ReviewRSC advances2026
Recent advances and challenges of analytical methods for detection of perfluoroalkyl and polyfluoroalkyl substances.
Review in RSC advances, 2026. 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
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Perfluoroalkyl and polyfluoroalkyl substances (PFAS) are synthetic chemicals widely used for domestic and industrial purposes. Because PFAS are highly persistent, bioaccumulative, and toxic, they pose potential threats to the environment and human health. This review provides a comprehensive overview of PFAS occurrence and monitoring across diverse environmental and biological matrices, as well as their toxicological impacts on soil microbiota, plants, animals, and humans. Advancements and limitations of PFAS analytical techniques such as liquid chromatography-mass spectrometry, gas chromatography-mass spectrometry, supercritical fluid chromatography, nuclear magnetic resonance, and high-resolution mass spectrometry are evaluated. Moreover, we explore the emerging integration of artificial intelligence and machine learning approaches in PFAS detection, classification, and toxicity prediction. These data-driven methods offer promising solutions to overcome existing analytical challenges, such as high costs, complex sample preparation, and long analysis time.
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.