Developments in machine learning software and nanoparticles-assisted Surface-Enhanced Raman Scattering (SERS) techniques have remarkable potential in improving the detection accuracy and sensitivity of pollutants molecules. A cancerogenic class of environmental and biological pollutants of great interest are polycyclic aromatic hydrocarbons (PAHs), molecules consisting of multiple fused benzene ring structures. Traditional methods of detection including High-performance liquid chromatography/mass spectrometry and gas chromatography require expensive lab equipment and laborious sample preparation. Here we investigate whether SERS in combination with machine learning is a more streamlined approach to accurately detect individual Pyrene PAH molecules from contaminating soil.

Rice University,  Nano-Enabled Water Treatment National Science Foundation (NSF) award #EEC-1449500

Researchers should cite this work as follows:

• Julia Dolive, Rice University, NEWT Center (2022), "Detecting Cancerous Pollutants," https://nanohub.org/resources/36321.

  BibTex | EndNote

1. cancer
2. machine learning
3. NanoEnabled Water Treatment (NEWT) ERC
4. nanoparticles
5. PAHs
6. polycyclic aromatic hydrocarbons
7. Raman scattering
8. Surface-Enhanced Raman Scattering