Issue 23, 2025

Solvated ion transport in hierarchical tremella-like ionic membranes for low-power and high-sensitivity ethanol sensing

Abstract

Biological olfactory perception relies on ionic transport, offering a promising alternative to conventional gas sensors that depend on electronic signal transmission, which often suffer from limitations such as limited sensitivity, high power consumption, and susceptibility to moisture. Inspired by biological olfactory ion channels, nanochannel-based ionic membranes incorporating 2D materials and ionic liquids have been developed. Through functional modifications, these membranes exhibit unique tremella-like structures that optimize gas diffusion pathways and provide effective gas interaction sites. The developed membranes demonstrate exceptional performance, including a low detection limit of 189 ppb, a remarkable sensitivity of 2.02% ppm−1 across 5–500 ppm, specific selectivity to ethanol, stable reversibility over 100 cycles, and ultralow power consumption of only 0.28 μW. Experimental and simulation results confirm that the enhanced sensing performance stems from solvated ion transport within nanoconfined channels. Notably, the membranes maintain detection efficiency under varying humidity conditions, demonstrating practical applicability in both food quality assessment (30–40% RH) and intoxicated driving monitoring (80–90% RH). It is envisioned that the deepened understanding of solvated ion transport within nanoconfined channels will advance the development of bioinspired olfactory perception and integrated sensing systems.

Graphical abstract: Solvated ion transport in hierarchical tremella-like ionic membranes for low-power and high-sensitivity ethanol sensing

Supplementary files

Article information

Article type
Communication
Submitted
30 Jun 2025
Accepted
25 Jul 2025
First published
28 Jul 2025

Mater. Horiz., 2025,12, 10091-10102

Solvated ion transport in hierarchical tremella-like ionic membranes for low-power and high-sensitivity ethanol sensing

L. Xu, H. Liu, Q. Song, X. Sun, Z. Zhao, Z. Ge, Y. Chen, X. Jiang and L. Jiang, Mater. Horiz., 2025, 12, 10091 DOI: 10.1039/D5MH01249J

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