Creating locally enriched single-atom catalysts via bio-inspired internalization process for interference-resistant NO2 detection in complex environments

Abstract Intelligent gas-sensing technology that accurately and stably identifies gas categories in complex atmospheres is critical for protecting public safety and environment. However, competing interactions among different gases on sensing surfaces can trigger interference even poison of sensors. Here, we present a bio-inspired atomic internalization process that produces locally enriched single Pt species, enabling highly selective and interference-resistant gas detection. By engineering Sn/C precursors as homologous receptors, Pt 3 Sn alloys are two-step redistributed into high-density single Pt species within regionalized SnO 2 surface. This structure constitutionally alters the distribution patterns of NO 2 molecules and thus delivers accurate NO 2 monitoring in multicomponent atmospheres and stable detection for over 550 days, surpassing state-of-the-art commercial devices. Further integrating characteristic-specific sensors into arrays, the resulting device further achieves 100% classification accuracy for single and mixed gases at ultralow cost. Moreover, we demonstrate an autonomous “cruise-monitoring” system by equipping the sensor on a robot to detect and identify NO 2 in real time. Our findings thus guide the accurate analysis and interference-resistant detection in complex gas mixtures.

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Publication Details

Journal
Nature Communications
Published
2026-08-20
DOI
https://doi.org/10.1038/s41467-026-76850-4
Primary Topic
Gas Sensing Nanomaterials and Sensors
Type
article
Field-Weighted Citation Impact
0.00

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article

Creating locally enriched single-atom catalysts via bio-inspired internalization process for interference-resistant NO2 detection in complex environments

Muyu Yan, Dengsong Zhang, Runfang Mao, Yongjie Shen et al.
Nature Communications
Gas Sensing Nanomaterials and Sensors
article

Creating locally enriched single-atom catalysts via bio-inspired internalization process for interference-resistant NO2 detection in complex environments

Muyu Yan, Dengsong Zhang, Runfang Mao, Yongjie Shen, Wenqiang Qu, Pengcheng Xu, Jiaqiang Xu, Zhenggang Xue, Zheng Wang, Musen Li, Xuesong Zhang, Hu Shan, Haotian Huang, Zhiheng Ma, Shuai Guo, Xin Jia, Yi Jiang
article en

Abstract

Abstract Intelligent gas-sensing technology that accurately and stably identifies gas categories in complex atmospheres is critical for protecting public safety and environment. However, competing interactions among different gases on sensing surfaces can trigger interference even poison of sensors. Here, we present a bio-inspired atomic internalization process that produces locally enriched single Pt species, enabling highly selective and interference-resistant gas detection. By engineering Sn/C precursors as homologous receptors, Pt 3 Sn alloys are two-step redistributed into high-density single Pt species within regionalized SnO 2 surface. This structure constitutionally alters the distribution patterns of NO 2 molecules and thus delivers accurate NO 2 monitoring in multicomponent atmospheres and stable detection for over 550 days, surpassing state-of-the-art commercial devices. Further integrating characteristic-specific sensors into arrays, the resulting device further achieves 100% classification accuracy for single and mixed gases at ultralow cost. Moreover, we demonstrate an autonomous “cruise-monitoring” system by equipping the sensor on a robot to detect and identify NO 2 in real time. Our findings thus guide the accurate analysis and interference-resistant detection in complex gas mixtures.

Nature Communications
Shanghai University (CN), University of Toronto (CA), Hokkaido University (JP), Shanghai Normal University (CN), Huzhou Normal University (CN), State Key Laboratory of Transducer Technology (CN), Shanghai Institute of Microsystem and Information Technology (CN)
National Science Foundation, National Natural Science Foundation of China
Openalex Percentile: Top 18%
Gas Sensing Nanomaterials and Sensors
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