Mechano‐Driven Neuromimetic Logic Gates Established by Geometrically Asymmetric Hydrogel Iontronics

The human brain's neural network demonstrates exceptional efficiency in information processing and recognition, driving advancements in neuromimetic devices that emulate neuronal functions such as signal integration and parallel transmission. A key challenge remains in replicating these functions while minimizing energy consumption. Here, inspired by neuronal signal integration and axonal bidirectional transmission, mechano-driven hydrogel logic gates leveraging the piezoionic effect is presented, offering a novel bionic approach with significantly reduced power consumption. By exerting external force on the thick and thin sides of the geometrically asymmetric hydrogel, spike signals of differing amplitudes and opposite polarities can be generated, corresponding to '1' and '0', respectively. The differential mobility of anions and cations plays a crucial role in the piezoionic effect. This geometric asymmetry amplifies ion convection, improving force-to-electricity conversion efficiency, while the inclusion of salts with varying ion size can further enhance this disparity, even reversing the signal direction. Arranging asymmetric hydrogel iontronics in series-parallel configurations enables the emulation of complex neuronal logic operations, facilitating ionic spike signal addition and subtraction. This hydrogel-based logic control has been directly applied in human-machine interaction to control robot arms and offers significant potential for the advancement of artificial intelligence, robotics, and wearable technologies.

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Publication Details

Journal
Small
Published
2025-03-06
DOI
https://doi.org/10.1002/smll.202409998
Citations
10
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
7.30

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article

Mechano‐Driven Neuromimetic Logic Gates Established by Geometrically Asymmetric Hydrogel Iontronics

Yan Du, Di Wei, Zhong Lin Wang, Yuyang Zhang et al.
10 citations
Small
Advanced Memory and Neural Computing
7.30
article

Mechano‐Driven Neuromimetic Logic Gates Established by Geometrically Asymmetric Hydrogel Iontronics

Yan Du, Di Wei, Zhong Lin Wang, Yuyang Zhang, Li Xiang, Yaowen Ouyang
article en
10 citations

Abstract

The human brain's neural network demonstrates exceptional efficiency in information processing and recognition, driving advancements in neuromimetic devices that emulate neuronal functions such as signal integration and parallel transmission. A key challenge remains in replicating these functions while minimizing energy consumption. Here, inspired by neuronal signal integration and axonal bidirectional transmission, mechano-driven hydrogel logic gates leveraging the piezoionic effect is presented, offering a novel bionic approach with significantly reduced power consumption. By exerting external force on the thick and thin sides of the geometrically asymmetric hydrogel, spike signals of differing amplitudes and opposite polarities can be generated, corresponding to '1' and '0', respectively. The differential mobility of anions and cations plays a crucial role in the piezoionic effect. This geometric asymmetry amplifies ion convection, improving force-to-electricity conversion efficiency, while the inclusion of salts with varying ion size can further enhance this disparity, even reversing the signal direction. Arranging asymmetric hydrogel iontronics in series-parallel configurations enables the emulation of complex neuronal logic operations, facilitating ionic spike signal addition and subtraction. This hydrogel-based logic control has been directly applied in human-machine interaction to control robot arms and offers significant potential for the advancement of artificial intelligence, robotics, and wearable technologies.

SmallVol. 21(16)
Georgia Institute of Technology (US), Chinese Academy of Sciences (CN), University of Cambridge (GB), University of Manchester (GB), Beijing Institute of Nanoenergy and Nanosystems (CN), University of Chinese Academy of Sciences (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 3%
Advanced Memory and Neural Computing
7.30
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