ArticleSmall methods2025
Thermal Imaging for Quality Control in Thin Silicon-Based Coatings for Lithium-Ion Batteries: Defect Detection, Drying Dynamics, and Machine Learning-Based Mass Loading Estimation.
Article in Small methods, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
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Authors and funding
5 authors.
Funding
Abstract
This study demonstrates thermal imaging as a non-destructive, real-time quality-control-method for detecting coating defects, analyzing mass loading, and understanding drying dynamics in silicon-based thin coatings. Thermal imaging identifies critical defects such as streaks, pinholes, and chatter marks through distinct thermal signatures, with streaks reducing surface temperature by up to 15 °C. It establishes strong correlations between surface temperature, mass loading, and coating thickness: for instance, a 100 µm wet film thickness shows a surface temperature of ≈50 °C, corresponding to a mass loading of 2.4 mg cm⁻
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