Ultralow-Power Single-Sensor-Based E-Nose System Powered by Duty Cycling and Deep Learning for Real-Time Gas Identification-Reference-Cited by-同舟云学术

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Ultralow-Power Single-Sensor-Based E-Nose System Powered by Duty Cycling and Deep Learning for Real-Time Gas Identification

Published:2024-06-10 Issue:7 Volume:9 Page:3557-3572
ISSN:2379-3694
Container-title:ACS Sensors
language:en
Short-container-title:ACS Sens.

Author:

Kim Taejung¹,Kim Yonggi²^ORCID,Cho Wootaek¹,Kwak Jong-Hyun¹,Cho Jeonghoon²,Pyeon Youjang²,Kim Jae Joon²,Shin Heungjoo¹^ORCID

Affiliation:

1. Department of Mechanical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea

2. Department of Electrical Engineering, Ulsan National Institute of Science and Technology (UNIST), Ulsan 44919, Republic of Korea

Funder

National Research Foundation of Korea

Ministry of Trade, Industry and Energy

Publisher

American Chemical Society (ACS)

Link

https://pubs.acs.org/doi/pdf/10.1021/acssensors.4c00471

Reference65 articles.

1. Validate personal air-pollution sensors

2. Highly selective detection of methanol over ethanol by a handheld gas sensor

3. Highly sensitive and selective gas sensors using p-type oxide semiconductors: Overview

4. Challenges and Promises of Metal Oxide Nanosensors

5. Ultra-Low-Power Smart Electronic Nose System Based on Three-Dimensional Tin Oxide Nanotube Arrays

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Gas Sensing with Nanoporous In2O3 under Cyclic Optical Activation: Machine Learning-Aided Classification of H2 and H2O;Chemosensors;2024-09-03

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