Ångstrom-Scale-Channel Iontronic Memristors for Neuromorphic Computing

GH Xu and HY Cui and L Wang and ML Zhang and WC Liu and TT Mei and B Wu and CJ Wan and K Xiao, ACS APPLIED MATERIALS & INTERFACES, 17, 34659-34668 (2025).

DOI: 10.1021/acsami.5c01409

The signal transmission utilizing ions as carriers provides the brain with efficient and outstanding computational capability. The controllable ion transport in neurons relies on a multiplicity of ion channels, which have & aring;ngstrom (& Aring;) dimensions. The interactions between ions and channel walls contribute to their nonlinear ion transport behavior, which can be manifested as natural memory resistors, i.e., memristors. However, it remains a great challenge to understand the relationship between the nonlinear ion transport behaviors of different solid-state ionic memristors and artificial ion channel properties. Here, we present two different unipolar and bipolar ionic memristors based on artificial & Aring;-scale channels in polymeric membranes with varying surface charge densities. The resistive switching mechanism of ionic memristors is attributed to synergistic energy barriers arising from size exclusion and interactions between ions and channel walls. The synaptic functions were mimicked by configuring synaptic devices using ionic memristors, which can be used to stimulate artificial neural network algorithms for energy-efficient image recognition. Our results open a pathway for revealing the nonlinear dynamics of fluidic memristors based on artificial & Aring;-scale channels and realizing neuromorphic computation in aqueous media.

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