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Transmembrane transport characterization across ionic redox transistors using surface-tracked scanning ion conductance microscopy 被引量:1

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摘要 A fundamental understanding of ion transport at the nanoscale is critical to the development of efficient chemical separation membranes,catalysts,ionic/bio-inspired materials,and its scale up into multi-functional ionic devices.Electrochemical imaging using scanning probe microscopy hardware has provided a method to visualize and understand processes that occur at the surface of ionic active materials.The suite of scanning probe microscopy techniques developed over the last few years are limited to imaging surface-level phenomena and have not been applied to investigate transmembrane properties of synthetic and natural membranes with high spatial and temporal resolution.In this article,we demonstrate the application our recently developed‘surface-tracked scanning ion conductance microscopy’technique to characterize voltage-regulated ion transport in an ionic redox transistor.The ionic redox transistor exhibits controlled transmembrane ion transport as a function of its electrochemical redox state.The technique presented in this article uses shear force measured between the nanopipette and ionic substrate to image topography of the porous substrate and simultaneously characterize topography-correlated transmembrane transport through the ionic redox transistor.The transmembrane conductance measured across an array of pores within the ionic redox transistor varies from 0.004μS/cm(OFF state)to 0.015μS/cm(ON state).We anticipate that the spatial correlation of transmembrane ion transport in the ionic redox transistor would result in a scale up into smart membrane separators for energy storage,neuromorphic circuits,and desalination membranes.
出处 《Advanced Sensor and Energy Materials》 2022年第3期17-23,共7页 先进传感器与能源材料(英文)
基金 Financial support was provided by the member organizations of the Smart Vehicle Concepts Center,a Phase III National Science Foundation Industry-University Cooperative Research Center(www.SmartVehicleC enter.org)under grant NSF IIP 1738723.
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