ArticleACS omega2026
Predicting Carbon Dot Photoluminescence: A Comparative Machine Learning Study on Systematic Synthesis Data.
Article in ACS omega, 2026. 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.
- A critical review on antibacterial mechanisms of carbon dots: a comprehensive approach to combating multidrug resistance.Archives of microbiology · 2026Review
Corrections and comments
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The design of carbon dots (CDs) with tailored optical properties is a significant challenge in materials science, often hindered by complex synthetic protocols and nonlinear synthesis-property relationships. To accelerate this process, we present a data-driven approach leveraging machine learning to predict the photoluminescent emission of CDs from their synthesis parameters. A systematic experimental data set was utilized, comprising 407 CD syntheses prepared from p-benzoquinone and ethylenediamine across different solvents. We performed a rigorous comparative analysis of state-of-the-art ensemble learning algorithms: Random Forest, XGBoost, and CatBoost. The results demonstrate that CatBoost provides superior predictive accuracy, achieving a mean cross-validation coefficient of determination (
Identifiers
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Registered trials
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