@article{Xu2025, 
author = {Nuo Xu and Yifei Wang and Ziwei Huo and Jinran Yu and Jiahong Yang and Zhong Lin Wang and Qijun Sun},
title = {Flexible and self-powered paper-based artificial synapse for neuromorphic computing and 3d information transmission},
year = {2025},
journal = {Nano Research Energy},
volume = {4},
pages = {e9120187},
keywords = {artificial synapse, triboelectric nanogenerators, self-powered, paper-based, neuromorphic device, logic gate},
url = {https://www.sciopen.com/article/10.26599/NRE.2025.9120187},
doi = {10.26599/NRE.2025.9120187},
abstract = {The advent of the Internet of Things (IoT) era has significantly accelerated advancements in neuromorphic computing research. Triboelectric nanogenerators (TENGs) exhibit dual functionality as both energy harvesters and synaptic simulators, facilitated by their inherent mechanoelectrical transduction properties and seamless circuit integration capabilities. In this work, we presented a vertically contact-separated paper-based artificial synaptic device employing TENG technology. The fabricated device successfully replicates fundamental synaptic behaviors, including paired-pulse facilitation (PPF), high-pass filtering characteristics, and spatiotemporal dynamic logic operations. Through optimized circuit configurations, we achieved elementary “NOT” logic gate using single devices, while implementing “AND/NAND” logic gates and “OR/NOR” logic gates operations through two- and three-device assemblies, respectively. Capitalizing on the mechanical flexibility and lightweight of paper substrates, we further developed a trilayer artificial synaptic architecture that mimics hierarchical neural information processing. This mechanoelectrical coupling approach establishes a novel paradigm for flexible neuromorphic systems, demonstrating exceptional potential for environmentally interactive robotics and adaptive wearable prosthetics.}
}