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

Advanced Electronic Materials, Journal Year: 2025, Volume and Issue: unknown

Published: Feb. 22, 2025

Abstract Triboelectric nanogenerators (TENGs) offer a promising solution for energy harvesting in wearable devices and sensors. However, their output is dependent on process parameters should be optimized to maximize performance. Due the absence of effective analytical models TENG systems, complex relationship among these variables effect cannot easily boiled down into conventional theoretical framework. To address this problem, study takes four such as thickness, pore ratio, applied force, frequency account leverages advanced design methods (e.g., Design Experiment) machine learning‐based regression systematically explore space. A contact‐separation has been designed that includes tribonegative porous layer graphene nanoplatelets (GNP) dispersed polydimethylsiloxane (PDMS) matrix aluminum tribopositive material. Several experiments are conducted train support vector regressor (SVR) model, validate predicted performance, refine can further used obtain an design.

Language: Английский

AI‐Driven TENGs for Self‐Powered Smart Sensors and Intelligent Devices

Advanced Science, Journal Year: 2025, Volume and Issue: unknown

Published: April 25, 2025

Abstract Triboelectric nanogenerators (TENGs) are emerging as transformative technologies for sustainable energy harvesting and precision sensing, offering eco‐friendly power generation from mechanical motion. They harness while enabling self‐sustaining sensing self‐powered devices. However, challenges such material optimization, fabrication techniques, design strategies, output stability must be addressed to fully realize their practical potential. Artificial intelligence (AI), with its capabilities in advanced data analysis, pattern recognition, adaptive responses, is revolutionizing fields like healthcare, industrial automation, smart infrastructure. When integrated TENGs, AI can overcome current limitations by enhancing output, stability, adaptability. This review explores the synergistic potential of AI‐driven TENG systems, optimizing materials embedding machine learning deep algorithms intelligent real‐time sensing. These advancements enable improved harvesting, predictive maintenance, dynamic performance making TENGs more across industries. The also identifies key future research directions, including development low‐power algorithms, materials, hybrid robust security protocols AI‐enhanced solutions.

Language: Английский