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Full-Text Articles in Physical Sciences and Mathematics
Chemical Analysis Of Organic Compounds In Dew Water, Monica Dibley
Chemical Analysis Of Organic Compounds In Dew Water, Monica Dibley
Undergraduate Honors Theses
Water films on outdoor surfaces, such as dew, can act as a reservoir for organic molecules deposited from the atmosphere and they present a potential reactive medium for chemical transformations. To better understand the flux of volatile organic compounds from evaporating films, the composition and reactivity of the complex mixture of dissolved organic material (DOM) found in these films need to be characterized. Previous studies have measured the salts and the small organic molecules in dew collected on clean Teflon surfaces or condensers. Here, we expand on this by probing the organic chemicals found on natural outdoor surfaces covered in …
Honey As A Biomonitor For Air Pollutant Deposition In The Eastern United States Using Ion Chromatography And Scanning Electron Microscopy, Cole Cochran
Undergraduate Honors Theses
Anthropogenic activities generate metal, acid, and particulate air pollutants which negatively impact human and ecological health. In the United States, power plant, industrial, and vehicle emissions are leading causes of air pollution, however, the measurement of air pollution at high-resolution spatial regimes remains a challenge. Honey has emerged as a powerful biomonitoring tool to effectively quantify contaminants without the need for a large array of monitoring instruments. I hypothesized that honey could be used to effectively measure and map modern air pollutant spatiotemporal relationships over the Eastern U.S. Using ion chromatography with sulfate as an indicator for air pollution and …
Molecular Cluster Fragment Machine Learning Training Techniques To Predict Energetics Of Brown Carbon Aerosol Clusters, Emily E. Chappie
Molecular Cluster Fragment Machine Learning Training Techniques To Predict Energetics Of Brown Carbon Aerosol Clusters, Emily E. Chappie
Undergraduate Honors Theses
Density functional theory (DFT) has become a popular method for computational work involving larger molecular systems as it provides accuracy that rivals ab initio methods while lowering computational cost. Nevertheless, computational cost is still high for systems greater than ten atoms in size, preventing their application in modeling realistic atmospheric systems at the molecular level. Machine learning techniques, however, show promise as cost-effective tools in predicting chemical properties when properly trained. In the interest of furthering chemical machine learning in the field of atmospheric science, I have developed a training method for predicting cluster energetics of newly characterized nitrogen-based brown …