New method detects hidden soil pollutants

The new approach developed at Rice University and Baylor College of Medicine uses light-based imaging, theoretical predictions of compounds’ light signatures and machine learning (ML) algorithms to detect toxic compounds such as polycyclic aromatic hydrocarbons (PAHs) and their derivative compounds (PACs) in soil.

A common by-product of combustion, PAHs and PACs have been linked to cancer, developmental issues and other serious health problems.

Identifying pollutants in soil usually requires advanced laboratories and standard physical reference samples of the suspected contaminants. However, for many environmental pollutants that pose a public health risk, there is no experimental data available that can be used to detect them.

“This method makes it possible to identify chemicals that have not yet been isolated experimentally,” said Naomi Halas, University Professor and the Stanley C. Moore Professor of Electrical and Computer Engineering at Rice.

The new method uses surface-enhanced Raman spectroscopy, which analyses how light interacts with molecules, tracking the unique patterns (spectra) they emit. According to the team, the technique is refined through the use of signature nanoshells designed to enhance relevant traits in the spectra.