Pain-Perception-Inspired Neuromorphic Sensing: Nonmonotonic Electrical Response and Spatial Visualization in a Near-Infrared-Responsive MXene Hydrogel

Neuromorphic sensory devices that encode environmental stimuli at the sensing interface offer a route toward more adaptive machine-environment interactions. Ionic hydrogels provide mechanically compliant, ion-conducting hydrated networks that can incorporate photothermal components. However, many near-infrared (NIR)-responsive hydrogels show conductivity changes in one direction, while terminal electrical signals provide little information about stimulus location. Achieving response-direction reversal and spatially resolved visual feedback in one soft material using a single NIR input, therefore, remains challenging. Inspired by protective inhibition and post-stimulus rebound in pain-related neural signaling, we developed an NIR neuromorphic sensory device based on a Ca-PAA-EMB@IL-MXene hydrogel. The material couples photothermal conversion by IL-MXene with temperature-dependent ion transport in a hydrated Ca2+-coordinated PAA-EMB network. Weak irradiation transiently enhances conductivity, whereas stronger or prolonged irradiation drives the conductivity through a maximum into a suppressed state, followed by rebound and recovery after stimulus removal. Thermal, electrical, optical, and water-state measurements are consistent with heat-induced water redistribution and reorganization of the hydrated network, with the characteristic transition temperatures regulated by the EMB content. Programmed NIR pulses further produce paired-pulse facilitation-like behavior, signal accumulation, and responses dependent on recent stimulation history. Localized irradiation induces reversible whitening in the exposed region, supplementing the overall electrical signal with spatial information. An impedance-acquisition and microcontroller interface converts these responses into initial, strong-stimulus warning, and recovery states. This work provides a soft-material strategy for encoding NIR stimulus intensity, duration, history, and location through coupled electrical and optical outputs, supporting NIR-responsive neuromorphic sensing and warning interfaces.