Explainable Machine Learning Framework for Optimizing Electrospinning Parameters in ZnO-PVP Nanofiber Synthesis

Explainable Machine Learning Framework for Optimizing Electrospinning Parameters in ZnO-PVP Nanofiber Synthesis | 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 Article Explainable Machine Learning Framework for Optimizing Electrospinning Parameters in ZnO-PVP Nanofiber Synthesis Princy Randhawa, Ruchita R Patil, Shwetha V, Harshada Vishal Mhetre, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7299441/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 electrospinning technique is crucial for synthesizing zinc oxide (ZnO) nanofibers within a polyvinylpyrrolidone (PVP) polymer matrix, where precise control over fiber diameter is essential for optimizing functional properties. This study investigates the application of Machine Learning (ML) methods, including Multiple Linear Regression (MLR), Support Vector Regression (SVR), and XGBoost, to model and optimize key electrospinning parameters: PVP concentration, flow rate, needle tip-to-collector distance, and applied voltage, aiming to produce uniform nanofibers with minimal diameters. A systematic experimental dataset was generated to capture fiber diameter variations under different parameter settings. Among the models evaluated, SVR demonstrated the best predictive performance, achieving an R² score of 0.97 with accuracy of 0.96, indicating its effectiveness in capturing the complex nonlinear relationships inherent to the electrospinning process. Feature importance analysis using F-scores, SHAP values, and permutation importance consistently identified PVP concentration as the most influential factor, followed by flow rate and voltage, while distance showed minimal impact. The proposed ML-based predictive modeling framework offers a data-driven approach to fine-tune electrospinning conditions, enabling greater precision and efficiency in the fabrication of ZnO-PVP nanofibers. Physical sciences/Engineering Physical sciences/Materials science Physical sciences/Mathematics and computing Physical sciences/Nanoscience and technology Machine Learning MLR SVR XGBoost ZnO nanofiber Explainable AI (XAI) SHAP Feature Importance Full Text Additional Declarations No competing interests reported. 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. 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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-7299441","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":509377793,"identity":"c358fc9f-1a3e-4933-8748-08382f931abc","order_by":0,"name":"Princy Randhawa","email":"","orcid":"","institution":"Manipal University Jaipur","correspondingAuthor":false,"prefix":"","firstName":"Princy","middleName":"","lastName":"Randhawa","suffix":""},{"id":509377795,"identity":"8f3afae7-2b29-4040-8f4d-64f24a597089","order_by":1,"name":"Ruchita R Patil","email":"","orcid":"","institution":"Manipal Academy of Higher 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