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Assembly and Characterization of Biomolecular Memristors Consisting of Ion Channel-doped Lipid Membranes
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Light-Modulated Xylan-Reinforced Nanofluidic Memristor for Ionic Neural Network-Based Robot Movement Modulation.

Guanghui Song1, Hao Zhou1, Zehui Li1

  • 1Beijing Key Laboratory of Lignocellulosic Chemistry, Beijing Forestry University, Beijing, China.

Advanced Materials (Deerfield Beach, Fla.)
|May 21, 2026
PubMed
Summary
This summary is machine-generated.

Researchers developed light-modulated memristors from biomass waste, mimicking brain signaling for robotic control. These devices emulate excitatory and inhibitory functions, paving the way for advanced ionic neural networks.

Keywords:
biomass‐based membraneion‐based neural networknanofluidicsynapse functionxylan

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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.