Cited by 21Gas Sensing Nanomaterials and SensorsMachine Learning in Materials ScienceAdvanced Chemical Physics Studies

Machine learning prediction of perovskite sensors for monitoring the gas in lithium-ion battery

Hu, Dunan, Yang, Zijiang, Huang, Sheng·2024·Sensors and Actuators A: Physical

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Crossref · 10.1016/j.sna.2024.115162

Cites

73 of 73 references verified0 citation errors · 0 not checkable

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  • Key factors such as adsorption energy, band structure, and density of states were found to significantly impact carrier transport and gas-sensing performance, providing a new framework for future research.

    “Key factors such as adsorption energy, band structure, and density of states were found to significantly impact carrier transport and gas-sensing performance, providing a new framework for future research (Figure 16C).”
  • By employing machine learning with voting classifiers, they achieved a prediction accuracy of 85.71%.

    “By employing machine learning with voting classifiers, they achieved a prediction accuracy of 85.71%.”
  • Hu et al. proposed a strategy combining machine learning and ab initio calculations to predict gas-sensitive materials for lithium-ion batteries.

    “Hu et al. [231] proposed a strategy combining machine learning and ab initio calculations to predict gas-sensitive materials for lithium-ion batteries.”
  • Using copper acetylacetonate-functionalized perovskite CsPbBr3 as a case study, they discovered that gas adsorption parameters uniquely influence electrical response.

    “Using copper acetylacetonate-functionalized perovskite CsPbBr 3 as a case study, they discovered that gas adsorption parameters uniquely influence electrical response.”

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Authors
Hu, D., Yang, Z., & Huang, S.
Affiliations
China University of Mining and Technology (CN)
Corresponding author
Huang, Sheng
Venue
Sensors and Actuators A: Physical
Publisher
Elsevier BV
Published
2024-04
Volume
369
Pages
115162
ISSN
0924-4247 (ISSN-L)
OpenAlex
W4391692037
Cited by
21
References
74
Citations by year
2026: 3 · 2025: 13 · 2024: 5
FWCI
2.18
Type
journal article
Language
en
Open access
closed
Locations
Sensors and Actuators A Physical (publishedVersion)
Funders
Fundamental Research Funds for the Central Universities (2021QN1110)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Gas Sensing Nanomaterials and Sensors
  • Physical Sciences › Materials Science › Materials Chemistry › Machine Learning in Materials Science
  • Physical Sciences › Physics and Astronomy › Atomic and Molecular Physics, and Optics › Advanced Chemical Physics Studies
Keywords
gas sensing, CsPbBr3, copper acetylacetonate, gas adsorption energy, electronic band structure, carrier transport, machine learning prediction, first-principles calculations

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