Sunlight Induces the Production of Atmospheric Volatile Organic Compounds (VOCs) from Thermokarst Ponds.

Ground subsidence caused by permafrost thawing causes the formation of thermokarst ponds, where organic compounds from eroding permafrost accumulate. We photolyzed water samples from two such ponds in Northern Quebec and discovered the emission of volatile organic compounds (VOCs) using mass spectrometry. One pond near peat-covered permafrost mounds was organic-rich, while the other near sandy mounds was organic-poor. Compounds up to C10 were detected, comprising the atoms of O, N, and S. The main compounds were methanol, acetaldehyde, and acetone. Hourly VOC fluxes under actinic fluxes similar to local solar fluxes might reach up to 1.7 nmol C m-2 s-1. Unexpectedly, the fluxes of VOCs from the organic-poor pond were greater than those from the organic-rich pond. We suggest that different segregations of organics at the air/water interface may partly explain this observation. This study indicates that sunlit thermokarst ponds are a significant source of atmospheric VOCs, which may affect the environment and climate via ozone and aerosol formation. Further work is required for understanding the relationship between the pond's organic composition and VOC emission fluxes.

Pushing the Limits of Surface-Enhanced Raman Spectroscopy (SERS) with Deep Learning: Identification of Multiple Species with Closely Related Molecular Structures.

Raman spectroscopy is a non-destructive and label-free molecular identification technique capable of producing highly specific spectra with various bands correlated to molecular structure. Moreover, the enhanced detection sensitivity offered by surface-enhanced Raman spectroscopy (SERS) allows analyzing mixtures of related chemical species in a relatively short measurement time. Combining SERS with deep learning algorithms allows in some cases to increase detection and classification capabilities even further. The present study evaluates the potential of applying deep learning algorithms to SERS spectroscopy to differentiate and classify different species of bile acids, a large family of molecules with low Raman cross sections and molecular structures that often differ by a single hydroxyl group. Moreover, the study of these molecules is of interest for the medical community since they have distinct pathological roles and are currently viewed as potential markers of gut microbiome imbalances. A convolutional neural network model was developed and used to classify SERS spectra from five bile acid species. The model succeeded in identifying the five analytes despite very similar molecular structures and was found to be reliable even at low analyte concentrations.