Confinement of ions within graphene oxide membranes enables neuromorphic artificial

• 成果类型: Article
• 署名作者: Zhang, Yuchun; Liu, Lin; Qiao, Yu; Yao, Tian; Zhao, Xing; Yan, Yong
• 署名单位: Chinese Academy of Sciences; National Center for Nanoscience & Technology, CAS; Chinese Academy of Sciences; University of Chinese Academy of Sciences, CAS; Shandong University of Technology; University of Science & Technology Beijing
• 刊物名称: PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA
• ISSN/ISSBN: 0027-10097
• DOI: 10.1073/pnas.2413060122
• 发表日期: 2025-07-15
• 关键词: memory; transistor

摘要

Introducing neuromorphic computing paradigms into taste-sensing technology will bring unprecedented opportunities for developing new hardware architectures with perceptual intelligence. Constructing the biomimetic gustatory system, however, remains a challenge due to the scarcity of suitable components operating under wet conditions. Here, we report that ion confinement within the layered graphene oxide membranes can be used to develop a memristive device capable of implementing both synaptic function and chemical sensing. The continuum model and ion dynamics characterizations demonstrate that interfacial adsorption-desorption slows down ion transport and leads to memristive behavior. Based on this nanofluidic device, we built an artificial gustatory system in the physiological environment, which can efficiently classify different flavors according to the reservoir computing algorithm. Our results suggest a paradigm for in-sensor computing in liquid.