Machine learning-driven gas concentration forecasting: A case study with WS2 nanoflower gas sensor-Reference-Cited by-同舟云学术

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Machine learning-driven gas concentration forecasting: A case study with WS2 nanoflower gas sensor

Published:2024-09 Issue: Volume:307 Page:117455
ISSN:0921-5107
Container-title:Materials Science and Engineering: B
language:en
Short-container-title:Materials Science and Engineering: B

Author:

Liu Shuai,Xue Jiale,Liang Xiaonan,Qiu Jie,Yang Hangfan,Xu Ruojun,Chen Guoxiang

Publisher

Elsevier BV

Reference40 articles.

1. One-pot synthesis of zinc oxide-polyaniline nanocomposite for fabrication of efficient room temperature ammonia gas sensor;Das;Ceram. Int.,2017

2. Surface and gas sensing properties of nanocrystalline nickel oxide thin films;Soleimanpour;Appl. Surf. Sci.,2013

3. Nanostructured TiO2 films: enhanced NH3 detection at room temperature;Dhivya;Ceram. Int.,2014

4. NH3 gas sensor based on Pd/SnO2/RGO ternary composite operated at room-temperature;Su;Sensor. Actuator. B Chem.,2016

5. Fast response ammonia sensors based on TiO2 and NiO nanostructured bilayer thin films;Kumar;RSC Adv.,2016

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