Hydrothermal Synthesis of Vinylidene-Linked Ionic Covalent Organic Frameworks for Robust Biomimetic Memristors

As a fundamental component of brain-like computing, the electrical performance and stability of organic memristors are intricately linked to both the preparation methods of the active layer film and the molecular structure of the materials used. This study reports the hydrothermal synthesis of a vinylidene-linked ionic covalent organic framework (iCOF) film on ITO substrates, utilizing copper trinuclear clusters and pyridinium salt ions as key building blocks. The resultant Al/ECu<sub>3</sub>-COF/ITO memristor demonstrates 32 distinct and stable conductance states, facilitated by the synergistic redox activity of copper trinuclear clusters and ionic charge compensation mechanisms. Remarkably, the device exhibits exceptional environmental stability with less than a 5.2% current variation under harsh acid/base conditions, alongside an outstanding conductivity uniformity of 99.92%. Furthermore, the memristor successfully emulates synaptic plasticity, replicating the biological "learning-forgetting" behavior, wherein repeated stimuli reinforce neuronal connectivity, leading to the transition from short-term to long-term memory. Capitalizing on these properties, we integrated the device into a convolutional neural network for image recognition, achieving 90% accuracy after 200 training epochs. This study not only advances the design of robust iCOF-based memristors but also provides a promising platform for the development of neuromorphic computing systems.

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Publication Details

Journal
ACS Applied Materials & Interfaces
Published
2025-08-05
DOI
https://doi.org/10.1021/acsami.5c13200
Primary Topic
Advanced Memory and Neural Computing
Type
article
Field-Weighted Citation Impact
0.00

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article

Hydrothermal Synthesis of Vinylidene-Linked Ionic Covalent Organic Frameworks for Robust Biomimetic Memristors

Fu‐Zhen Xuan, Tianyue Huang, Hao Wang, Bin Zhang et al.
ACS Applied Materials & Interfaces
Advanced Memory and Neural Computing
article

Hydrothermal Synthesis of Vinylidene-Linked Ionic Covalent Organic Frameworks for Robust Biomimetic Memristors

Fu‐Zhen Xuan, Tianyue Huang, Hao Wang, Bin Zhang, Zhao Jing
article en

Abstract

As a fundamental component of brain-like computing, the electrical performance and stability of organic memristors are intricately linked to both the preparation methods of the active layer film and the molecular structure of the materials used. This study reports the hydrothermal synthesis of a vinylidene-linked ionic covalent organic framework (iCOF) film on ITO substrates, utilizing copper trinuclear clusters and pyridinium salt ions as key building blocks. The resultant Al/ECu<sub>3</sub>-COF/ITO memristor demonstrates 32 distinct and stable conductance states, facilitated by the synergistic redox activity of copper trinuclear clusters and ionic charge compensation mechanisms. Remarkably, the device exhibits exceptional environmental stability with less than a 5.2% current variation under harsh acid/base conditions, alongside an outstanding conductivity uniformity of 99.92%. Furthermore, the memristor successfully emulates synaptic plasticity, replicating the biological "learning-forgetting" behavior, wherein repeated stimuli reinforce neuronal connectivity, leading to the transition from short-term to long-term memory. Capitalizing on these properties, we integrated the device into a convolutional neural network for image recognition, achieving 90% accuracy after 200 training epochs. This study not only advances the design of robust iCOF-based memristors but also provides a promising platform for the development of neuromorphic computing systems.

ACS Applied Materials & InterfacesVol. 17(33)
East China University of Science and Technology (CN), Advanced Energy Materials (United States) (US), Intelligent Health (United Kingdom) (GB)
Natural Science Foundation of Shanghai, National Natural Science Foundation of China, National Key Research and Development Program of China
Openalex Percentile: Top 82%
Advanced Memory and Neural Computing
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