The use of heterogeneous catalysis is well established in chemical synthesis, energy, and environmental engineering applications. Supported Pt nanoparticles have been widely reported to act as catalysts in a vast number of chemical reactions. In this report, the performance of Pt/ZrO2 nanocatalyst for the decomposition of methanol, ethanol, 2-propanol, and 2-butanol is investigated. The potential of each alcohol for the production of H2 and other relevant products in the presence of a catalyst is studied. All the alcohols studied show some decomposition activity below 200°C which increased with increasing temperature. In all cases, high selectivity towards H2 formation is observed. With the exception of methanol, all alcohol conversion reactions lead to catalyst deactivation at high temperatures (T > 250°C for 2-propanol and 2- butanol, T > 325°C for ethanol) due to carbon poisoning. However, long-term catalyst deactivation can be avoided by optimizing reaction conditions such as operating temperature. In addition, the performance of Pt/γ-Al2O3 is evaluated in the oxidation of 2-propanol. Pt nanoclusters of similar size (~1 nm diameter) but different structure (shape) were found to display distinctively different catalytic properties. All the systems studied achieve high conversion (~ 90%) below 100°C. However, flatter particles display a lower reaction onset temperature, demonstrating superior catalytic performance. Acetone, CO2, and water are generated as products indicating that both partial and complete oxidation are taking place. A number of techniques including AFM, XPS, TEM, HAADF-TEM, XAFS as well as packed-bed reactor experiments were used for sample characterization and evaluation of catalytic performance.

Catalysis technologies are among the most important in the modern world. They are instrumental in the realization of a variety of products and processes including chemicals, polymers, foods, pharmaceuticals, fuels, and fuel cells. As such, interest in the catalysts that drive these processes is ongoing, and basic research has led to significant advances in the field, including the production of more environmentally friendly catalysts that can be tuned at the molecular/atomic level. However, there are many factors which influence the performance of a catalyst and many unanswered questions still remain. The first part of this work is concerned with the factors that influence the catalytic properties (activity, selectivity, and stability) of supported Pt and Pt-M nanoparticles (NPs). These factors are a synergistic combination of size, composition, support, oxidation state, and reaction environment (i.e. adsorbates, temperature, pressure, etc.). To probe the catalytic properties of complex and dynamic NP systems we have used MeOH decomposition and oxidation reactions, each of which has significant environmental and economic potential. We have given some emphasis to the state of NP oxidation, and with the aid of X-ray photoelectron spectroscopy (XPS) and temperature programmed desorption (TPD), have followed the formation and temperature-dependent evolution of oxide species on Pt NPs. Further, we have explored how these species behave under the conditions of our probe reactions using a packed-bed mass flow reactor coupled to a quadrupole mass spectrometer (QMS). To carry out our investigations we exploit a NP synthesis method which is rather novel to nanocatalysis, micelle encapsulation. Since most available experimental techniques give information on ensemble averages, control over size distributions in NP samples is critical if unambiguous results are to be obtained. Micelle encapsulation allows us this control with several unique, inherent iv advantages. It is to this end that micelle encapsulation has allowed us to probe the detailed structure of small (~1 nm), supported, Pt NPs with extended X-ray absorption fine structure spectroscopy (EXAFS). Furthermore, we were able to explore experimentally, for the first time, the vibrational density of states (VDOS) of supported, isolated, monodispersed, mono and bimetallic NP systems via nuclear resonant inelastic X-ray scattering (NRIX). These synchrotron-based techniques (EXAFS, NRIXS) rely heavily on the monodispersity of the NP ensemble for reliable information

Thesis advisor: Xiao-Xiang Zhang / Thesis advisor: James Morken / This dissertation will present three projects focusing on the development of stereoselective radical cascade reactions via metalloradical catalysis (MRC) using Co(II) D2-symmetric chiral amidoporphyrins [Co(D2-Por*)] as the catalyst. The first project demonstrated the feasibility of applying MRC for asymmetric radical cascade processes by achieving an enantioselective radical bicyclization of 1,6-enynes with diazo compounds, which constructed multi-substituted cyclopropane-fused tetrahydrofurans bearing three contiguous stereogenic centers and one trisubstituted alkene. Detailed mechanistic studies including EPR studies and DFT calculation unveiled a radical-based stepwise mechanism. The synthetic utility of this reaction was demonstrated by a series of diastereoselective transformations of the bicyclic products. In the second project, this strategy was expanded to the application of Co(II)-based MRC to catalyze radical cascade reactions involving hydrogen-atom abstraction (HAA) process. A broad array of homopropargyl ethers reacted with diazo compounds to generate enantiomerically enriched ɑ,β-disubstituted tetrahydrofurans in good yields with high diastereoselectivities and enantioselectivities. The third project explored the utilization of the established strategy to accomplish more challenging bicyclization of 1,6,8-dienynes for the construction of cycloheptadiene-fused tetrahydrofurans in regio- and diastereoselective fashions. Such 5,7-fused ring system has been widely found in natural products and bioactive species. / Thesis (PhD) — Boston College, 2022. / Submitted to: Boston College. Graduate School of Arts and Sciences. / Discipline: Chemistry.

The importance of addressing environmental issues such as lowering the emission of greenhouse gases, preventing eutrophication of aquatic life due to high concetrations of nitrogen oxyanions, and producing sustainable energy is at an all-time high. With a concentration on employing earth abundant metals, a Mn(I) complex was synthesized with an integrated photocatalyst in an attempt at developing a new electrophotocatalytic system. Additionally, a Ni(II) pincer complex was synthesized and structurally and computationally characterized as well as observed to perform electrocatalytic hydrogen generation in water. Lastly, with inspiration from the Ni(II) pincer complex, two macrocyclic complexes, one with a Ni(II) center and the other with a Co(II) center, were structurally characterized and observed to electrocatalytically reduce aqueous nitrite at neutral pH. DFT calculations were also performed to elucidate the nitrite reduction reaction mechanism.

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The electrochemical oxygen reduction reaction (ORR) is a very important catalytic process that is directly used in carbon-free energy systems like fuel cells. However, the lack of active, stable, and cost-effective ORR cathode materials has been a major impediment to the broad adoption of these technologies. So, the challenge for researchers in catalysis is to find catalysts that are electrochemically efficient to drive the reaction, made of earth-abundant elements to lower material costs and allow scalability, and stable to make them last longer. The majority of commercial catalysts that are now being used have been found through trial and error techniques that rely on the chemical intuition of experts. This method of empirical discovery is, however, very challenging, slow, and complicated because the performance of the catalyst depends on a myriad of factors. Researchers have recently turned to machine learning (ML) to find and design heterogeneous catalysts faster with emerging catalysis databases. Black-box models make up a lot of the ML models that are used in the field to predict the properties of catalysts that are important to their performance, such as their adsorption energies to reaction intermediates. However, as these black-box models are based on very complicated mathematical formulas, it is very hard to figure out how they work and the underlying physics of the desired catalyst properties remains hidden. As a way to open up these black boxes and make them easier to understand, more attention is being paid to interpretable and explainable ML. This work aims to speed up the process of screening and optimizing Pt monolayer alloys for ORR while gaining physical insights. We use a theory-infused machine learning framework in combination with a high-throughput active screening approach to effectively find promising ORR Pt monolayer catalysts. Furthermore, an explainability game-theory approach is employed to find electronic factors that control surface reactivity. The novel insights in this study can provide new design strategies that could shape the paradigm of catalyst discovery. / Doctor of Philosophy / The electrochemical oxygen reduction reaction (ORR) is a very important catalytic process that is used directly in carbon-free energy systems like fuel cells. But the lack of ORR cathode materials that are active, stable, and cheap has made it hard for these technologies to be widely used. Most of the commercially used catalysts have been found through trial-and-error methods that rely on the chemical intuition of experts. This method of finding out through experience is hard, slow, and complicated, though, because the performance of the catalyst depends on a variety of factors. Researchers are now using machine learning (ML) and new catalysis databases to find and design heterogeneous catalysts faster. But because black-box ML models are based on very complicated mathematical formulas, it is very hard to figure out how they work, and the physics behind the desired catalyst properties remains hidden. In recent years, more attention has been paid to ML that can be understood and explained as a way to decode these "black boxes" and make them easier to understand. The goal of this work is to speed up the screening and optimization of Pt monolayer alloys for ORR. We find promising ORR Pt monolayer catalysts by using a machine learning framework that is based on theory and a high-throughput active screening method. A game-theory approach is also used to find the electronic factors that control surface reactivity. The new ideas in this study can lead to new ways of designing that could alter how researchers find catalysts.

Computational chemistry and machine learning approaches are combined to understand the mechanisms, derive activity trends, and ultimately to search for active electrocatalysts for the electrochemical oxidation of ammonia (AOR) and nitrate reduction (NO3RR). Both re- actions play vital roles within the nitrogen cycle and have important applications within tackling current environmental issues. Mechanisms are studied through the use of density functional theory (DFT) for AOR and NO3RR, subsequently a descriptor based approach is used to understand activity trends on a wide range of electrocatalysts. For AOR inter- pretable machine learning is used in conjunction with active learning to screen for active and stable ternary electrocatalysts. We find Pt3RuCo, Pt3RuNi and Pt3RuFe show great activity, and are further validated via experimental results. By leveraging the advantages of the interpretible machine learning model we elucidate the underlying electronic factors for the stronger *N binding which leads to the observed improved activity. For NO3RR an interpretible machine learning model is used to understand ways to bypass the stringent limitations put on the electrocatalytic activity due to the *N vs *NO3 scaling relations. It is found that the *N binding energy can be tuned while leaving the *NO3 binding energy unaffected by ensuring that the subsurface atom interacts strongly with the *N. Based on this analysis we suggest the B2 CuPd as a potential active electrocatalyst for this reaction, which is further validated by experiments / Doctor of Philosophy / The chemical reactions that makeup the nitrogen cycle have played a pivotal role in human society, consider the fact that one of the most impactful achievements of the 20th century was the conversion of nitrogen (N2) to ammonia (NH3) via the Haber-Bosch process. The key class of materials to facilitate such transformations are called catalysts, which provide a reactive surface for the reaction to occur at reasonable reaction rates. Using quantum chemistry we can understand how various reactions proceed on the catalyst surface and how the catalyst can be designed to maximize the reaction rate. Specifically here we are interested in the electrochemical oxidation of ammonia (AOR) and reduction of nitrate (NO3RR), which have important energy and environmental applications. The atomistic insight provided by quantum chemistry helps us understand the reaction mechanism and key hurdles in developing new catalysts. Machine learning can then be leveraged in various ways to find novel catalysts. For AOR machine learning finds novel active catalysts from a diverse design space, which are then experimentally tested and verified. Through the use of our machine learning algorithm (TinNet) we also provide new insights into why the catalysts are more active, and suggest novel physics that can help design active catalysts. For NO3RR we use machine learning as a tool to help us understand the hurdles in catalyst design better which then guides our catalyst discovery. It is shown that CuPd could be a potential candidate and is also verified via experimental synthesis and performance testing.

An atomistic control of chemical bonds formation and cleavage holds the key to making molecular transformations more energy efficient and product selective. However, inherent scaling relations among binding strengths of adsorbates on various catalytic materials often give rise to volcano-shaped relationships between the catalytic activity and the affinity of critical intermediates to the surface. The optimal catalysts should bind the reactants 'just right', i.e., neither too strong nor too weak, which is the Sabatier's principle. It is extremely useful for searching promising catalysts, but also imposes serious constraints on design flexibility. Therefore, how to circumvent scaling constraints is crucial for advancing catalytic science. It has been shown that hot electrons can selectively activate the chemical bonds that are not responsive to phonon excitation, thus providing a rational approach beyond scaling limitation. Another emerging yet effective way to break the scaling constraint is single atom catalysis. Strong interactions of supported single atoms with supports dramatically affect the electronic structure of active sites, which reroutes mechanistic pathways of surface reactions. In my PhD research, we use CO oxidation reaction on metal-based active sites as a benchmark system to tailor mechanistic pathways through those two strategies 1) ultra-fast laser induced nonadiabatic surface chemistry and 2) oxide-supported single metal catalysis, with the aim to go beyond the Sabatier activity volcano in metal catalysis. / Doctor of Philosophy / Catalysis is the process of increasing the chemical reaction rate by lowering down the activation barrier. There are three different types of catalysis including enzyme, homogeneous, and heterogeneous catalysis. Heterogeneous catalytic reactions involve a sequence of elementary steps, e.g., adsorption of reactants onto the solid surface, transformation of adsorbed species, and desorption of the products. However, the existing scaling relations among binding energies of reaction intermediates on various catalytic materials lead to volcano-shaped relationships, which show the reaction activity versus the binding energy of critical intermediates. The optimal catalysts should bind the reaction intermediates neither too strong nor too weak. This is the Sabatier's principle, which provides useful guidance for searching promising catalysts. But it also imposes the constraint on the attainable catalytic performance. How to break the constraint to further improve the catalytic activity is an emerging problem. The recent studies have shown that the hot surface electrons on the metal surfaces induced by the ultra-fast laser can selectively activate the chemical bonds, thus providing a rational approach beyond scaling constraints. Another way to break the scaling constraint is single atom catalysis. The metal oxides are frequently used as the support to stabilize the single metal atoms. The strong interaction between the single metal atoms and the support affects the electronic structure of the catalysts. Thereby catalytic reactions on the single metal atoms catalyst are very different from that on metal surfaces. In my PhD research, we use CO oxidation reaction as a benchmark system, to tailor reaction pathways through those two strategies on 1) Ru(0001) under ultra-fast laser pulse and 2) Ir single metal atoms supported on spinel oxides, to go beyond Sabatier activity volcano in metal catalysis.

Due to their large-scale production by the oil refinement industry, alkenes are some of the most ubiquitous starting materials in organic chemistry. The synthesis of many building block chemicals can be traced back to an alkene functionalization reaction. One alkene functionalization that remains underexplored is the introduction of amides. Amide formation is incredibly important, as it is the most common functional group found in bioactive molecules, and amide bond formation is the most common reaction in medicinal chemistry. My thesis will discuss several new methodologies for converting simple alkene starting materials into value-added amide products. First, I will talk about an anti-Markovnikov alkene hydroamidation procedure. Next, I will talk about leveraging chain walking to enable remote hydroamidation of internal alkenes. Finally, I will talk about alkene difunctionalization via nucleometalation to yield heterocyclic products with amides appended. In addition to reaction development, much of the talk will focus on elucidating and studying the mechanisms of each of these transformations.

Density functional theory (DFT), a powerful toolbox, can unveil chemical transformations in detail. This dissertation focuses on exploring catalytic puzzles, deciphering experimental results, and occasionally, reevaluating conventional concepts. In the first problem, a combination of DFT and kinetic studies uncovers the hidden role of borane in directed borylation reactions catalyzed by iridium complex. Borane, initially considered a side product, is revealed to be an autocatalyst. Chiral catalysts are pivotal for achieving asymmetric molecular construction. However, when the chirality center in the catalyst changes with each turnover, what impact does this have? In our second investigation, we delved into a thorough mechanistic study of enantiomeric selectivity during ruthenium complex-catalyzed hydroarylation. This study leads to a reevaluation and refinement of our concepts of asymmetric induction, specifically tailored to dynamic chirality. A series of six Ni(II) complexes featuring N-heterocyclic carbene (NHC) ligands demonstrate photocatalytic CO2 reduction to CO. Remarkably, these complexes retain their activity even in the absence of a photosensitizer, exhibiting self-sensitized photocatalytic capabilities. Our investigation involved ultrafast transient absorption spectroscopy (TAS) experiments and computational studies to provide a deeper understanding of these catalytic activities. Throughout my PhD journey at Mississippi State University, I engaged in diverse research areas within the chemistry department. The final chapter presents a series of chemistry problems encountered in the Hand Lab, where the application of DFT offers insightful solutions. These problems emerged from discussions and collaborations among graduate students, reflecting the spirit of teamwork and collective problem-solving in the department: 1. Understanding electronic structure of FAVE polymer (Smith lab); 2. Explaining the unexpected isomerization of RhCl(3-Si,Si,P) complex (Montiel lab); 3. Understanding stable dinitrogen pincer abnormal CCCPt(N2) complex (Hollis lab) and 4. Characterization of Ni tripodal PE (E = Si, Ge) complexes and studies on the hydroboration mechanism (Montiel lab)