Chemical Research in Chinese Universities ›› 2026, Vol. 42 ›› Issue (5): 1408-1425.doi: 10.1007/s40242-026-6160-7

• Review Articles • Previous Articles     Next Articles

Bioinspired Nanofluidic Iontronics for Neuromorphic Computing

YANG Siqi1, GUO Ziwen2, ZHANG Mingshi1, HOU Yaqi3, HOU Xu1,2   

  1. 1. State Key Laboratory of Physical Chemistry of Solid Surfaces, College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, P. R. China;
    2. Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, P. R. China;
    3. The Institute of Flexible Electronics (IFE, Future Technologies), Xiamen University, Xiamen 361005, P. R. China
  • Received:2026-06-30 Accepted:2026-08-11 Online:2026-10-01 Published:2026-09-24
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (Nos. U24A20205, 52025132, 21621091, 22021001, 22121001, 52303380), the ‘111’ Project of China (Nos. B17027, B16029), and the New Cornerstone Science Foundation Through the XPLORER PRIZE.

Abstract: Bioinspired nanofluidic iontronics regulates ion transport in confined, hydrated environments to enable neuromorphic functions. Nanofluidic neuromorphic devices vary widely in materials, structures, transport mechanisms, and computational roles, complicating systematic comparison across platforms. Unlike existing reviews that primarily organize the field by material system, device structure, or individual synaptic-like behavior, this review establishes a mechanism-timescale-function framework that connects the physical origin, operation-relevant timescale, and computational role of ionic conductance responses. Within this framework, stable and transient conductance responses are treated not as separate device categories, but as functional regimes determined by their physical mechanism, temporal evolution behaviors, and computational roles. We first summarize the fundamental mechanisms that regulate ionic conductance under nanoconfinement, and then discuss the stable conductance responses for synaptic weight storage, the transient ionic conductance dynamics for temporal information processing, and the progression from individual devices to scalable iontronic circuits and systems. Finally, we highlight key challenges in mechanism attribution, reproducibility, benchmarking, interface design, and system integration, and outline future directions for reliable, function-oriented, and biointerfaced nanofluidic neuromorphic hardware.

Key words: Bioinspired nanofluidics, Nanofluidic iontronics, Neuromorphic computing, Ionic conductance dynamics, Iontronic integration