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Updated: May 22, 2025

High-Throughput Measurement and Classification of Organic P in Environmental Samples
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In silico machine learning-enabled detection of polycyclic aromatic hydrocarbons from contaminated soil.

Yilong Ju1, Oara Neumann2,3, Sara B Denison4

  • 1Department of Computer Science, Rice University, Houston, TX 77005.

Proceedings of the National Academy of Sciences of the United States of America
|May 8, 2025
PubMed
Summary

This study introduces a new method using Surface-enhanced Raman spectroscopy and in silico DFT-calculated spectra to detect challenging polycyclic aromatic hydrocarbons (PAHs) in soil. The approach accurately identifies contaminants without needing experimental spectra.

Keywords:
SERSmachine learningpolycyclic aromatic compoundspolycyclic aromatic hydrocarbons

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Area of Science:

  • Environmental Chemistry
  • Analytical Chemistry
  • Spectroscopy

Background:

  • Detecting polycyclic aromatic hydrocarbons (PAHs) and their derivatives in soil is difficult due to complex soil organic matter.
  • Traditional methods for PAH identification face limitations like spectral interference and unavailability of reference compounds.

Purpose of the Study:

  • To develop an innovative analytical approach for detecting and identifying PAHs and their modified derivatives in contaminated soil.
  • To overcome limitations of traditional experimental spectral libraries.

Main Methods:

  • Combined Surface-enhanced Raman spectroscopy (SERS) with an in silico Raman spectral library generated using density functional theory (DFT).
  • Employed a two-stage physics-informed machine learning pipeline: characteristic peak extraction (CaPE) and characteristic peak similarity (CaPSim).

Main Results:

  • Achieved strong similarity (>0.6) between DFT-calculated and experimental SERS spectra for multiple PAHs.
  • Demonstrated the accuracy and discriminative capability of the developed methodology.
  • Validated the use of DFT-calculated spectra for identifying analytes lacking experimental references.

Conclusions:

  • Established the viability of DFT-calculated spectra as reliable references for PAH identification in environmental samples.
  • Provided a robust tool for environmental monitoring and assessing public health risks from PAH contamination.
  • Addressed a critical gap in identifying environmentally modified PAHs.