Confinement of ions within graphene oxide membranes enables neuromorphic artificial gustation

Sep 29, 2026

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Neuromorphic taste sensing is pursued by combining sensory and computing functions within liquid-compatible hardware. This work reports a memristive device formed from ion confinement in layered graphene oxide membranes, where continuum modeling and ion-dynamics measurements show that interfacial adsorption–desorption slows ion transport and induces memristive behavior. Using this nanofluidic platform, the authors build a reservoir-computing artificial gustatory system in physiological conditions to classify sweet, salty, bitter, and sour flavors, outlining a paradigm for in-sensor computing in liquids.

LanguageEnglish

CategoryResearch & Report

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Pages9

Reading Time23 min

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Confinement of ions within graphene oxide membranes enables neuromorphic artificial gustation Yuchun Zhanga,1, Lin Liua, b,1, Yu Qiaoc,1, Tian Yaoa, Xing Zhaoa , and Yong Yana, b,d,2 Affiliations are included on p. 9. Edited by Risto Nieminen, Aalto-yliopisto Teknillisen fysiikan laitos, Espoo, Finland; received June 29, 2024; accepted April 25, 2025 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. nanofluidic synapse | chemical sensing | graphene oxide membrane | interfacial adsorption–desorption | artificial gustatory system Separating sensory elements and computing units usually leads to time latency, low processing efficiency, and high power consumption; thereby, considerable efforts have recently been devoted to integrating both functions to achieve neuromorphic perceptual intelligence (1, 2) . Due to the rapid development of artificial neural algorithms and neuromorphic devices, a large number of integrated systems have been created to mimic biological visual (3, 4), auditory (5), tactual (6), olfactory (7), and gustatory (8–11) systems, displaying wide-range applications from machine vision (4) to motion recognition (12) and health monitoring (13) . Most of these hardware architectures are based on solid-state building blocks, which deal with physical signals via electrons and cannot respond directly to chemical stimuli in aqueous environments (14, 15) . The lack of suitable fluidic neuromorphic components would impact the design of fluid-based intelligent systems, for example, the bioinspired gustatory system, which needs to operate under wet conditions. To address this problem, current artificial gustatory architectures usually employ the ionic component to sense while the electronic counterpart to process. Although they succeeded in emulating taste perception for salt concentration and different flavors (8, 9), significant disparities in materials and structures of separated modules prevent their further integration. In addition, the biological gustatory system relies purely on the dynamics of ionic charges under physiological conditions, and therefore, to fully “replicate” the functions of the human taste, it is indispensable to develop neuromorphic components and systems by manipulating the ions in the fluidic circumstances (16–18) . With delicate control of interfacial interactions and/or ion concentration distributions within confined nanofluidic space (19), diverse ionic devices such as sensors (20, 21), rectifiers/diodes (22, 23), transistors (24), and their assembled circuits have been developed (23, 25). Nonetheless, these ionic components face great challenges in performing neuromorphic computing. Until very recently, several studies have observed the resistive-switching memristive phenomenon in confined nanofluidic channels (26–28) . For example, the analytical theory predicts that the ionic charges will assemble into elongated clusters in the quasi–two–dimensional slits, which leads to nonlinear ion transport and hysteretic conduction (26). In the active carbon (sub)nanofluidic channels, the inte

FAQs

What mechanism produces the memristive behavior in the graphene oxide device?

Interfacial adsorption–desorption slows ion transport when ions are confined in layered graphene oxide membranes, leading to memristive behavior.

How is the artificial gustatory system implemented and tested?

A reservoir-computing gustatory system is built from the nanofluidic device and operated in a physiological environment to classify different flavors.

Why is integrating sensory and computing functions challenging in existing taste-sensing hardware?

Many architectures separate sensing and electronic processing, causing latency and reducing efficiency, and solid-state components cannot directly respond to chemical stimuli in aqueous conditions.

Sep 29, 2026

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