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Nano Research 2024, 17(2): 503-514 https://doi.org/10.1007/s12274-023-5900-y
Review Article | Issue | Published: 14 July 2023

Bioinspired nanofluidic iontronics for brain-like computing

Show Author's Information Lejian Yu1,§,  Xipeng Li1,6,§,  Chunyi Luo1,  Zhenkang Lei2,  Yilan Wang1,  Yaqi Hou5( ),  Miao Wang4( ),  Xu Hou1,2,3,7,8( )
State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China
Department of Physics, Research Institute for Biomimetics and Soft Matter, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Jiujiang Research Institute, College of Physical Science and Technology, Xiamen University, Xiamen 361005, China
The Higher Educational Key Laboratory for Biomedical Engineering of Fujian Province, Department of Biomaterials, Research Center of Biomedical Engineering of Xiamen, College of Materials, Xiamen University, Xiamen 361005, China
The Institute of Flexible Electronics (IFE, Future Technologies), Xiamen University, Xiamen 361005, China
Binzhou Institute of Technology, Binzhou 256600, China
Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361102, China
Engineering Research Center of Electrochemical Technologies of Ministry of Education, Xiamen University, Xiamen 361005, China

§ Lejian Yu and Xipeng Li contributed equally to this work.

Keywords:
ion transport, nanofluidics, human brain, brain-like computing, memristive effect
Topics:
Nanostructures
Cite this article:
Yu L, Li X, Luo C, et al. Bioinspired nanofluidic iontronics for brain-like computing. Nano Research, 2024, 17(2): 503-514. https://doi.org/10.1007/s12274-023-5900-y
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Abstract Full text About this article

The human brain performs computations via a highly interconnected network of neurons. Taking inspiration from the information delivery and processing mechanism of the human brain in central nervous systems, bioinspired nanofluidic iontronics has been proposed and gradually engineered to overcome the limitations of the conventional electron-based von Neumann architecture, which shows the promising potential to enable efficient brain-like computing. Anomalous and tunable nanofluidic ion transport behaviors and spatial confinement show promising controllability of charge carriers, and a wide range of structural and chemical modification paves new ways for realizing brain-like functions. Herein, a comprehensive framework of mechanisms and design strategy is summarized to enable the rational design of nanofluidic systems and facilitate the further development of bioinspired nanofluidic iontronics. This review provides recent advances and prospects of the bioinspired nanofluidic iontronics, including ion-based brain computing, comprehension of intrinsic mechanisms, design of artificial nanochannels, and the latest artificial neuromorphic functions devices. Furthermore, the challenges and opportunities of bioinspired nanofluidic iontronics in the pioneering and interdisciplinary research fields are proposed, including brain–computer interfaces and artificial neurons.


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About this article

Bioinspired nanofluidic iontronics for brain-like computing

Show Author's information Lejian Yu1,§, Xipeng Li1,6,§, Chunyi Luo1, Zhenkang Lei2, Yilan Wang1, Yaqi Hou5( ), Miao Wang4( ), Xu Hou1,2,3,7,8( )
State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China
Department of Physics, Research Institute for Biomimetics and Soft Matter, Fujian Provincial Key Laboratory for Soft Functional Materials Research, Jiujiang Research Institute, College of Physical Science and Technology, Xiamen University, Xiamen 361005, China
The Higher Educational Key Laboratory for Biomedical Engineering of Fujian Province, Department of Biomaterials, Research Center of Biomedical Engineering of Xiamen, College of Materials, Xiamen University, Xiamen 361005, China
The Institute of Flexible Electronics (IFE, Future Technologies), Xiamen University, Xiamen 361005, China
Binzhou Institute of Technology, Binzhou 256600, China
Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (IKKEM), Xiamen 361102, China
Engineering Research Center of Electrochemical Technologies of Ministry of Education, Xiamen University, Xiamen 361005, China

§ Lejian Yu and Xipeng Li contributed equally to this work.

Abstract

The human brain performs computations via a highly interconnected network of neurons. Taking inspiration from the information delivery and processing mechanism of the human brain in central nervous systems, bioinspired nanofluidic iontronics has been proposed and gradually engineered to overcome the limitations of the conventional electron-based von Neumann architecture, which shows the promising potential to enable efficient brain-like computing. Anomalous and tunable nanofluidic ion transport behaviors and spatial confinement show promising controllability of charge carriers, and a wide range of structural and chemical modification paves new ways for realizing brain-like functions. Herein, a comprehensive framework of mechanisms and design strategy is summarized to enable the rational design of nanofluidic systems and facilitate the further development of bioinspired nanofluidic iontronics. This review provides recent advances and prospects of the bioinspired nanofluidic iontronics, including ion-based brain computing, comprehension of intrinsic mechanisms, design of artificial nanochannels, and the latest artificial neuromorphic functions devices. Furthermore, the challenges and opportunities of bioinspired nanofluidic iontronics in the pioneering and interdisciplinary research fields are proposed, including brain–computer interfaces and artificial neurons.

Keywords: ion transport, nanofluidics, human brain, brain-like computing, memristive effect

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Publication history
Copyright
Acknowledgements

Publication history

Received: 11 May 2023
Revised: 05 June 2023
Accepted: 06 June 2023
Published: 14 July 2023
Issue date: February 2024

Copyright

© Tsinghua University Press 2023

Acknowledgements

Acknowledgement

This work was supported by the National Natural Science Foundation of China (Nos. 21975209, 52273305, 22205185, 52025132, T2241022, 21621091, 22021001, and 22121001), the 111 Project (Nos. B17027 and B16029), the National Science Foundation of Fujian Province of China (No. 2022J02059), the Science and Technology Projects of Innovation Laboratory for Sciences and Technologies of Energy Materials of Fujian Province (No. RD2022070601), and the Tencent Foundation (The XPLORER PRIZE). The authors thank Yeyun Chen and Xuelian Yang for beneficial discussion.

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