AI model identifies over 500 toxic chemicals in e-liquids, revealing vaping’s hidden dangers

By Vijay Kumar MalesuMay 10 2024Reviewed by Lily Ramsey, LLM In a recent review published in the Scientific Reports, a group of authors used a graph-convolutional neural network to predict and analyze the thermal decomposition products of e-liquid flavors, correlating them with mass spectrometry data to assess potential health risks.

The 2019 outbreak of vaping-related lung injuries, linked to additives like vitamin E acetate, underscores the potential risks of inhaling chemically complex e-liquids. Workflow for e-liquid flavor risk assessment The risk assessment for 180 e-liquid flavors involved a workflow that integrated NN: predictions of pyrolysis reactions with experimental MS data.

For this study, the Weisfeiler–Lehman neural network model was adopted due to its ability to predict reaction centers and bond changes in molecules without requiring pyrolysis-specific training data. Correlation with experimental electron-impact mass spectrometry data Experimental EI-MS data was used to confirm the NN predictions. EI-MS identifies bond-breaking in molecules due to energy impact, analogous to bond-breaking in pyrolysis due to heat.

Source: Healthcare Press (healthcarepress.net)

Vaping Esters Heat Mass Spectrometry Nicotine Public Health Research Spectrometry Vitamin E

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