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  5. Graphene‐Based Oscillators for Biomimetic Neuro‐Interfaces

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Article
en
2025

Graphene‐Based Oscillators for Biomimetic Neuro‐Interfaces

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0 Files

en
2025
Vol 11 (15)
Vol. 11
DOI: 10.1002/aelm.202500219

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Konstantin ‘kostya’  Novoselov
Konstantin ‘kostya’ Novoselov

The University of Manchester

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Konstantin G. Nikolaev
Sergey Yu. Grebenchuk
Zhao Jinpei
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Abstract

Abstract Chemical oscillators—such as the Belousov‐Zhabotinsky reaction—have long served as model systems for studying non‐equilibrium chemical dynamics and as analogues of biological oscillations. However, many biological processes rely on out‐of‐equilibrium, often oscillatory, ionic fluxes that do not involve chemical reactions. Examples include action potentials in neurons, muscle contraction, cardiac rhythmicity, intracellular calcium signaling, and calcium wave oscillations. Despite these parallels, the development of biomimetic systems compatible with neuromorphic interfaces remains a significant challenge. Here, a strategy is demonstrated to organize oscillating ionic currents by developing ionic transistors composed of graphene oxide and polyelectrolyte, and assembling them into all‐ionic integrated circuits. By driving these systems out of equilibrium using external voltages, periodic motion of various ions across defined interfaces is achieved. This behavior, governed by local electric fields arising from unbalanced ionic concentrations, closely mimics biological excitability, such as that observed in neuronal and cardiac systems. These ionic transistors serve as a foundational building block for neuromorphic interfaces, offering a universal platform to emulate complex biological ionic processes with high fidelity.

How to cite this publication

Konstantin G. Nikolaev, Sergey Yu. Grebenchuk, Zhao Jinpei, Kou Yang, Yixin Zhang, Ong Mei Shan, V. A. Sorokin, Siyu Chen, Quan Wang, Jia Hui Bong, Konstantin ‘kostya’ Novoselov, Daria V. Andreeva (2025). Graphene‐Based Oscillators for Biomimetic Neuro‐Interfaces. , 11(15), DOI: https://doi.org/10.1002/aelm.202500219.

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Publication Details

Type

Article

Year

2025

Authors

12

Datasets

0

Total Files

0

Language

en

DOI

https://doi.org/10.1002/aelm.202500219

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