ArticleRSC advances2025
Machine learning-based prediction and mechanistic insight into PFAS adsorption on carbon-based materials.
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
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Carbon-based materials hold significant potential in environmental remediation, as they can effectively remove per- and polyfluoroalkyl substances (PFAS) through adsorption, thereby influencing their environmental behavior and associated risks. However, due to the complexity of the physicochemical properties of carbon-based materials, the molecular diversity of PFAS-including variations in chain length, functional groups, and degree of fluorination-as well as differences in environmental conditions, it remains challenging to fully elucidate the adsorption mechanisms solely through experimental approaches. In this study, a gradient boosting decision tree (GBDT) model was developed and optimized to systematically predict the adsorption performance of carbon-based materials toward PFAS. The GBDT model demonstrated excellent predictive accuracy on the test dataset, achieving an
Identifiers
What Socratic holds
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 Socratic graph.