Phosphorylation Enables Nano‐Graphene for Tunable Artificial Synapses

Abstract Flexible and robust memristors with controllable resistance‐switching characteristics are important to neuromorphic computing. However, the nanomaterials‐based, solution‐processed resistance switching layer usually has poor reliability and tunability due to uneven morphology and invariable surface properties. Herein, phosphorylated graphene nanoflakes (phos‐GPs) are synthesized for high‐performance solution‐processed flexible memristors. In situ conductive atomic force microscopy reveals that the tightly stacked uniform nanoflakes and modified phosphorate groups jointly reduce the formation barrier of the conductive filaments. Furthermore, phosphorylation gives rise to surface silver ion coordination leading to enhanced radial growth of the conductive filaments. The memristor shows volatile characteristics in the Ag/phos‐GPs/ITO architecture and exhibits non‐volatile properties in the Ag/Ag + ‐(phos‐GPs)/ITO structure. Both types of memristors display consistent I‐ ‐ V curves during long‐term cycling and under repetitive mechanical bending, in addition to excellent synaptic plasticity. Moreover, ultrasmall nonlinearity is observed from non‐volatile long‐term synaptic potentiation and depression. By utilizing the tunable artificial synapses, the processes of memory‐forgetting and re‐recognition are simulated, and the image recognition tasks are accomplished by the artificial neural networks.

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Publication Details

Journal
Advanced Functional Materials
Published
2024-11-24
DOI
https://doi.org/10.1002/adfm.202416794
Citations
3
Primary Topic
Graphene research and applications
Type
article
Field-Weighted Citation Impact
0.35

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article

Phosphorylation Enables Nano‐Graphene for Tunable Artificial Synapses

Xue‐Feng Yu, Shanwu Ke, Yongyue Xiao, Yuanduo Qu et al.
3 citations
Advanced Functional Materials
Graphene research and applications
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article

Phosphorylation Enables Nano‐Graphene for Tunable Artificial Synapses

Xue‐Feng Yu, Shanwu Ke, Yongyue Xiao, Yuanduo Qu, Jiangrong Xiao, Siran Chen, Ziqiang Cheng, Paul Kim-Ho Chu, Zhenyu Zhang, Cong Ye, Jiahong Wang, Mengdi Hao, Hao Huang, Shuai Zhang
article en
3 citations

Abstract

Abstract Flexible and robust memristors with controllable resistance‐switching characteristics are important to neuromorphic computing. However, the nanomaterials‐based, solution‐processed resistance switching layer usually has poor reliability and tunability due to uneven morphology and invariable surface properties. Herein, phosphorylated graphene nanoflakes (phos‐GPs) are synthesized for high‐performance solution‐processed flexible memristors. In situ conductive atomic force microscopy reveals that the tightly stacked uniform nanoflakes and modified phosphorate groups jointly reduce the formation barrier of the conductive filaments. Furthermore, phosphorylation gives rise to surface silver ion coordination leading to enhanced radial growth of the conductive filaments. The memristor shows volatile characteristics in the Ag/phos‐GPs/ITO architecture and exhibits non‐volatile properties in the Ag/Ag + ‐(phos‐GPs)/ITO structure. Both types of memristors display consistent I‐ ‐ V curves during long‐term cycling and under repetitive mechanical bending, in addition to excellent synaptic plasticity. Moreover, ultrasmall nonlinearity is observed from non‐volatile long‐term synaptic potentiation and depression. By utilizing the tunable artificial synapses, the processes of memory‐forgetting and re‐recognition are simulated, and the image recognition tasks are accomplished by the artificial neural networks.

Advanced Functional MaterialsVol. 35(16)
East China Jiaotong University (CN), Wuhan Polytechnic University (CN), City University of Hong Kong (HK), Shenzhen University (CN), Chinese Academy of Sciences (CN), Southern University of Science and Technology (CN), Shenzhen Institutes of Advanced Technology (CN), Shenzhen Technology University (CN), University of Chinese Academy of Sciences (CN), Hubei University (CN)
City University of Hong Kong, National Natural Science Foundation of China, Youth Innovation Promotion Association of the Chinese Academy of Sciences, National Key Research and Development Program of China, Shenzhen Science and Technology Innovation Program, Basic and Applied Basic Research Foundation of Guangdong Province
Openalex Percentile: Top 43%
Graphene research and applications
0.35
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