Fingertip-chip sensor based on Pd nanocluster sensitized 3D NiO nanotube arrays for real-time, selective methane detection

Abstract

The selective detection of methane (CH4) at trace levels is essential for applications such as mining safety and natural gas leak detection. However, achieving high selectivity and sensitivity remains a significant challenge due to interference from gases like hydrogen sulfide (H2S) and carbon monoxide (CO). In this study, we present a novel fingertip-chip sensor that combines palladium (Pd) nanoclusters with three-dimensional (3D) nickel oxide (NiO) nanotube arrays for highly selective and sensitive CH4 detection. The 3D NiO structure offers a large surface area that enhances CH4 adsorption, while the Pd nanoclusters serve as catalytic sites, improving the interaction between CH4 molecules and the NiO surface. Fabricated via atomic layer deposition (ALD), the sensor demonstrates an ultra-low detection limit of 70 parts per billion (ppb) and exceptional selectivity, with a response ratio greater than 10 for CH4 relative to common interferents such as H2S and CO. Comprehensive evaluations of the sensor's sensitivity, stability, and performance under varying environmental conditions confirm its potential for real-time monitoring. Integrated into a wireless fingertip-chip system, the sensor enables seamless, remote CH4 monitoring in dynamic and challenging environments, such as mining sites and natural gas pipelines. This work presents a scalable approach for next-generation safety gas sensors, enhancing both detection sensitivity and real-time applicability in industrial and environmental monitoring.

Graphical abstract: Fingertip-chip sensor based on Pd nanocluster sensitized 3D NiO nanotube arrays for real-time, selective methane detection

Supplementary files

Article information

Article type
Paper
Submitted
10 Jun 2025
Accepted
28 Jul 2025
First published
07 Aug 2025

Lab Chip, 2025, Advance Article

Fingertip-chip sensor based on Pd nanocluster sensitized 3D NiO nanotube arrays for real-time, selective methane detection

K. Yang, Y. Kang, J. Yan, W. Fang, J. Zhang and Z. Song, Lab Chip, 2025, Advance Article , DOI: 10.1039/D5LC00570A

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