ArticleRSC advances2025
Accelerated discovery of new organic photovoltaic dyes for OPVs using light absorbance as the primary screening criterion
Article in RSC advances, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This research provides an analysis of the light absorption properties of organic dyes in different organic solvents. By employing state-of-the-art machine learning (ML) techniques, including multi-output Gaussian process regression and ensemble methods like XGBoost and Random Forest regressors, we successfully predicted solvent-specific absorbance characteristics. XGBoost demonstrated outstanding predictive efficiency, and interpretation
Identifiers
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.