Reversible Charge Inversion Enables Field-Programmable Nanofluidic Memristor and Synapse for Neuromorphic Applications.

Abstract

Memristors, whose conductance depends on their past electrical history, are the foundation of emerging brain-inspired artificial computing architectures. Here, we demonstrate a unipolar memristor in which both ionic conductance and electroosmotic flow exhibit pronounced hysteresis, enabling dual-mode memory in charge and water transport. Strikingly, this behavior emerges without structural asymmetry or chemical modification. Instead, it originates from a novel mechanism, which involves a reversible transition in a nanoconfined system driven by charge inversion, where counterions overcompensate surface charge. This transition marks a boundary between two distinct electrostatic states in response to an applied electric field. We harness this unique mechanism to emulate synaptic plasticity and implement learning and classification in artificial neural networks and convolutional models. These findings establish a new class of field-tunable aqueous platforms, unlocking opportunities in neuromorphic logic, adaptive computing, biointerfacing, and real-time environmental sensing.