Toward Healthcare Diagnoses by Machine-Learning-Enabled Volatile Organic Compound Identification-Reference-Cited by-同舟云学术

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Toward Healthcare Diagnoses by Machine-Learning-Enabled Volatile Organic Compound Identification

Published:2020-12-14 Issue:1 Volume:15 Page:894-903
ISSN:1936-0851
Container-title:ACS Nano
language:en
Short-container-title:ACS Nano

Author:

Zhu Jianxiong¹²³^ORCID,Ren Zhihao¹²³,Lee Chengkuo¹²³⁴^ORCID

Affiliation:

1. Department of Electrical and Computer Engineering, National University of Singapore, Singapore, 117576, Singapore

2. Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, Singapore, 117576, Singapore

3. NUS Suzhou Research Institute (NUSRI), Suzhou, 215123, People’s Republic of China

4. NUS Graduate School for Integrative Science and Engineering (NGS), National University of Singapore, Singapore, 117576, Singapore

Funder

Agency for Science, Technology and Research

National Research Foundation Singapore

Narodowe Centrum Badan i Rozwoju

Publisher

American Chemical Society (ACS)

Subject

General Physics and Astronomy,General Engineering,General Materials Science

Link

https://pubs.acs.org/doi/pdf/10.1021/acsnano.0c07464

Reference65 articles.

1. Determination of acetone, 2-butanone, diethyl ketone and BTX using HSCC-UV-IMS

2. Vibrational spectroscopic investigations of ammonia gas sensing mechanism in polypyrrole nanostructures

3. Mid-Infrared Tunable Laser-Based Broadband Fingerprint Absorption Spectroscopy for Trace Gas Sensing: A Review

4. The self-powered CO2 gas sensor based on gas discharge induced by triboelectric nanogenerator

5. Development and Improvement of Carbon Nanotube-Based Ammonia Gas Sensors Using Ink-Jet Printed Interdigitated Electrodes

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