<p dir="ltr">Tandem mass spectrometry (MS<sup>n</sup>) has become the most widely used analytical technique for the chemical characterization of unknown organic compounds in complex mixtures. It has led to the development of a large number of mass spectrometers with different mass analyzers as well as a wide array of ionization methods. This technique can be coupled with a diverse range of chromatography methods, such as gas chromatography (GC) and high-performance liquid chromatography (HPLC). Some of the primary strengths of MS include its great sensitivity, its versatility to seamlessly integrate with various chromatography techniques and its flexibility in the sense of access to different mass analyzers and different ionization methods. During MS experiments, analytes are evaporated and ionized and the resulting ions are separated based on their mass-to-charge (<i>m/z</i>) ratios and then detected. On the other hand, MS<sup>n</sup> experiments involve isolating a specific ion of interest from all other ions and subjecting them to reactions such as collision-activated dissociation (CAD) or ion-molecule reactions. These reactions generate product ions that can be used to obtain structural information for the analyte. In addition, MS<sup>n</sup> experiments can be used to generate and study the chemical properties of reaction intermediates, such as oxenium cations. </p><p dir="ltr">The mass spectrometer and the ionization source used to perform the research discussed in this thesis are described in Chapter 2. After this, the development of experiments involving ion-molecule reactions accompanied by collision-activated dissociation in a linear quadrupole ion trap is discussed, with the goals of differentiating the aziridine functionality from structurally related functional groups, such as the amino group and identifying aromatic aldehyde functionalities in protonated oxygen-containing monofunctional analytes. The integration of machine learning with mass spectral data has become an increasingly prevalent and valuable way to interpret data faster and more accurately without human bias than conventional manual approaches. Chapter 5 discusses combining machine learning-guided automated HPLC analysis coupled with MS<sup>n</sup> experiments based on diagnostic ion-molecule reactions for the structural elucidation of unknown compounds. Finally, experimental and computational studies on the gas-phase reactivity of quinoline-based ground-state singlet oxenium cations are discussed.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/24632859 |
| Date | 27 November 2023 |
| Creators | Ruth Anyaeche (17449233) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/DEVELOPMENT_OF_MASS_SPECTROMETRIC_METHODS_FOR_FAST_IDENTIFICATION_OF_MUTAGENIC_DRUG_IMPURITIES_AND_A_GAS-PHASE_REACTIVITY_STUDY_OF_GROUND-STATE_SINGLET_OXENIUM_CATIONS_VIA_ION-MOLECULE_REACTIONS/24632859 |
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