Single-Atom Ni-Modified SnO₂ for Ultrasensitive NO₂ Gas Sensing through Enhanced Molecular Adsorption and Efficient Charge Transfer.

Authors

Yuting Yang, Jiarui Zhu, Yihong Zhong, Hao Zhang, Wanqing Yu, Yangyang Hao, Yuan Pan, Bin Song, Hao Yang, Tao Cheng, Xuhui Sun

Published in

ACS sensors. Jun 29, 2026. Epub Jun 29, 2026.

Abstract

Precisely introducing single-atom sites into metal oxide semiconductors (MOS) is essential for achieving ultrasensitive and selective detection of trace gases; however, the structure-dependent role of these atomically dispersed sites remains insufficiently understood. Here, we report a defect-assisted strategy to construct Ni-modified SnO₂ with tunable surface Ni dispersion, enabling a direct correlation between Ni coordination structure and NO₂ sensing behavior. Hydrogen treatment of SnO₂ (H-SnO₂) produces abundant surface defects that stabilize isolated Ni atoms through Ni-O-Sn coordination. Aberration-corrected scanning transmission electron microscopy and X-ray absorption spectroscopy unambiguously confirm the formation of atomically dispersed Ni sites and their aggregation into NiO_x clusters at higher loading. Among them, the single-atom-dominated Ni/H-SnO₂ sensor exhibits an ultrahigh response of 13,152 toward 1 ppm NO₂ at 175 °C, together with excellent selectivity, good long-term stability, and a detection limit down to 10 ppb. Experimental results combined with density functional theory calculations reveal that the superior sensing performance originates from the synergy of strong and selective NO₂ adsorption and efficient charge transfer via the single-atom Ni-O-Sn bonding structure. In contrast, the formation of NiO_x clusters weakens NO₂ adsorption and deteriorates charge-transfer efficiency, leading to reduced sensing performance. This work identifies the single-atom Ni-O-Sn bonding as the decisive active structure for ultrasensitive NO₂ detection and provides an atomic-level bonding engineering strategy for high-performance MOS-based gas sensors.

PMID:
42372088
Bibliographic data and abstract were imported from PubMed on 30 Jun 2026.

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