Light-Modulated Xylan-Reinforced Nanofluidic Memristor for Ionic Neural Network-Based Robot Movement Modulation

The balance between excitatory and inhibitory (E/I) signaling underpins complex neural functions in biological systems. However, replicating such ion-mediated regulation with biobased materials in artificial systems remains challenging. Herein, we demonstrate two distinct light-modulated 2D nanofluidic memristors based on paper-mill waste (xylan) reinforced membranes that emulate complementary E/I synaptic signaling, enabling precise robotic motion control via ionic neural networks. The memristors were constructed using xylan-reinforced MXene membranes with asymmetric electrolytes, leveraging the interfacial interactions between the functional groups of xylan derivatives and MXene to achieve nanofluidic membrane assembly and precise control over surface charge and ion selectivity. Upon illumination, the photothermal effect of MXene induces a uniform thermal field that enables thermally activated ion transport in the interlayer spacing, allowing these biomass-based memristors to emulate key excitatory and inhibitory synaptic behaviors. These complementary memristors further implement reconfigurable Boolean logic operations and serve as foundational components for ionic circuits, as demonstrated in series and parallel configurations. As a proof-of-concept, an E/I-integrated ionic neural network achieved precise control of ten robotic motion modes by tuning light pulses, concentration gradients, and ion selectivity. This work highlights the potential of biomass-reinforced materials for nanofluidic memristors and explores new frontiers in the application of biomass materials.