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  1. Home
  3. Nanofluidic Systems For Ionic Intelligence.
  1. Home
  3. Nanofluidic Systems For Ionic Intelligence.

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Nanofluidic systems for ionic intelligence.

Makusu Tsutsui1, René van Roij2, Ye Yuan3

  • 1SANKEN, The University of Osaka, 8-1 Mihogaoka, Ibaraki, Osaka 567-0047, Japan. tsutsui@sanken.osaka-u.ac.jp.

Nanoscale Horizons
|June 16, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Ionic memristors leverage ion flow for low-energy computing, mimicking neural processes. This review explores fluidic and solid-state approaches for developing advanced ionic intelligence technologies.

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Area of Science:

  • Materials Science
  • Neuroscience
  • Computer Engineering

Background:

  • Artificial intelligence (AI) faces power and heat constraints.
  • Neural computation offers low-energy solutions using ionic flows.
  • Ion transport dynamics are key to biological information processing.

Purpose of the Study:

  • To review the emerging field of fluidic ionic memristors.
  • To explore bioinspired and solid-state architectures for ionic computing.
  • To identify requirements for scalable ionic intelligence technologies.

Main Methods:

  • Investigating ion-transport dynamics in various fluidic and solid-state systems.
  • Analyzing materials like lipid bilayers, ionic polymers, and nanofluidic architectures.
  • Examining hybrid approaches integrating biological components and microfabrication.

Main Results:

  • Demonstrated memory and learning capabilities in fluidic systems using ions.
  • Showcased history-dependent transport in solid-state nanofluidic devices.
  • Highlighted the coupling of electrical activity with chemistry and mechanics for plasticity.

Conclusions:

  • Fluidic ionic memristors offer a path towards energy-efficient AI.
  • Both soft, bioinspired and rigid, inorganic architectures show promise.
  • Scalable integration is crucial for realizing ionic intelligence technologies.