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

Area of Science:

• Materials Science
• Neuroscience
• Biotechnology

Background:

• The balance between excitatory and inhibitory (E/I) signaling is crucial for neural functions.
• Replicating ion-mediated regulation with biobased materials in artificial systems is challenging.
• Xylan, a paper-mill waste product, offers potential as a sustainable biomaterial.

Purpose of the Study:

• To develop light-modulated 2D nanofluidic memristors using biomass-reinforced membranes.
• To emulate complementary excitatory and inhibitory synaptic signaling for artificial neural networks.
• To demonstrate precise robotic motion control using these biobased memristors.

Main Methods:

• Constructed memristors using xylan-reinforced MXene membranes with asymmetric electrolytes.
• Utilized interfacial interactions between xylan derivatives and MXene for nanofluidic assembly.
• Leveraged the photothermal effect of MXene for light-modulated ion transport and synaptic emulation.

Main Results:

• Demonstrated two distinct light-modulated nanofluidic memristors emulating E/I synaptic signaling.
• Achieved reconfigurable Boolean logic operations and implemented ionic circuits.
• Successfully controlled ten robotic motion modes using an E/I-integrated ionic neural network.

Conclusions:

• Biomass-reinforced materials, specifically xylan-MXene membranes, are suitable for advanced nanofluidic memristors.
• Light-modulated memristors can emulate biological synaptic functions, enabling ionic neural networks.
• This work opens new avenues for utilizing biomass materials in neuromorphic computing and robotics.