Machine-learning-enabled exploitation of gas-sensing descriptors: A case study of five pristine metal oxides-Reference-Cited by-同舟云学术

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Machine-learning-enabled exploitation of gas-sensing descriptors: A case study of five pristine metal oxides

Published:2024-07 Issue: Volume:492 Page:152280
ISSN:1385-8947
Container-title:Chemical Engineering Journal
language:en
Short-container-title:Chemical Engineering Journal

Author:

Guo Yongchang,Yang Mingzhi,Huang Gary,Zheng Yangong^ORCID

Publisher

Elsevier BV

Reference24 articles.

1. Metal oxides for solid-state gas sensors: what determines our choice?;Korotcenkov;Mater. Sci. Eng. B,2007

2. Advances and new directions in gas-sensing devices;Kim;Acta Mater.,2013

3. Machine learning-enabled exploration of the electrochemical stability of real-scale metallic nanoparticles;Bang;Nat. Commun.,2023

4. Machine Learning Guided Dopant Selection for Metal Oxide-Based Photoelectrochemical Water Splitting: The Case Study of Fe(2) O(3) and CuO;Wang;Adv Mater,2022

5. Explainable machine learning in materials science;Zhong;npj Comput. Mater.,2022

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