Real-Time Gas Identification at Room Temperature Using UV-Modulated Sb-Doped SnO Sensors via Machine Learning.

Journal: ACS sensors
Published Date: Jul 2, 2025

Abstract

This study presents a novel approach for real-time gas identification at room temperature. We use UV-modulated Sb-doped SnO sensors combined with machine learning. Our method exclusively employs the gas response () as the sole metric. This eliminates the need for time-dependent parameters such as response and recovery times. By modulating the UV light intensity at five distinct levels (5, 10, 15, 20, and 30 mW/cm), we generate a five-dimensional optical fingerprint. This fingerprint captures subtle variations in sensor response under different illumination conditions. Gas discrimination was evaluated for both oxidizing gases (O and NO) and reducing gases (NH and H). Our machine learning results show that Support Vector Machine (SVM) and K-Nearest Neighbors (KNN) achieve nearly 100% accuracy when four UV intensity levels are used. Using as the sole input metric allows for instantaneous response detection, which is essential for real-time gas identification. This approach addresses the limitations of conventional thermally activated sensors that require multiple parameters and paves the way for the development of rapid-response monitoring systems.

Authors

  • Yan-Fong Lin
    Photonics Group, Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei 10660, Taiwan.
  • Yu-Chen Chi
    Photonics Group, Department of Engineering Science and Ocean Engineering, National Taiwan University, Taipei 10660, Taiwan.
  • Sheng-Hong Tseng
    Department of Applied Materials and Optoelectronic Engineering, National Chi Nan University, Nantou 54561, Taiwan.
  • Te-Fu Wang
    Department of Applied Materials and Optoelectronic Engineering, National Chi Nan University, Nantou 54561, Taiwan.
  • Ying-Tsung Lin
    Department of Applied Materials and Optoelectronic Engineering, National Chi Nan University, Nantou 54561, Taiwan.
  • Min-Ta Yang
    Department of Applied Materials and Optoelectronic Engineering, National Chi Nan University, Nantou 54561, Taiwan.
  • Chih-Hao Lin
    Department of Emergency Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
  • Su-Yu Liao
    Department of Applied Materials and Optoelectronic Engineering, National Chi Nan University, Nantou 54561, Taiwan.
  • Chun-Ying Huang
    Department of Trauma Surgery, Kaohsiung Chang Gung Memorial Hospital and Chang Gung University College of Medicine, Taiwan.

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