Data driven modeling of TiO2 PVP nanofiber diameter using LSTM and regression for enhanced functional performance
Abstract The prospective utilization of electrospun nanofibers across diverse fields has elicited substantial scientific attention. Nevertheless, managing their diameter remains problematic due to the intricate interactions among electrospinning variables. This research explores the application of L...
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| Format: | Article |
| Language: | English |
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Springer
2025-04-01
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| Series: | Discover Applied Sciences |
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| Online Access: | https://doi.org/10.1007/s42452-025-06823-7 |
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| author | Harshada Mhetre Sagar Pande Babita Singla Pavan Hiremath Samriddh Sahu Sarvesh Sorte Ketan Kotecha Nithesh Naik |
| author_facet | Harshada Mhetre Sagar Pande Babita Singla Pavan Hiremath Samriddh Sahu Sarvesh Sorte Ketan Kotecha Nithesh Naik |
| author_sort | Harshada Mhetre |
| collection | DOAJ |
| description | Abstract The prospective utilization of electrospun nanofibers across diverse fields has elicited substantial scientific attention. Nevertheless, managing their diameter remains problematic due to the intricate interactions among electrospinning variables. This research explores the application of Long Short-Term Memory (LSTM) networks and multiple regression models to forecast the diameters of Titanium Dioxide (TiO₂) and Polyvinyl pyrrolidone (PVP) nanofibers, facilitating improved process regulation and enhancement. TiO₂ + PVP nanofibers were fabricated under diverse conditions, including changes in applied voltage, solution concentration, and distance between tip to collector. The acquired data underwent analysis using LSTM and regression models to assess their predictive capabilities. The outcomes revealed that both approaches effectively estimated nanofiber diameters; however, the regression model surpassed LSTM with a lower error rate of 0.077% compared to 0.305%. This indicates that while LSTM captures nonlinear relationships, conventional regression models yield more precise predictions in this scenario. These findings underscore the potential of machine learning in advancing electrospinning technology by minimizing trial-and-error experiments and boosting nanofiber production efficiency. The incorporation of artificial intelligence-driven modeling into the electrospinning process sets the stage for more accurate control over fiber morphology, resulting in enhanced material properties and expanded applications in biomedical, environmental, and energy sectors. |
| format | Article |
| id | doaj-art-cf4f0dca02f34e45bd36e205110f3d6d |
| institution | OA Journals |
| issn | 3004-9261 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Springer |
| record_format | Article |
| series | Discover Applied Sciences |
| spelling | doaj-art-cf4f0dca02f34e45bd36e205110f3d6d2025-08-20T02:16:06ZengSpringerDiscover Applied Sciences3004-92612025-04-017411810.1007/s42452-025-06823-7Data driven modeling of TiO2 PVP nanofiber diameter using LSTM and regression for enhanced functional performanceHarshada Mhetre0Sagar Pande1Babita Singla2Pavan Hiremath3Samriddh Sahu4Sarvesh Sorte5Ketan Kotecha6Nithesh Naik7Department of Electronics and Communication Engineering, Bharati Vidyapeeth (Deemed to Be University) College of EngineeringDepartment of Computer Science and Engineering, School of Engineering and Technology, Pimpri Chinchwad UniversityChitkara Business School, Chitkara UniversityDepartment of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher EducationDepartment of Electronics and Telecommunication Engineering, Bharati Vidyapeeth (Deemed to Be University) College of EngineeringDepartment of Electronics and Communication Engineering, Bharati Vidyapeeth (Deemed to Be University) College of EngineeringSymbiosis Centre for Applied Artificial Intelligence, Symbiosis International (Deemed University)Department of Mechanical and Industrial Engineering, Manipal Institute of Technology, Manipal Academy of Higher EducationAbstract The prospective utilization of electrospun nanofibers across diverse fields has elicited substantial scientific attention. Nevertheless, managing their diameter remains problematic due to the intricate interactions among electrospinning variables. This research explores the application of Long Short-Term Memory (LSTM) networks and multiple regression models to forecast the diameters of Titanium Dioxide (TiO₂) and Polyvinyl pyrrolidone (PVP) nanofibers, facilitating improved process regulation and enhancement. TiO₂ + PVP nanofibers were fabricated under diverse conditions, including changes in applied voltage, solution concentration, and distance between tip to collector. The acquired data underwent analysis using LSTM and regression models to assess their predictive capabilities. The outcomes revealed that both approaches effectively estimated nanofiber diameters; however, the regression model surpassed LSTM with a lower error rate of 0.077% compared to 0.305%. This indicates that while LSTM captures nonlinear relationships, conventional regression models yield more precise predictions in this scenario. These findings underscore the potential of machine learning in advancing electrospinning technology by minimizing trial-and-error experiments and boosting nanofiber production efficiency. The incorporation of artificial intelligence-driven modeling into the electrospinning process sets the stage for more accurate control over fiber morphology, resulting in enhanced material properties and expanded applications in biomedical, environmental, and energy sectors.https://doi.org/10.1007/s42452-025-06823-7LSTMMultiple regression modelsTiO2 nanofibersElectrospinningMachine learning model |
| spellingShingle | Harshada Mhetre Sagar Pande Babita Singla Pavan Hiremath Samriddh Sahu Sarvesh Sorte Ketan Kotecha Nithesh Naik Data driven modeling of TiO2 PVP nanofiber diameter using LSTM and regression for enhanced functional performance Discover Applied Sciences LSTM Multiple regression models TiO2 nanofibers Electrospinning Machine learning model |
| title | Data driven modeling of TiO2 PVP nanofiber diameter using LSTM and regression for enhanced functional performance |
| title_full | Data driven modeling of TiO2 PVP nanofiber diameter using LSTM and regression for enhanced functional performance |
| title_fullStr | Data driven modeling of TiO2 PVP nanofiber diameter using LSTM and regression for enhanced functional performance |
| title_full_unstemmed | Data driven modeling of TiO2 PVP nanofiber diameter using LSTM and regression for enhanced functional performance |
| title_short | Data driven modeling of TiO2 PVP nanofiber diameter using LSTM and regression for enhanced functional performance |
| title_sort | data driven modeling of tio2 pvp nanofiber diameter using lstm and regression for enhanced functional performance |
| topic | LSTM Multiple regression models TiO2 nanofibers Electrospinning Machine learning model |
| url | https://doi.org/10.1007/s42452-025-06823-7 |
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