ArticleProceedings of the National Academy of Sciences of the United States of America2025
In silico machine learning-enabled detection of polycyclic aromatic hydrocarbons from contaminated soil.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2025. 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
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
1 citing paper in PubMed.
- SERS Cheminformatics: Opportunities for Data-Driven Discovery and Applications.ACS central science · 2025Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors.
Funding
Abstract
The detection and identification of polycyclic aromatic hydrocarbons (PAHs) and their modified derivatives in contaminated soil is challenging due to the chemical and microbial complexity of soil organic matter. To address these challenges, we developed an innovative analytical approach that combines Surface-enhanced Raman spectroscopy with a Raman spectral library constructed in silico using density functional theory (DFT)-calculated spectra. This method overcomes several limitations associated with traditional experimental libraries, including spectral background interference, solvent effects, and commercially unavailable or challenging to synthesize compounds. Our methodology employs a physics-informed machine learning pipeline that operates in two stages: the characteristic peak extraction (CaPE) algorithm, which isolates distinctive spectral features, and the characteristic peak similarity (CaPSim) algorithm, which identifies analytes with high robustness to spectral shifts and amplitude variations. Validation of this approach showed strong similarity values (>0.6) between DFT-calculated and experimental Surface-enhanced Raman spectra for multiple PAHs, confirming its accuracy and discriminative capability. This study establishes the viability of DFT-calculated spectra as reliable references for identifying analytes that lack experimental reference spectra, including those formed through environmental modification of PAHs. This advancement addresses a critical gap in environmental monitoring, providing a valuable tool for assessing public health risks associated with these contaminants.
Indexed as
Identifiers
What OpenQuestion 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 OpenQuestion graph.