Machine Learning‐Driven Surrogate Modeling for Optimization of Triboelectric Nanogenerator Design Parameters
Abstract Triboelectric nanogenerators (TENGs) offer a promising solution for energy harvesting in wearable devices and sensors. However, their energy output is dependent on process parameters and should be optimized to maximize performance. Due to the absence of effective analytical models for TENG...
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| Format: | Article |
| Language: | English |
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Wiley-VCH
2025-06-01
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| Series: | Advanced Electronic Materials |
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| Online Access: | https://doi.org/10.1002/aelm.202400771 |
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| author | Mohammad Abrar Uddin Myeongju Lim Rubiga Kim Barrett London Burgess Ken Roberts Junghyun Kim Taeil Kim |
| author_facet | Mohammad Abrar Uddin Myeongju Lim Rubiga Kim Barrett London Burgess Ken Roberts Junghyun Kim Taeil Kim |
| author_sort | Mohammad Abrar Uddin |
| collection | DOAJ |
| description | Abstract Triboelectric nanogenerators (TENGs) offer a promising solution for energy harvesting in wearable devices and sensors. However, their energy output is dependent on process parameters and should be optimized to maximize performance. Due to the absence of effective analytical models for TENG systems, the complex relationship among these variables and the effect of these variables cannot be easily boiled down into a conventional theoretical framework. To address this problem, this study takes four process parameters such as thickness, pore ratio, applied force, and frequency into account and leverages advanced design methods (e.g., Design of Experiment) and machine learning‐based regression models to systematically explore the design space. A contact‐separation TENG has been designed that includes a tribonegative porous layer of graphene nanoplatelets (GNP) dispersed into polydimethylsiloxane (PDMS) matrix and aluminum as the tribopositive material. Several experiments are conducted to train a support vector regressor (SVR) model, validate the predicted performance, and refine the design that can be further used to obtain an optimized TENG design. |
| format | Article |
| id | doaj-art-e2c2d215853748efa5afa81c2825dac9 |
| institution | DOAJ |
| issn | 2199-160X |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Wiley-VCH |
| record_format | Article |
| series | Advanced Electronic Materials |
| spelling | doaj-art-e2c2d215853748efa5afa81c2825dac92025-08-20T03:07:35ZengWiley-VCHAdvanced Electronic Materials2199-160X2025-06-01118n/an/a10.1002/aelm.202400771Machine Learning‐Driven Surrogate Modeling for Optimization of Triboelectric Nanogenerator Design ParametersMohammad Abrar Uddin0Myeongju Lim1Rubiga Kim2Barrett London Burgess3Ken Roberts4Junghyun Kim5Taeil Kim6Mechanical Engineering Department Baylor University One Bear Place #97356 Waco TX 76798‐7356 USASchool of Applied Artificial Intelligence Handong Global University Pohang Gyeongbuk 37554 Republic of KoreaLuddy School of Informatics, Computing, and Engineering Indiana University Bloomington IN 47408 USAMechanical Engineering Department Baylor University One Bear Place #97356 Waco TX 76798‐7356 USAMechanical Engineering Department Baylor University One Bear Place #97356 Waco TX 76798‐7356 USASchool of Aerospace Engineering Sejong University Gwangjin‐gu Seoul 05006 Republic of KoreaMechanical Engineering Department Baylor University One Bear Place #97356 Waco TX 76798‐7356 USAAbstract Triboelectric nanogenerators (TENGs) offer a promising solution for energy harvesting in wearable devices and sensors. However, their energy output is dependent on process parameters and should be optimized to maximize performance. Due to the absence of effective analytical models for TENG systems, the complex relationship among these variables and the effect of these variables cannot be easily boiled down into a conventional theoretical framework. To address this problem, this study takes four process parameters such as thickness, pore ratio, applied force, and frequency into account and leverages advanced design methods (e.g., Design of Experiment) and machine learning‐based regression models to systematically explore the design space. A contact‐separation TENG has been designed that includes a tribonegative porous layer of graphene nanoplatelets (GNP) dispersed into polydimethylsiloxane (PDMS) matrix and aluminum as the tribopositive material. Several experiments are conducted to train a support vector regressor (SVR) model, validate the predicted performance, and refine the design that can be further used to obtain an optimized TENG design.https://doi.org/10.1002/aelm.202400771energy harvestingmachine learningsurrogate modeling‐driven optimizationtriboelectric nanogeneratorswearable biosensors |
| spellingShingle | Mohammad Abrar Uddin Myeongju Lim Rubiga Kim Barrett London Burgess Ken Roberts Junghyun Kim Taeil Kim Machine Learning‐Driven Surrogate Modeling for Optimization of Triboelectric Nanogenerator Design Parameters Advanced Electronic Materials energy harvesting machine learning surrogate modeling‐driven optimization triboelectric nanogenerators wearable biosensors |
| title | Machine Learning‐Driven Surrogate Modeling for Optimization of Triboelectric Nanogenerator Design Parameters |
| title_full | Machine Learning‐Driven Surrogate Modeling for Optimization of Triboelectric Nanogenerator Design Parameters |
| title_fullStr | Machine Learning‐Driven Surrogate Modeling for Optimization of Triboelectric Nanogenerator Design Parameters |
| title_full_unstemmed | Machine Learning‐Driven Surrogate Modeling for Optimization of Triboelectric Nanogenerator Design Parameters |
| title_short | Machine Learning‐Driven Surrogate Modeling for Optimization of Triboelectric Nanogenerator Design Parameters |
| title_sort | machine learning driven surrogate modeling for optimization of triboelectric nanogenerator design parameters |
| topic | energy harvesting machine learning surrogate modeling‐driven optimization triboelectric nanogenerators wearable biosensors |
| url | https://doi.org/10.1002/aelm.202400771 |
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