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通过金属有机框架纳米通道中的非线性离子传输构建超级电容器-忆阻器。

Constructing a supercapacitor-memristor through non-linear ion transport in MOF nanochannels.

作者信息

Tang Pei, Jing Pengwei, Luo Zhiyuan, Liu Kekang, Zhao Xiaoxi, Lao Yining, Yao Qianqian, Zhong Chuyi, Fu Qingfeng, Zhu Jian, Liu Yanghui, Dou Qingyun, Yan Xingbin

机构信息

Department of Materials Science and Engineering, Sun Yat-sen University, Guangzhou 510275, China.

School of Materials, Sun Yat-sen University, Shenzhen 518107, China.

出版信息

Natl Sci Rev. 2024 Sep 11;11(10):nwae322. doi: 10.1093/nsr/nwae322. eCollection 2024 Oct.

Abstract

The coexistence and coupling of capacitive and memristive effects have been an important subject of scientific interest. While the capacitive effect in memristors has been extensively studied, the reciprocal scenario of the memristive effect in capacitors remains unexplored. In this study, we introduce a supercapacitor-memristor (CAPistor) concept, which is constructed by leveraging non-linear ion transport within the pores of a metal-organic framework zeolitic-imidazolate framework (ZIF-7). Within the nanochannels of the ZIF-7 electrode in an aqueous pseudocapacitor, the anionic species (OH) of the electrolyte can be enriched and dissipated in different voltage regimes. This difference leads to a hysteresis effect in ion conductivity, constituting a memristive behavior in the pseudocapacitor. Thus, the pseudocapacitor-converted CAPistor seamlessly integrates the programmable resistance and memory functions of an ionic memristor into a supercapacitor, demonstrating enormous potential to extend the traditional energy storage applications of supercapacitors into emerging fields, including biomimetic nanofluidic ionics and neuromorphic computing.

摘要

电容效应和忆阻效应的共存与耦合一直是科学研究的重要课题。虽然忆阻器中的电容效应已得到广泛研究,但电容器中忆阻效应的反向情况仍未被探索。在本研究中,我们引入了一种超级电容器-忆阻器(CAPistor)概念,它是通过利用金属有机框架沸石咪唑酯框架(ZIF-7)孔隙内的非线性离子传输构建而成。在水性赝电容器中ZIF-7电极的纳米通道内,电解质的阴离子物种(OH)在不同电压状态下会富集和消散。这种差异导致离子电导率出现滞后效应,在赝电容器中构成忆阻行为。因此,由赝电容器转换而来的CAPistor将离子忆阻器的可编程电阻和记忆功能无缝集成到超级电容器中,显示出将超级电容器传统的能量存储应用扩展到新兴领域的巨大潜力,包括仿生纳米流体离子学和神经形态计算。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/57b2/11462086/b24d615383b5/nwae322fig1.jpg

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