<div>Mass spectrometry (MS) is a powerful and versatile analytical tool that is extensively used for the identification and analysis of complex mixtures. The ability to couple MS to atmospheric pressure ionization techniques and high-performance liquid chromatography (HPLC) or gas chromatography (GC) provides a high degree of experimental flexibility. MS is based on the analysis of gas-phase ions. Gas-phase ions are manipulated within the mass spectrometer and separated for detection based on their mass-to-charge (m/z) ratio.</div><div>One of the most commonly used techniques for complex mixture analysis is tandem mass spectrometry (MSⁿ). MSⁿ involves the isolation of the desired ion and allowing it to undergo reactions, such as collision-activated dissociation (CAD) or ion-molecule reactions. Based on the generated product ions, structural information can be obtained for unknown analytes in complex mixtures. In addition, MSⁿ methods based on diagnostic gas-phase ion-molecule reactions have been demonstrated to provide a general and predictable tool to identify specific functional groups in unknown ionized analytes and to classify unknown analytes into different compound classes depending on their functionalities.</div><div>The research described in this dissertation mainly focuses on the development of tandem mass spectrometric methods based on gas-phase ion-molecule reactions and/or CAD for the identification of the <i>N</i>-nitroso functionality, which is present in some potentially mutagenic drug impurities. Furthermore, the dissertation discusses combining machine learning and MSⁿexperiments based on diagnostic ion-molecule reactions of 2-methoxypropene to predict reaction outcomes in a semiautomated fashion for protonated analytes containing specific functional groups. Lastly, chemical characterization and gas-phase reactivity of the boron-centered radical dianion [B₁₂I₁₁]^2-• toward some organic molecules are discussed.</div>

<div>Mass spectrometry (MS) is a powerful and versatile analytical tool, especially for identification and analysis of complex mixtures. Coupling to high-performance liquid chromatography (HPLC) or gas chromatography (GC) provides additional dimension for mixture analysis. MS manipulates ionized analytes and separates them based on their mass-to-charge (<i>m/z</i>) ratios. MS is capable of providing molecular weight (MW) information by generating pseudo-molecular ions of the analytes. Detailed elemental compositions can be also obtained if high resolution MS is used. MS can also provide extensive structural information of the analyte ions. One of the most commonly used technique is tandem mass spectrometry (MSⁿ). Ions of interest are isolated and subject to sequential reactions (reactions with other molecules or dissociation reactions) to generate product ions that can provide structural information. MS is also a powerful tool for generating and studying highly reactive reaction intermediates, such as organic polyradicals.</div><div><br></div><div>The research described in this dissertation mainly focuses on the generation and gas-phase reactivity studies of different organic biradicals. Their reactions with various organic reagents are studied, and the reactivity-controlling factors are discussed. For example, the reactivity of several substituted pyridine-based biradical cations with 2,6-topology are discussed (all with singlet ground states), and their special reactivity from their excited triplet states are illustrated. Besides, several quinoline-based biradicals and cyano-substituted pyridine-based <i>para</i>-benzyne cations are also discussed. Some of the radicals (or ions) described in this dissertation are generated for the first time, i.e. the quinoline-based oxenium cations. Their structural characterization and gas-phase reactivity toward some organic molecules are discussed in the dissertation. Further, an electrophilic anion, <i>closo</i>-[B₁₂X₁₁]^- (X = Cl, Br) and its application in the activation of C-H and C-C bonds in hydrocarbon molecules are described in the dissertation.</div>

<p dir="ltr">Tandem mass spectrometry (MSⁿ) 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, MSⁿ 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, MSⁿ 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 MSⁿ 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>