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    Finding Hidden Signals in Chemical Sensors Using Deep Learning
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    • Soo-Yeon Cho
      Soo-Yeon Cho
      Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea
      KAIST Institute for NanoCentury, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea
      Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02142, United States
      More by Soo-Yeon Cho
      Orcidhttp://orcid.org/0000-0001-6294-1154
    • Youhan Lee
      Youhan Lee
      Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea
      KAIST Institute for NanoCentury, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea
      More by Youhan Lee
      Orcidhttp://orcid.org/0000-0001-8950-3701
    • Sangwon Lee
      Sangwon Lee
      Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea
      KAIST Institute for NanoCentury, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea
      More by Sangwon Lee
    • Hohyung Kang
      Hohyung Kang
      Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea
      KAIST Institute for NanoCentury, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea
      More by Hohyung Kang
    • Jaehoon Kim
      Jaehoon Kim
      Data Analytics Lab, Samsung SDS, Seongchon-gil 56, Seocho-gu, Seoul 06765, Republic of Korea
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    • Junghoon Choi
      Junghoon Choi
      Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea
      KAIST Institute for NanoCentury, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea
      More by Junghoon Choi
    • Jin Ryu
      Jin Ryu
      Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea
      KAIST Institute for NanoCentury, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea
      More by Jin Ryu
    • Heeeun Joo
      Heeeun Joo
      Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea
      KAIST Institute for NanoCentury, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea
      More by Heeeun Joo
    • Hee-Tae Jung*
      Hee-Tae Jung
      Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea
      KAIST Institute for NanoCentury, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea
      *Email: [email protected]
      More by Hee-Tae Jung
      Orcidhttp://orcid.org/0000-0002-5727-6732
    • Jihan Kim*
      Jihan Kim
      Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea
      KAIST Institute for NanoCentury, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea
      *Email: [email protected]
      More by Jihan Kim
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    Analytical Chemistry

    Cite this: Anal. Chem. 2020, 92, 9, 6529–6537
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    https://doi.org/10.1021/acs.analchem.0c00137
    Published April 14, 2020
    Copyright © 2020 American Chemical Society
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    Abstract

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    Achieving high signal-to-noise ratio in chemical and biological sensors enables accurate detection of target analytes. Unfortunately, below the limit of detection (LOD), it becomes difficult to detect the presence of small amounts of analytes and extract useful information via any of the conventional methods. In this work, we examine the possibility of extracting “hidden signals” using deep neural network to enhance gas sensing below the LOD region. As a test case system, we conduct experiments for H2 sensing in six different metallic channels (Au, Cu, Mo, Ni, Pt, Pd) and demonstrate that deep neural network can enhance the sensing capabilities for H2 concentration below the LOD. We demonstrate that this technique could be universally used for different types of sensors and target analytes. Our approach can extract new information from the hidden signals, which can be crucial for next-generation chemical sensing applications and analytical chemistry.

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    The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.analchem.0c00137.

    • Materials and methods, schematic illustration and photoimage of the sensors, XPS profiles of the surface of the six sensors, photoimage and schematic diagram of the overall measurement systems, real-time baseline recording of the metallic sensors used for hidden signal detections in the manuscript, explanations of metrics for model evaluation, details of data preparation for training, kernel density plots using reconstruction errors from AE for all sensors, t-SNE plots of all samples in the latent layer for all sensors, model evaluation using various metrics, analysis of the resources of anomalies for six metals, control experiments for demonstration of the origin of hidden signals, hidden signal investigations of a single sensor (Pd) on different target analytes and different sensors (metal oxide semiconductor; MOS) on different target analytes (volatile organic compounds; VOCs), comparison of anomaly detection performance of various algorithms, architecture information on ANNs used in this work, and averaged metric values of the developed classifier for all metals (PDF)

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    Analytical Chemistry

    Cite this: Anal. Chem. 2020, 92, 9, 6529–6537
    Click to copy citationCitation copied!
    https://doi.org/10.1021/acs.analchem.0c00137
    Published April 14, 2020
    Copyright © 2020 American Chemical Society
    Request reuse permissions

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