Single Conical Nanopore and Anodized Aluminum Oxide Membrane Ionic Memristors
GANGLI WANG a
a Georgia State University, Department of Chemistry Georgia State University, Atlanta, United States
Proceedings of Neuronics Conference 2025 (Neuronics25)
Tsukuba, Japan, 2025 June 17th - 20th
Organizers: Takashi Tsuchiya, Chu-Chen Chueh, Sabina Spiga and Jung-Yao Chen
Invited Speaker, GANGLI WANG, presentation 030
Publication date: 15th April 2025

Memristors could mitigate the limitations of von Neumann architecture, empower brain-mimicking capabilities, and lower energy demand. However, advanced neuronal functions and long-term memory among other technical issues remain challenging to establish. Our group have adopted a combined experimental and simulation approach from fundamental nanoelectrochemistry perspective to study ion transport dynamics confined through ordered asymmetric nanogeometry and its defined nanointerfaces. This talk will introduce our recent findings based on two types of nanofluidic-type devices, both featuring broken symmetry along the transport trajectory. In the first system, we make individual conical quartz nanopipettes that can hold the same or different solutions inside and outside. As a scaled-up ensemble system, anodized aluminum oxide membranes containing arrays of nanochannels are synthesized as separators of two solutions. Characteristic memristor responses of pinched current–potential loops are resolved in voltammetric experiments and successfully reproduced through finite element simulation. Excitatory and inhibitory conductance states are shown to arise from the enrichment and depletion of mobile charge carriers, conveniently tuned by the pulse waveform. Negative differential resistance, a decrease in conductance with increasing potential, is shown to arise from the availability and redistribution of the ionic charges during the hysteretic and rectified transport at asymmetric nanointerfaces. Mechanistic understanding and controllable state-switching dynamics shine light on avenues to achieve higher-order complexity functions.

U.S. Department of Energy, Office of Science, Office of Basic Energy Sciences under Award Number DE-SC0024457 and FaCT EFRC under Award Number 3ERKCK61.

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