@article{Yang2026, 
author = {Xiangxiang Yang and Wei Li and Wenshan Qu and Lijuan Zhang and Meng Yuan and Junchuan Yang and Dongxue Li and Jiakuo Qiao and Zihan Guo and Jiawei Xie and Jinrong Zhang and Chao Yin and Kai Guo and Lin Bao and Zhixiang Gao and Lijuan Dong and Jinjin Zhao},
title = {A bioinspired neuromorphic tactile system: Merging MXene nanosheet sensors and MXene quantum dots memristor},
year = {2026},
journal = {Nano Research},
volume = {19},
number = {6},
pages = {94908337},
keywords = {memristor, sensor, MXene quantum dots, MXene nanosheet, neuromorphic tactile system},
url = {https://www.sciopen.com/article/10.26599/NR.2026.94908337},
doi = {10.26599/NR.2026.94908337},
abstract = {Inspired by biological sensory processing, this study presents a bioinspired neuromorphic tactile system that synergistically integrates an MXene nanosheet-based sensor and MXene quantum dots (MQDs) based memristor. The MXene-based sensor achieves multimodal perception of pressure, weight, curvature, roughness, and temperature through a comprehensive optimization strategy involving sensitivity enhancement via a vacuum-assisted approach, mechanical reinforcement by mixing sodium alginate (SA) to form composite networks, and environmental stabilization through polyimide encapsulation. The memristor based on MQDs exhibits digital and analog resistive switching behaviour with a high switching ratio and wide resistance range, enabling synaptic plasticity for data storage and computation. Feature signals from the sensor are directly processed by an artificial neural network constructed with these memristors. This tactile system demonstrates high accuracy (> 90%) in recognizing objects with different attributes. This work sets out a feasible approach towards the construction of a neuromorphic tactile system for human–machine interfaces, wearable electronics, and soft robotics.}
}