Machine Learning‐Driven Surrogate Modeling for Optimization of Triboelectric Nanogenerator Design Parameters

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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Bibliographic Details
Main Authors: Mohammad Abrar Uddin, Myeongju Lim, Rubiga Kim, Barrett London Burgess, Ken Roberts, Junghyun Kim, Taeil Kim
Format: Article
Language:English
Published: Wiley-VCH 2025-06-01
Series:Advanced Electronic Materials
Subjects:
energy harvesting
machine learning
surrogate modeling‐driven optimization
triboelectric nanogenerators
wearable biosensors
Online Access:https://doi.org/10.1002/aelm.202400771
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https://doi.org/10.1002/aelm.202400771

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