Metal–Organic Framework Nanofluidic Synapse,Journal of the American Chemical Society

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Metal–Organic Framework Nanofluidic Synapse
Journal of the American Chemical Society ( IF 14.4 ) Pub Date : 2024-09-18 , DOI: 10.1021/jacs.4c08833
Si-Yuan Yu ₁ , Jin Hu ₁ , Zheng Li ₁ , Yi-Tong Xu ₁ , Cheng Yuan ₁ , Dechen Jiang ₁ , Wei-Wei Zhao ₁

Affiliation

Chemical synapse completes the signaling through neurotransmitter-mediated ion flux, the emulation of which has been a long-standing obstacle in neuromorphic exploration. Here, we report metal–organic framework (MOF) nanofluidic synapses in which conjugated MOFs with abundant ionic storage sites underlie the ionic hysteresis and simultaneously serve as catalase mimetics that sensitively respond to neurotransmitter glutamate (Glu). Various neurosynaptic patterns with adaptable weights are realized via Glu-mediated chemical/ionic coupling. In particular, nonlinear Hebbian and anti-Hebbian learning in millisecond time ranges are achieved, akin to those of chemical synapses. Reversible biochemical in-memory encoding via enzymatic Glu clearance is also accomplished. Such results are prerequisites for highly bionic electrolytic computers.

中文翻译：

金属有机框架纳米流体突触

化学突触通过神经递质介导的离子流完成信号传导，其模拟一直是神经形态探索中长期存在的障碍。在这里，我们报告了金属有机框架（MOF）纳米流体突触，其中具有丰富离子存储位点的共轭MOF构成了离子滞后现象，同时充当对神经递质谷氨酸（Glu）敏感响应的过氧化氢酶模拟物。通过 Glu 介导的化学/离子耦合实现具有适应性权重的各种神经突触模式。特别是，在毫秒时间范围内实现了非线性赫布和反赫布学习，类似于化学突触的学习。还可以通过酶促 Glu 清除实现可逆的生化内存编码。这些结果是高度仿生电解计算机的先决条件。

更新日期：2024-09-18

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