Green Chem., 2026, Advance Article
DOI: 10.1039/D6GC01077F, Paper
DOI: 10.1039/D6GC01077F, Paper
Open Access
  This article is licensed under a Creative Commons Attribution 3.0 Unported Licence.Jiayin Li, Xinpei Lu, Pranav Arun, Jing Xu, Fausto Gallucci, Sirui Li, Annemie Bogaerts
A hybrid ML model trained on 358 cross-study data points predicts CO2 conversion in DBD reactors with an R2 of 0.92 on unseen experiments, providing an unbiased assessment of model generalizability across heterogeneous experimental sources.
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A hybrid ML model trained on 358 cross-study data points predicts CO2 conversion in DBD reactors with an R2 of 0.92 on unseen experiments, providing an unbiased assessment of model generalizability across heterogeneous experimental sources.
To cite this article before page numbers are assigned, use the DOI form of citation above.
The content of this RSS Feed (c) The Royal Society of Chemistry
