Finding Hidden Signals in Chemical Sensors Using Deep Learning-Reference-Cited by-同舟云学术

Finding Hidden Signals in Chemical Sensors Using Deep Learning

Published:2020-04-14 Issue:9 Volume:92 Page:6529-6537
ISSN:0003-2700
Container-title:Analytical Chemistry
language:en
Short-container-title:Anal. Chem.

Author:

Cho Soo-Yeon¹²³^ORCID,Lee Youhan¹²^ORCID,Lee Sangwon¹²,Kang Hohyung¹²,Kim Jaehoon⁴,Choi Junghoon¹²,Ryu Jin¹²,Joo Heeeun¹²,Jung Hee-Tae¹²^ORCID,Kim Jihan¹²

Affiliation:

1. Department of Chemical and Biomolecular Engineering, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea

2. KAIST Institute for NanoCentury, Korea Advanced Institute of Science and Technology, Daehak-ro 291, Yuseong-gu, Daejeon 34141, Republic of Korea

3. Department of Chemical Engineering, Massachusetts Institute of Technology, 77 Massachusetts Avenue, Cambridge, Massachusetts 02142, United States

4. Data Analytics Lab, Samsung SDS, Seongchon-gil 56, Seocho-gu, Seoul 06765, Republic of Korea

Funder

National Research Foundation

Publisher

American Chemical Society (ACS)

Subject

Analytical Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.analchem.0c00137

Reference45 articles.

1. Sensor Networks for Emergency Response: Challenges and Opportunities

2. Innovative Nanosensor for Disease Diagnosis

3. Highly Sensitive Bulk Silicon Chemical Sensors with Sub-5 nm Thin Charge Inversion Layers

4. Methods for the determination of limit of detection and limit of quantitation of the analytical methods

5. Metallic Ti3C2Tx MXene Gas Sensors with Ultrahigh Signal-to-Noise Ratio

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