Related Experiment Video
Updated: Jun 17, 2026
11:13
Creating Sub-50 Nm Nanofluidic Junctions in PDMS Microfluidic Chip via Self-Assembly Process of Colloidal Particles
Published on: March 13, 2016
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
Summary
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.
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.
