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Updated: Apr 14, 2026

Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
Published on: March 9, 2019
Polyoxometalates (POMs) Memristors/Neuromorphic Devices: From Structure Engineering to Material and Function
Jufang Hu1, Shengzhang Xu2, Yanfang Meng3
1Key Laboratory of Optoelectronic Devices and Systems of Ministry of Education and Guangdong Province, College of Physics and Optoelectronic Engineering, Shenzhen University, No. 3688, Nanhai Avenue, Nanshan District, Shenzhen 518060, China.
None:
The advancement of artificial intelligence and information technologies has presented higher demands on neuromorphic computing information devices, entailing the emergence of next-generation devices. Polyoxometalates (POMs) are emerging as promising molecular nanomaterials for next-generation neuromorphic computing, providing distinct advantages over conventional metal oxides. In contrast to bulk oxides that suffer from stochastic filament formation and device-to-device variability, POMs possess atomically precise structures with discrete, multi-electron redox states that enable highly reproducible and deterministic resistive switching. Their molecular nature allows for stable, multi-level data representation through stepwise reduction in metal centers (e.g., V, W, Mo) and the emulation of essential synaptic plasticity functions. Furthermore, the exceptional structural and chemical tunability of POMs favors covalent or supramolecular functionalization, enabling precise engineering of the POM-electrode interface and controlled self-assembly on surfaces. This molecular precision not only addresses the scalability challenges of traditional memristors but also unlocks unique functionalities, such as multimodal switching coupled with visible chromic response for state visualization. Taking the advantages of intermolecular crosstalk and countercation dynamics, POM-based networks offer a pathway toward constructing three-dimensional neuronal architectures, effectively connecting molecular redox chemistry to advanced high-density neuromorphic computing paradigms.
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