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Skip to Search Results- 33Mass-spectrometry
- 29Chemistry
- 23Organic compounds. Synthesis.
- 22Catalysis
- 22Metabolomics
- 21Capillary electrophoresis.
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Spring 2010
McKnight-Whitford, Anthony Nicholai
Arsenic is a widespread environmental contaminant whose toxicity depends on its valence and its chemical form. Arsenic species have been typically determined using high pressure liquid chromatography coupled to inductively coupled plasma mass spectrometry (HPLC-ICPMS), however ICPMS cannot...
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Development and Application of Chemical Isotope Labeling Methods and Metabolite Identification Solution for Liquid Chromatography-Mass Spectrometry-Based Metabolomics
DownloadFall 2018
Metabolomics, the comprehensive analysis of small molecules in biological specimens, has become an emerging and indispensable tool for systems biology and clinical research. Liquid chromatography-mass spectrometry (LC-MS) is a dominant analytical platform for metabolomics, featuring high...
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Spring 2019
Glycosphingolipids (GSLs) are a class of glycolipids characterized by two major components; an oligosaccharide chain and a ceramide lipid chain bearing a fatty acid and a sphingosine (Sph) base. Gangliosides belong to the family of GSLs whose distinguishing feature is the presence of one or more...
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Spring 2024
A variety of organic molecules have been used to study photophysical processes, such as singlet fission (SF) and triplet-triplet annihilation upconversion (TTA-UC), which are proposed strategies to improve the power conversion efficiencies of photovoltaics. Despite extensive study on these...
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Spring 2024
Hydrogen silsesquioxane (HSQ) is a versatile material with a lengthy history in the synthesis of silicon nanocrystals (SiNCs). Although SiNCs are an exciting material for many optical, electronic, and biological applications, the focal point of this thesis challenges many of the preconceived...
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A Study of the Utility of a Machine-Learning Approach Applied to the Prediction of Site Occupancy and New Members of the Half-Heusler Family
DownloadFall 2019
Predicting the formation and structures of non-molecular inorganic compounds has long been a fundamental goal in solid state chemistry. In this thesis, machine learning approaches have been applied to confront this challenge, focusing in particular on the large family of half-Heusler compounds...