神经形态工程学
纳米技术
材料科学
纳米材料
仿真
可扩展性
计算机科学
超分子化学
记忆电阻器
电阻随机存取存储器
纳米尺度
功能(生物学)
纳米孔
纳米电子学
金属有机骨架
价(化学)
人工神经网络
分子间力
压缩传感
锡
智能材料
超材料
计算机体系结构
作者
Jufang Hu,Shengzhang Xu,Yan Meng
出处
期刊:Nanomaterials
[Multidisciplinary Digital Publishing Institute]
日期:2026-03-31
卷期号:16 (7): 425-425
被引量:2
摘要
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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