A comprehensive gas recognition algorithm with label-free drift compensation based on domain adversarial network

Pan, Xiaofang; Chen, Jiebin; Wen, Xiaolin; Hao, Jiacheng; Xu, Wei; Fraser, Michael; Zhao, Xiaojin

doi:10.1016/j.snb.2023.133709

Sensors and Actuators B: Chemical

2023

DOI: 10.1016/j.snb.2023.133709

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A comprehensive gas recognition algorithm with label-free drift compensation based on domain adversarial network

Xiaofang Pan¹,

Jiebin Chen²,

Xiaolin Wen³

et al.

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Cited by 16 publications

References 29 publications

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Integrated Mixed Potential Gas Sensor with Efficient Structure for Discriminative Volatile Organic Compounds Detection

Lv,

Gu,

et al. 2024

Advanced Science

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Amid growing interest in the precise detection of volatile organic compounds (VOCs) in industrial field, the demand for highly effective gas sensors is at an all‐time high. However, traditional sensors with their classic single‐output signal, bulky and complex integrated structure when forming array often involve complicated technology and high cost, limiting their widespread adoption. Here, this study introduces a novel approach, employing an integrated YSZ‐based (YSZ: yttria‐stabilized zirconia) mixed potential sensor equipped with a triple‐sensing electrode array, to efficiently detect and differentiate six types of VOCs gases. This innovative sensor integrates NiSb2O6, CuSb2O6, and MgSb2O6 sensing electrodes (SEs), which are sensitive to pentane, isoprene, n‐propanol, acetone, acetic acid, and formaldehyde gases. Through feature engineering based on intuitive spike‐based response values, it accentuates the distinct characteristics of every gas. Eventually, an average classification accuracy of 98.8% and an overall R‐squared error (R2) of 99.3% for concentration regression toward six target gases can be achieved, showcasing the potential to quantitatively distinguish between industrial hazardous VOCs gases.

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Integrated Mixed Potential Gas Sensor with Efficient Structure for Discriminative Volatile Organic Compounds Detection

Lv,

Gu,

et al. 2024

Advanced Science

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Machine Learning Driven Atom‐Thin Materials for Fragrance Sensing

Liu,

Sun,

Bao

et al. 2024

Small

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Fragrance plays a crucial role in the daily lives. Its importance spans various sectors, from therapeutic purposes to personal care, making the understanding and accurate identification of fragrances essential. To fully harness the potential of fragrances, efficient and precise fragrance sensing and identification are necessary. However, current fragrance sensors face several limitations, particularly in detecting and differentiating complex scent profiles with high accuracy. To address these challenges, the use of atom‐thin materials in fragrance sensors has emerged as a groundbreaking approach. These atom‐thin sensors, characterized by their enhanced sensitivity and selectivity, offer significant improvements over traditional sensing technology. Moreover, the integration of Machine Learning (ML) into fragrance sensing has opened new opportunities in the field. ML algorithms applied to fragrance sensing facilitate advancements in four key domains: accurate fragrance identification, precise discrimination between different fragrances, improved detection thresholds for subtle scents, and prediction of fragrance properties. This comprehensive review delves into the synergistic use of atom‐thin materials and ML in fragrance sensing, providing an in‐depth analysis of how these technologies are revolutionizing the field, offering insights into their current applications and future potential in enhancing the understanding and utilization of fragrances.

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Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array

Mei,

Peng,

Wang

et al. 2024

Nano-Micro Lett.

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As information acquisition terminals for artificial olfaction, chemiresistive gas sensors are often troubled by their cross-sensitivity, and reducing their cross-response to ambient gases has always been a difficult and important point in the gas sensing area. Pattern recognition based on sensor array is the most conspicuous way to overcome the cross-sensitivity of gas sensors. It is crucial to choose an appropriate pattern recognition method for enhancing data analysis, reducing errors and improving system reliability, obtaining better classification or gas concentration prediction results. In this review, we analyze the sensing mechanism of cross-sensitivity for chemiresistive gas sensors. We further examine the types, working principles, characteristics, and applicable gas detection range of pattern recognition algorithms utilized in gas-sensing arrays. Additionally, we report, summarize, and evaluate the outstanding and novel advancements in pattern recognition methods for gas identification. At the same time, this work showcases the recent advancements in utilizing these methods for gas identification, particularly within three crucial domains: ensuring food safety, monitoring the environment, and aiding in medical diagnosis. In conclusion, this study anticipates future research prospects by considering the existing landscape and challenges. It is hoped that this work will make a positive contribution towards mitigating cross-sensitivity in gas-sensitive devices and offer valuable insights for algorithm selection in gas recognition applications.

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A comprehensive gas recognition algorithm with label-free drift compensation based on domain adversarial network

Cited by 16 publications

References 29 publications

Integrated Mixed Potential Gas Sensor with Efficient Structure for Discriminative Volatile Organic Compounds Detection

Integrated Mixed Potential Gas Sensor with Efficient Structure for Discriminative Volatile Organic Compounds Detection

Machine Learning Driven Atom‐Thin Materials for Fragrance Sensing

Overcoming the Limits of Cross-Sensitivity: Pattern Recognition Methods for Chemiresistive Gas Sensor Array

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