Detecting Cancerous Pollutants
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Abstract
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.
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Rice University, Nano-Enabled Water Treatment National Science Foundation (NSF) award #EEC-1449500
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Office of STEM Engagement, Rice University, Houston, TX