Machine learning prediction of perovskite sensors for monitoring the gas in lithium-ion battery
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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).”
From Design Strategies and Emerging Applications of Perovskite-Based Sensors (2025) · cites this paper as [231]
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%.”
From Design Strategies and Emerging Applications of Perovskite-Based Sensors (2025) · cites this paper as [231]
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.”
From Design Strategies and Emerging Applications of Perovskite-Based Sensors (2025) · cites this paper as [231]
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.”
From Design Strategies and Emerging Applications of Perovskite-Based Sensors (2025) · cites this paper as [231]
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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
- Licence
- 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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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