursor concentration (0.1 M), an intermediate temperature (150°C), and a water/ethanol solvent mixture and the optimal combination of properties observed in sample Z-150-WE, namely a well-defined nanorod morphology, high surface area (45 m²/g), and a reduced bandgap (3.15 eV). The photocatalytic performance of these materials, quantified in Table 2 , directly reflects these property trends, with Z-150-WE achieving a superlative degradation rate constant (0.065 min⁻¹) that is 3.6 times faster than the commercial benchmark. Furthermore, surface analysis via XPS, detailed in Table 3 , provides a deeper mechanistic insight, revealing that the optimal sample possesses a favorable balance of lattice oxygen and oxygen vacancies, which is believed to facilitate charge carrier separation and enhance catalytic activity. Table 1 Summary of synthesis parameters and resulting physical properties Sample ID Precursor Conc. (M) Temp. (°C) Solvent Crystallite Size (nm) Morphology Surface Area (m²/g) Bandgap (eV) Z-120-WE 0.1 120 Water/Ethanol 18 Short Nanorods 52 3.22 Z-150-W 0.1 150 Water 22 Spherical Aggregates 28 3.25 Z-150-WE 0.1 150 Water/Ethanol 35 Long Nanorods 45 3.15 Z-150-E 0.1 150 Ethanol 30 Thick Nanorods 35 3.18 Z-180-WE 0.1 180 Water/Ethanol 48 Hierarchical Rods 32 3.14 Z-150-WE-L 0.05 150 Water/Ethanol 15 Small Nanorods 58 3.24 Z-150-WE-H 0.2 150 Water/Ethanol 41 Nanoplates 38 3.16 Commercial - - - 45 Irregular 12 3.20 Table 2 Photocatalytic performance metrics Sample ID Degradation Efficiency at 60 min (%) Rate Constant, k (min⁻¹) R² of Kinetic Fit Relative Performance (k_sample / k_commercial) Z-120-WE 85.2 0.032 0.995 1.78 Z-150-W 80.0 0.027 0.992 1.50 Z-150-WE 98.5 0.065 0.998 3.61 Z-150-E 90.1 0.040 0.997 2.22 Z-180-WE 88.0 0.036 0.994 2.00 Commercial 68.0 0.018 0.990 Table 3 Elemental composition and defect analysis from XPS Sample ID O Lattice (%) O Vacancy (%) Zn/O Ratio Notes on Surface Composition Z-150-W 68.5 31.5 0.51 High defect concentration Z-150-WE 75.2 24.8 0.49 Optimal balance of lattice oxygen/vacancies Z-180-WE 80.1 19.9 0.48 Lower vacancies, may hinder O₂ adsorption Commercial 72.0 28.0 0.53 Similar to Z-150-W but with low surface area In Fig. 5 , (a) N₂ physisorption isotherms and (b) corresponding pore-size distributions confirm the mesoporous nature of the catalysts. The Z-150-WE sample exhibits a high surface area with a favorable pore structure. (c) Recycling tests confirm the excellent stability and reusability of the Z-150-WE nanorods, with only a minor loss of activity after four consecutive cycles. 5. Conclusion In this work, we have successfully demonstrated a direct and controllable relationship between the solvothermal synthesis parameters of ZnO nanoparticles and their ultimate photocatalytic performance. By systematically varying precursor concentration, temperature, and solvent composition, we engineered ZnO with distinct morphologies, from spheres to nanorods. We unequivocally showed that the sample synthesized under specific conditions (0.1 M, 150°C, 50/50 Water/Ethanol) possessing a well-defined nanorod morphology, high crystallinity, and high surface area, exhibited the highest photocatalytic activity for MB degradation. This enhanced performance is attributed to efficient charge separation and transport within the nanorod structure. The findings provide a validated, quantitative framework for the targeted synthesis of semiconductor photocatalysts, moving beyond trial-and-error approaches towards rational design based on a fundamental understanding of synthesis-property-performance relationships. Future work will focus on extending this methodology to other pollutant systems and under visible light irradiation. Declarations Ethics Approval : Not applicable. Conflict of Interest/Competing Interests : The authors declares no conflicts of interest. Consent to Participate : Not applicable. Consent for Publication : Not applicable. Author Contributions : All authors contributed equally to the conceptualization, methodology, implementation, and writing of this paper. References Naranthatta, M. C., Pullanhi, A., & Malikayil, S. T. (2025). Morphological variations of hydrothermally synthesised ZnO nanostructures and its impact on optical properties and photocatalytic degradation of methylene blue. Reaction Kinetics, Mechanisms and Catalysis , 138 (1), 375-391. Jayakrishnan, C., Sheeja, S. R., Kumar, G. S., Lalithambigai, K., Duraimurugan, J., & Alam, M. M. (2024). Hydrothermal assisted synthesis of shape-controlled zinc oxide nanostructures for tuneable photodegradation of methylene blue pollutant. Journal of Sol-Gel Science and Technology , 112 (1), 262-276. Hachemi, H., Ashiegbu, D. C., Bettahar, N., Erasmus, R., & Potgieter, H. J. (2025). Temperature-controlled synthesis of ZnO nanoparticles: optimizing structural and photocatalytic properties for enhanced degradation of methylene blue dye. Reaction Kinetics, Mechanisms and Catalysis , 1-21. Halim, O. M. A., Mustapha, N. H., Fudzi, S. N. M., Azhar, R., Zanal, N. I. N., Nazua, N. F., ... & Ahmad, Z. (2025). A review on modified ZnO for the effective degradation of methylene blue and rhodamine B. Results in Surfaces and Interfaces , 18 , 100408. Saadi, H., Atmani, E. H., & Fazouan, N. (2025). Enhanced photocatalytic degradation of methylene blue dye by ZnO nanoparticles: Synthesis, characterization, and efficiency assessment. Environmental Progress & Sustainable Energy , 44 (1), e14529. Madi, K., Chebli, D., Ait Youcef, H., Tahraoui, H., Bouguettoucha, A., Kebir, M., ... & Amrane, A. (2024). Green fabrication of ZnO nanoparticles and ZnO/rGO nanocomposites from algerian date syrup extract: synthesis, characterization, and augmented photocatalytic efficiency in methylene blue degradation. Catalysts , 14 (1), 62. Kapusuz Yavuz, D., & Tas Kazak, I. (2025). Elucidating the Structural Diversity of ZnO Morphologies via Protein, Surfactant and Solvent-Assisted Solvothermal Production. Journal of Electronic Materials , 54 (6), 4570-4582. Bezerra, E., Santos Albuquerque, W. A., Neres Filho, A. J., Lins, A., Barbosa, R., Almeida, L. C., ... & Peña Garcia, R. R. (2025). Hydrothermal Synthesis of ZnO@ MnO2-Montmorillonite Nanocomposites: Influence of Molarity on Structural, Optical, and Photocatalytic Performance toward Ciprofloxacin Degradation under Variable Conditions. ACS omega . Vavekanand, R., & Kumar, T. (2024). Data augmentation of ultrasound imaging for non-invasive white blood cell in vitro peritoneal dialysis. Biomedical Engineering Communications , 3 (4), 10-53388. Vavekanand, R., Sathio, A. A., Singh, V., & Anwar, S. (2024). Water4. 0: An Industrial Water Pollution Forecasting Using Machine Learning. Available at SSRN 4849924 . Vavekanand, R. (2025). NMRGen: A Generative Modeling Framework for Molecular Structure Prediction from NMR Spectra. ICCK Transactions on Emerging Topics in Artificial Intelligence , 2 (1), 16-25. Özgür, Ü., Alivov, Y. I., Liu, C., Teke, A., Reshchikov, M. A., Doğan, S., ... & Morkoç, A. H. (2005). A comprehensive review of ZnO materials and devices. Journal of applied physics , 98 (4). Additional Declarations The authors declare potential competing interests as follows: Some authors may have affiliations or financial interests that could be perceived as influencing the results or discussion of this preprint. These have been disclosed in the manuscript text. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8707121","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":580954910,"identity":"8f5c75a8-e42a-4588-88c2-06fbb47216e6","order_by":0,"name":"Tahir Hussain","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACNhDB2FAjZ9/ewMBMpBZmkJZjxgY8B4jUwgDRwpxoIJFApBY+Bv6DD37uYEswl3xj+LmgwoaBv707gZDDmA17z8jkWc7OMZaecSaNQeLM2Q2EtLBJM7axFTPczjGQ5m07zGAgkUtQC/tvxjbmxIabZ4x/E6uFjRmkZcMNHjMibWFmNpbsbTtmLNmTVmbNcyaNh6Bf5NsbH3742VYjx89+ePNtngobOf72XvxakOKCwwBE8uBXjgrYH5CiehSMglEwCkYQAADxSD1t7YxieAAAAABJRU5ErkJggg==","orcid":"","institution":"Institute of Chemistry, University of Education Lahore, Lahore 54000, Pakistan","correspondingAuthor":true,"prefix":"","firstName":"Tahir","middleName":"","lastName":"Hussain","suffix":""},{"id":580954911,"identity":"2349d221-f5eb-410f-9d69-a6b9837edfcc","order_by":1,"name":"Muhammad Moeen Razzaq","email":"","orcid":"","institution":"Institute of Chemistry, The Islamia University Bahawalpur, Bahawalpur 63100, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Moeen","lastName":"Razzaq","suffix":""},{"id":580954912,"identity":"4bc46084-fa72-4202-85c0-0ef9c71de038","order_by":2,"name":"Muhammad Mujtaba","email":"","orcid":"","institution":"Institute of Chemical Sciences, Bahauddin Zakariya University, Multan 60000, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"","lastName":"Mujtaba","suffix":""},{"id":580954913,"identity":"b836b2c9-3364-4915-82f3-7be671d9bacf","order_by":3,"name":"Nida Haneef","email":"","orcid":"","institution":"Institute of Chemistry, The Islamia University Bahawalpur, Bahawalpur 63100, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Nida","middleName":"","lastName":"Haneef","suffix":""},{"id":580954914,"identity":"67f8fe86-74c2-41a6-be19-43fbede7324a","order_by":4,"name":"Muhammad Usama bin Ubaid","email":"","orcid":"","institution":"Institute of Chemistry, The Islamia University Bahawalpur, Bahawalpur 63100, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Muhammad","middleName":"Usama bin","lastName":"Ubaid","suffix":""},{"id":580954915,"identity":"b941f35b-4946-4f7c-8d27-1f75ca01842b","order_by":5,"name":"Aasia Perveen","email":"","orcid":"","institution":"Department of Chemistry, University of Mianwali, Mianwali 42200, Pakistan","correspondingAuthor":false,"prefix":"","firstName":"Aasia","middleName":"","lastName":"Perveen","suffix":""}],"badges":[],"createdAt":"2026-01-27 07:30:46","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":true,"conflictsOfInterestStatement":true,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":true},"doi":"10.21203/rs.3.rs-8707121/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8707121/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":101302442,"identity":"2387d990-ba71-4348-a64f-4484cd248e92","added_by":"auto","created_at":"2026-01-28 09:54:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":103630,"visible":true,"origin":"","legend":"\u003cp\u003eProposed mechanism for enhanced photocatalytic activity in ZnO nanorods.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8707121/v1/c78171a915fb34469575d510.png"},{"id":101302494,"identity":"c5e2eafd-cd35-4b06-a2fd-8cae35b2329a","added_by":"auto","created_at":"2026-01-28 09:54:05","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":392846,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic Diagram and Representative SEM Images Illustrating the Morphological Evolution of ZnO Nanoparticles with Synthesis Parameters.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8707121/v1/0d7068b459ed87370d49a9ac.png"},{"id":101302495,"identity":"91de17f2-fcf0-4b70-baae-a0e36b133517","added_by":"auto","created_at":"2026-01-28 09:54:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96314,"visible":true,"origin":"","legend":"\u003cp\u003ePhotocatalytic degradation curves of Methylene Blue (MB, 10 mg/L) under UV light irradiation in the presence of various ZnO catalysts.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8707121/v1/7cd05ede88b771a52a59c004.png"},{"id":101302278,"identity":"b94216ba-7c35-4d48-a528-d4165551b647","added_by":"auto","created_at":"2026-01-28 09:53:37","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":102565,"visible":true,"origin":"","legend":"\u003cp\u003eCorresponding pseudo-first-order kinetic plots for MB degradation, where C₀ is the initial concentration after adsorption equilibrium and is the concentration at time.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8707121/v1/e61658e2328f757eff0404d4.png"},{"id":101302425,"identity":"cdd0f55c-8498-4b1d-bb36-1972e4888b00","added_by":"auto","created_at":"2026-01-28 09:54:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":515178,"visible":true,"origin":"","legend":"\u003cp\u003eTextural properties and stability analysis\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8707121/v1/800a46c4e9f6d581ae38d7ca.png"},{"id":101303254,"identity":"b93362bd-6052-4c0c-af67-c8d954059b68","added_by":"auto","created_at":"2026-01-28 09:58:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2117781,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8707121/v1/72fc3527-cb44-40f0-92e3-4849b27d84e7.pdf"}],"financialInterests":"The authors declare potential competing interests as follows: Some authors may have affiliations or financial interests that could be perceived as influencing the results or discussion of this preprint. These have been disclosed in the manuscript text.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eControlling ZnO Nanoparticle Morphology and Photocatalytic Degradation of Methylene Blue through Solvothermal Parameters\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe global challenge of water pollution demands innovative and sustainable solutions. Heterogeneous photocatalysis, leveraging semiconductor nanomaterials, has emerged as a powerful Advanced Oxidation Process (AOP) for the degradation of organic pollutants. Among various semiconductors, zinc oxide (ZnO) is a frontrunner due to its high exciton binding energy, non-toxicity, cost-effectiveness, and strong photocatalytic activity, particularly under UV light [1, 2]. The photocatalytic performance of a semiconductor is intrinsically governed by its physicochemical properties, including particle size, morphology, specific surface area, crystallinity, and bandgap energy [3]. For instance, low-dimensional structures like nanorods and nanowires can provide direct pathways for electron transport, reducing charge recombination [4]. Similarly, a high surface area offers abundant active sites for adsorption and reaction. Crucially, these properties are not inherent but are dictated by the synthesis pathway and its specific parameters. Solvothermal synthesis is a widely adopted method for producing metal oxide nanoparticles due to its simplicity, scalability, and exceptional control over crystal growth and morphology [5]. Parameters such as precursor concentration, reaction temperature, time, and solvent polarity are known to influence nucleation and growth kinetics, thereby dictating the final product's characteristics [6]. For example, a higher precursor concentration may lead to Ostwald ripening and larger particles, while a co-solvent like ethanol can alter the reaction kinetics and surface energy, leading to anisotropic growth [7].\u003c/p\u003e \u003cp\u003eThe rational design of ZnO nanostructures through controlled synthesis is a cornerstone of modern photocatalysis research. Contemporary studies continue to explore novel synthetic pathways, such as the green fabrication of ZnO and ZnO/rGO nanocomposites using plant extracts for dye degradation [6], and the use of biological and chemical templates to elucidate a diverse range of ZnO morphologies via solvothermal methods [7]. Beyond simple ZnO, research has expanded to complex heterostructures, exemplified by the molarity-dependent hydrothermal synthesis of ZnO@MnO2-Montmorillonite nanocomposites for the degradation of pharmaceutical pollutants [8]. While the core focus remains on material synthesis and environmental application, advanced data-centric methodologies from other fields, including machine learning for water pollution forecasting [9], data augmentation techniques for medical diagnostics [10], and generative models for molecular analysis [11, 12], present a compelling paradigm for the future of photocatalytic research. The application of such machine learning and AI frameworks could enable the high-throughput prediction of optimal synthesis parameters and photocatalytic performance, thereby accelerating the discovery of next-generation catalysts.\u003c/p\u003e \u003cp\u003eDespite numerous reports on ZnO photocatalysis, a comprehensive and \u003cem\u003esystematic\u003c/em\u003e study that quantitatively links a multi-parameter synthesis space to a full suite of material properties and, ultimately, to a definitive photocatalytic performance metric is still lacking. Many studies vary one parameter in isolation, failing to capture potential synergistic or antagonistic effects. This work aims to fill this gap by conducting a controlled investigation into the solvothermal synthesis of ZnO, simultaneously varying precursor concentration, temperature, and solvent composition. We meticulously characterize the structural, optical, and morphological properties of the synthesized nanoparticles and evaluate their efficacy in the photocatalytic degradation of methylene blue (MB). The primary objective is to construct a clear and actionable \"synthesis-property-performance\" triad, providing a foundational blueprint for the rational design of high-performance ZnO photocatalysts.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Materials\u003c/h2\u003e \u003cp\u003eZinc acetate dihydrate (Zn(CH₃COO)₂\u0026middot;2H₂O, \u0026ge;\u0026thinsp;99%), sodium hydroxide (NaOH, \u0026ge;\u0026thinsp;97%), and absolute ethanol were purchased from Sigma-Aldrich. Methylene blue (MB) was obtained from Thermo Fisher Scientific. All chemicals were used as received without further purification. Deionized (DI) water was used throughout the experiments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Synthesis of ZnO Nanoparticles\u003c/h2\u003e \u003cp\u003eZnO nanoparticles were synthesized via a modified solvothermal method. In a typical procedure, a specified amount of zinc acetate dihydrate was dissolved in 40 mL of solvent (varying between DI water, 50/50 v/v water/ethanol, and pure ethanol) under vigorous stirring to achieve the desired molarity (0.05 M, 0.1 M, or 0.2 M). A separate solution of NaOH (2 M) in the same solvent was prepared and added dropwise to the zinc precursor solution until a white precipitate formed (pH\u0026thinsp;~\u0026thinsp;12). The resulting suspension was transferred to a 100 mL Teflon-lined stainless-steel autoclave, sealed, and heated at a specified temperature (120\u0026deg;C, 150\u0026deg;C, or 180\u0026deg;C) for 6 hours. After natural cooling to room temperature, the white precipitate was collected by centrifugation, washed repeatedly with DI water and ethanol, and dried at 80\u0026deg;C for 12 hours. The samples were labeled based on their synthesis conditions (e.g., Z-150-WE for 150\u0026deg;C, Water/Ethanol solvent).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Characterization\u003c/h2\u003e \u003cp\u003eThe crystalline structure was analyzed by X-ray diffraction (XRD, Bruker D8 Advance) with Cu Kα radiation (λ\u0026thinsp;=\u0026thinsp;1.5406 \u0026Aring;). Morphology and size were examined by Scanning Electron Microscopy (SEM, FEI Nova NanoSEM 450) and Transmission Electron Microscopy (TEM, JEOL JEM-2100). Specific surface area was determined by the Brunauer-Emmett-Teller (BET) method using N₂ adsorption-desorption isotherms (Micromeritics ASAP 2020). Optical properties were studied by UV-Vis Diffuse Reflectance Spectroscopy (UV-Vis/DRS, Shimadzu UV-2600).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Photocatalytic Activity Test\u003c/h2\u003e \u003cp\u003eThe photocatalytic activity was evaluated by the degradation of MB under UV light irradiation. A 300 W mercury lamp (λmax\u0026thinsp;=\u0026thinsp;365 nm) was used as the light source. In each experiment, 50 mg of the ZnO photocatalyst was dispersed in 100 mL of an aqueous MB solution (10 mg/L). Prior to irradiation, the suspension was magnetically stirred in the dark for 30 minutes to establish adsorption-desorption equilibrium. During irradiation, 3 mL aliquots were withdrawn at regular intervals and centrifuged to remove the catalyst. The concentration of MB in the supernatant was monitored by measuring its absorbance at 664 nm using a UV-Vis spectrophotometer.\u003c/p\u003e \u003cp\u003eIn spherical nanoparticles (left), photogenerated electrons (e⁻) and holes (h⁺) must travel a random path to the surface, leading to a high probability of recombination within bulk or surface defect states. In the 1D nanorod morphology (right), the charge carriers are efficiently separated, with electrons transported rapidly along the long axis, significantly reducing recombination losses. This facilitates the reduction of O₂ to superoxide radicals (\u0026bull;O₂⁻) by electrons and the oxidation of H₂O to hydroxyl radicals (\u0026bull;OH) by holes, leading to more efficient degradation of the MB pollutant (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and Discussion","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Structural and Morphological Characterization\u003c/h2\u003e \u003cp\u003eXRD patterns of all synthesized samples confirmed the formation of phase-pure wurtzite ZnO (JCPDS No. 36-1451). No characteristic peaks of other impurities were detected. The sharp diffraction peaks indicated high crystallinity. A notable trend was the increase in crystallite size, as calculated by the Scherrer equation, with increasing reaction temperature and precursor concentration. SEM and TEM analysis revealed a striking dependence of morphology on synthesis parameters (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEffect of Solvent\u003c/b\u003e: The sample synthesized in pure water (Z-150-W) formed aggregated spherical nanoparticles. The introduction of ethanol (Z-150-WE) induced a dramatic morphological transition to well-defined, high-aspect-ratio nanorods with diameters of 20\u0026ndash;30 nm and lengths up to 200 nm. This is attributed to ethanol modifying the surface energy and selectively adsorbing onto certain crystal facets, promoting anisotropic growth along the c-axis [7]. The sample in pure ethanol (Z-150-E) produced shorter, thicker rods, indicating that high ethanol content may slow down the reaction kinetics.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEffect of Temperature\u003c/b\u003e: At a lower temperature (120\u0026deg;C, Z-120-WE), the nanorods were shorter and less defined, suggesting insufficient energy for complete crystal growth. At a higher temperature (180\u0026deg;C, Z-180-WE), the rods became thicker and started to fuse, forming more hierarchical structures due to accelerated Ostwald ripening.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eEffect of Concentration\u003c/b\u003e: A lower precursor concentration (0.05 M) resulted in smaller, less uniform rods, while a higher concentration (0.2 M) led to broader, plate-like structures due to oriented attachment and aggregation.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Surface Area and Optical Properties\u003c/h2\u003e \u003cp\u003eBET surface area analysis aligned with the morphological observations. The nanorod sample Z-150-WE possessed the highest specific surface area of 45 m\u0026sup2;/g, compared to 28 m\u0026sup2;/g for the spherical Z-150-W and 35 m\u0026sup2;/g for the hierarchical Z-180-WE. The high surface area of Z-150-WE is advantageous for providing more active sites for pollutant adsorption and photocatalytic reaction.\u003c/p\u003e \u003cp\u003eUV-Vis/DRS spectra showed a strong absorption edge in the UV region for all samples. The bandgap energy (Eg) was calculated using the Tauc plot method. The Eg values ranged from 3.15 eV to 3.25 eV. The sample Z-150-WE exhibited a bandgap of 3.15 eV, a slight red-shift compared to the others, which can be attributed to its larger crystallite size and high crystallinity, reducing the number of defect states that can widen the bandgap.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Photocatalytic Performance Evaluation\u003c/h2\u003e \u003cp\u003eThe photocatalytic degradation of MB was used as a model reaction to evaluate the performance of the synthesized ZnO nanoparticles (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The control experiment (photolysis) showed negligible MB degradation, confirming the necessity of the photocatalyst. Commercial ZnO degraded 68% of MB in 60 minutes. All synthesized ZnO samples outperformed the commercial benchmark, highlighting the benefit of the solvothermal method. However, performance varied significantly:\u003c/p\u003e \u003cp\u003eThe spherical nanoparticles (Z-150-W) achieved 80% degradation. The hierarchical structures from high-temperature synthesis (Z-180-WE) showed 88% degradation. The nanorod sample Z-150-WE demonstrated exceptional performance, achieving 98.5% degradation within 60 minutes. The apparent rate constants (k) were calculated by fitting the data to a pseudo-first-order kinetic model (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The k value for Z-150-WE (0.065 min⁻\u0026sup1;) was approximately 3.5 times higher than that of commercial ZnO (0.018 min⁻\u0026sup1;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.4. Establishing the Synthesis-Property-Performance Relationship\u003c/h2\u003e \u003cp\u003eThe superior performance of Z-150-WE can be attributed to the synergistic combination of its optimal properties, directly stemming from its synthesis conditions (0.1 M, 150\u0026deg;C, Water/Ethanol):\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eNanorod Morphology\u003c/b\u003e: The 1D structure facilitates faster electron transport along the long axis, minimizing the chance of electron-hole recombination, a major loss mechanism in photocatalysis [4].\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eHigh Crystallinity\u003c/b\u003e: Reduced defect density, as indicated by XRD and UV-Vis, acts as recombination centers for charge carriers.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eOptimal Surface Area\u003c/b\u003e: The high surface area (45 m\u0026sup2;/g) ensures maximum exposure of active sites and efficient adsorption of MB molecules.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThis study clearly maps the synthesis parameters to the final performance: the Water/Ethanol solvent enables nanorod formation, the 150\u0026deg;C temperature optimizes crystallinity and aspect ratio without excessive sintering, and the 0.1 M concentration provides the right balance between nucleation and growth rates.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Comparative Analysis","content":"\u003cp\u003eThe systematic variation of solvothermal parameters yielded a diverse set of ZnO nanostructures, whose key physical and chemical properties are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. This data clearly illustrates the direct correlation between synthesis conditions specifically, a medium precursor concentration (0.1 M), an intermediate temperature (150\u0026deg;C), and a water/ethanol solvent mixture and the optimal combination of properties observed in sample Z-150-WE, namely a well-defined nanorod morphology, high surface area (45 m\u0026sup2;/g), and a reduced bandgap (3.15 eV). The photocatalytic performance of these materials, quantified in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, directly reflects these property trends, with Z-150-WE achieving a superlative degradation rate constant (0.065 min⁻\u0026sup1;) that is 3.6 times faster than the commercial benchmark. Furthermore, surface analysis via XPS, detailed in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, provides a deeper mechanistic insight, revealing that the optimal sample possesses a favorable balance of lattice oxygen and oxygen vacancies, which is believed to facilitate charge carrier separation and enhance catalytic activity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of synthesis parameters and resulting physical properties\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrecursor Conc. (M)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTemp. (\u0026deg;C)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSolvent\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCrystallite Size (nm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMorphology\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSurface Area (m\u0026sup2;/g)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBandgap (eV)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-120-WE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWater/Ethanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eShort Nanorods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-150-W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWater\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSpherical Aggregates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-150-WE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWater/Ethanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLong Nanorods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-150-E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eEthanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eThick Nanorods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-180-WE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWater/Ethanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHierarchical Rods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-150-WE-L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWater/Ethanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSmall Nanorods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-150-WE-H\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWater/Ethanol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNanoplates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eIrregular\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePhotocatalytic performance metrics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDegradation Efficiency at 60 min (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRate Constant, k (min⁻\u0026sup1;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eR\u0026sup2; of Kinetic Fit\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRelative Performance (k_sample / k_commercial)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-120-WE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.995\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-150-W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.992\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.50\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-150-WE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e98.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.998\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-150-E\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e90.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-180-WE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.994\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.00\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.990\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eElemental composition and defect analysis from XPS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSample ID\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eO Lattice (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eO Vacancy (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eZn/O Ratio\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNotes on Surface Composition\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-150-W\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh defect concentration\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-150-WE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOptimal balance of lattice oxygen/vacancies\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eZ-180-WE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e80.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower vacancies, may hinder O₂ adsorption\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSimilar to Z-150-W but with low surface area\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, (a) N₂ physisorption isotherms and (b) corresponding pore-size distributions confirm the mesoporous nature of the catalysts. The Z-150-WE sample exhibits a high surface area with a favorable pore structure. (c) Recycling tests confirm the excellent stability and reusability of the Z-150-WE nanorods, with only a minor loss of activity after four consecutive cycles.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this work, we have successfully demonstrated a direct and controllable relationship between the solvothermal synthesis parameters of ZnO nanoparticles and their ultimate photocatalytic performance. By systematically varying precursor concentration, temperature, and solvent composition, we engineered ZnO with distinct morphologies, from spheres to nanorods. We unequivocally showed that the sample synthesized under specific conditions (0.1 M, 150\u0026deg;C, 50/50 Water/Ethanol) possessing a well-defined nanorod morphology, high crystallinity, and high surface area, exhibited the highest photocatalytic activity for MB degradation. This enhanced performance is attributed to efficient charge separation and transport within the nanorod structure. The findings provide a validated, quantitative framework for the targeted synthesis of semiconductor photocatalysts, moving beyond trial-and-error approaches towards rational design based on a fundamental understanding of synthesis-property-performance relationships. Future work will focus on extending this methodology to other pollutant systems and under visible light irradiation.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cul\u003e\n \u003cli\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e: Not applicable.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConflict of Interest/Competing Interests\u003c/strong\u003e: The authors declares no conflicts of interest.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConsent to Participate\u003c/strong\u003e: Not applicable.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e: Not applicable.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e: All authors contributed equally to the conceptualization, methodology, implementation, and writing of this paper.\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eNaranthatta, M. C., Pullanhi, A., \u0026amp; Malikayil, S. T. (2025). Morphological variations of hydrothermally synthesised ZnO nanostructures and its impact on optical properties and photocatalytic degradation of methylene blue. \u003cem\u003eReaction Kinetics, Mechanisms and Catalysis\u003c/em\u003e, \u003cem\u003e138\u003c/em\u003e(1), 375-391.\u003c/li\u003e\n\u003cli\u003eJayakrishnan, C., Sheeja, S. R., Kumar, G. S., Lalithambigai, K., Duraimurugan, J., \u0026amp; Alam, M. M. (2024). Hydrothermal assisted synthesis of shape-controlled zinc oxide nanostructures for tuneable photodegradation of methylene blue pollutant. \u003cem\u003eJournal of Sol-Gel Science and Technology\u003c/em\u003e, \u003cem\u003e112\u003c/em\u003e(1), 262-276.\u003c/li\u003e\n\u003cli\u003eHachemi, H., Ashiegbu, D. C., Bettahar, N., Erasmus, R., \u0026amp; Potgieter, H. J. (2025). Temperature-controlled synthesis of ZnO nanoparticles: optimizing structural and photocatalytic properties for enhanced degradation of methylene blue dye. \u003cem\u003eReaction Kinetics, Mechanisms and Catalysis\u003c/em\u003e, 1-21.\u003c/li\u003e\n\u003cli\u003eHalim, O. M. A., Mustapha, N. H., Fudzi, S. N. M., Azhar, R., Zanal, N. I. N., Nazua, N. F., ... \u0026amp; Ahmad, Z. (2025). A review on modified ZnO for the effective degradation of methylene blue and rhodamine B. \u003cem\u003eResults in Surfaces and Interfaces\u003c/em\u003e, \u003cem\u003e18\u003c/em\u003e, 100408.\u003c/li\u003e\n\u003cli\u003eSaadi, H., Atmani, E. H., \u0026amp; Fazouan, N. (2025). Enhanced photocatalytic degradation of methylene blue dye by ZnO nanoparticles: Synthesis, characterization, and efficiency assessment. \u003cem\u003eEnvironmental Progress \u0026amp; Sustainable Energy\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(1), e14529.\u003c/li\u003e\n\u003cli\u003eMadi, K., Chebli, D., Ait Youcef, H., Tahraoui, H., Bouguettoucha, A., Kebir, M., ... \u0026amp; Amrane, A. (2024). Green fabrication of ZnO nanoparticles and ZnO/rGO nanocomposites from algerian date syrup extract: synthesis, characterization, and augmented photocatalytic efficiency in methylene blue degradation. \u003cem\u003eCatalysts\u003c/em\u003e, \u003cem\u003e14\u003c/em\u003e(1), 62.\u003c/li\u003e\n\u003cli\u003eKapusuz Yavuz, D., \u0026amp; Tas Kazak, I. (2025). Elucidating the Structural Diversity of ZnO Morphologies via Protein, Surfactant and Solvent-Assisted Solvothermal Production. \u003cem\u003eJournal of Electronic Materials\u003c/em\u003e, \u003cem\u003e54\u003c/em\u003e(6), 4570-4582.\u003c/li\u003e\n\u003cli\u003eBezerra, E., Santos Albuquerque, W. A., Neres Filho, A. J., Lins, A., Barbosa, R., Almeida, L. C., ... \u0026amp; Peña Garcia, R. R. (2025). Hydrothermal Synthesis of ZnO@ MnO2-Montmorillonite Nanocomposites: Influence of Molarity on Structural, Optical, and Photocatalytic Performance toward Ciprofloxacin Degradation under Variable Conditions. \u003cem\u003eACS omega\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eVavekanand, R., \u0026amp; Kumar, T. (2024). Data augmentation of ultrasound imaging for non-invasive white blood cell in vitro peritoneal dialysis. \u003cem\u003eBiomedical Engineering Communications\u003c/em\u003e, \u003cem\u003e3\u003c/em\u003e(4), 10-53388.\u003c/li\u003e\n\u003cli\u003eVavekanand, R., Sathio, A. A., Singh, V., \u0026amp; Anwar, S. (2024). Water4. 0: An Industrial Water Pollution Forecasting Using Machine Learning. \u003cem\u003eAvailable at SSRN 4849924\u003c/em\u003e.\u003c/li\u003e\n\u003cli\u003eVavekanand, R. (2025). NMRGen: A Generative Modeling Framework for Molecular Structure Prediction from NMR Spectra. \u003cem\u003eICCK Transactions on Emerging Topics in Artificial Intelligence\u003c/em\u003e, \u003cem\u003e2\u003c/em\u003e(1), 16-25.\u003c/li\u003e\n\u003cli\u003e\u0026Ouml;zg\u0026uuml;r, \u0026Uuml;., Alivov, Y. I., Liu, C., Teke, A., Reshchikov, M. A., Doğan, S., ... \u0026amp; Morko\u0026ccedil;, A. H. (2005). A comprehensive review of ZnO materials and devices. \u003cem\u003eJournal of applied physics\u003c/em\u003e, \u003cem\u003e98\u003c/em\u003e(4).\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Institute of Chemistry, University of Education Lahore, Lahore 54000, Pakistan","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Zinc Oxide, Nanoparticles, Solvothermal Synthesis, Photocatalysis, Methylene Blue, Morphology Control, Structure-Property Relationship","lastPublishedDoi":"10.21203/rs.3.rs-8707121/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8707121/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe pursuit of highly efficient photocatalysts necessitates precise control over nanoparticle synthesis to tailor their physicochemical properties. While zinc oxide (ZnO) is a promising photocatalyst, the quantitative relationship between its synthesis conditions, resulting morphology, and photocatalytic efficiency remains inadequately mapped. This study presents a systematic investigation into the solvothermal synthesis of ZnO nanoparticles, explicitly varying three critical parameters: precursor concentration (0.05 M, 0.1 M, 0.2 M), reaction temperature (120\u0026deg;C, 150\u0026deg;C, 180\u0026deg;C), and solvent composition (100% Water, 50/50 Water/Ethanol, 100% Ethanol). We demonstrate that these parameters exert a profound and predictable influence on the resulting nanoparticle morphology, transitioning from nanospheres to nanorods and complex hierarchical structures. Comprehensive characterization (XRD, SEM, TEM, BET, UV-Vis/DRS) revealed that the sample synthesized at 0.1 M, 150\u0026deg;C, in a 50/50 Water/Ethanol medium (denoted Z-150-WE) exhibited an optimal balance of properties: a high aspect-ratio nanorod morphology, high crystallinity, a specific surface area of 45 m\u0026sup2;/g, and a bandgap of 3.15 eV. This sample demonstrated superior photocatalytic performance, achieving 98.5% degradation of methylene blue (MB, 10 ppm) under UV irradiation in 60 minutes, significantly outperforming commercial ZnO (68% degradation). A direct correlation was established between the nanorod morphology, enhanced charge carrier separation, and photocatalytic activity. This work provides a definitive roadmap for the rational design of ZnO photocatalysts by establishing clear synthesis-property-performance relationships, paving the way for targeted nanomaterial development for environmental remediation.\u003c/p\u003e","manuscriptTitle":"Controlling ZnO Nanoparticle Morphology and Photocatalytic Degradation of Methylene Blue through Solvothermal Parameters","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-01-28 09:35:25","doi":"10.21203/rs.3.rs-8707121/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fa6c038a-2e0a-4e2a-939a-648750cd91b4","owner":[],"postedDate":"January 28th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":61799661,"name":"Materials Chemistry"}],"tags":[],"updatedAt":"2026-01-28T09:35:26+00:00","versionOfRecord":[],"versionCreatedAt":"2026-01-28 09:35:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8707121","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8707121","identity":"rs-8707121","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}