A major driving force for development of new catalyst systems is the need for more efficient synthesis of chemical compounds essential to modern life. Catalysts having superior performance offer significant environmental and economic advantages, but their discovery is not trivial. Well-defined, homogeneous catalysts can offer unparalleled understanding of ligand effects, which proves invaluable in directing redesign strategies. This thesis work focuses on the design of ruthenium complexes for applications in dinitrogen activation and olefin metathesis. The complexes developed create new directions in small-molecule activation and asymmetric catalysis by late-metal complexes. Also examined are the dual challenges, ubiquitous in catalysis, of adequate interrogation of catalyst structure and performance. Insight into both is essential to enable correlation of ligand properties with catalyst activity and/or selectivity. Improved methods for accelerated assessment of catalyst performance are described, which expand high-throughput catalyst screening to encompass parallel acquisition of kinetic data. A final aspect focuses on direct examination of metal complexes, both as isolated species, and under catalytic conditions. Applications of charge-transfer MALDI mass spectrometry to structural elucidation in organometallic chemistry is described, and the technique is employed to gain insight into catalyst decomposition pathways under operating conditions.

A major driving force for development of new catalyst systems is the need for more efficient synthesis of chemical compounds essential to modern life. Catalysts having superior performance offer significant environmental and economic advantages, but their discovery is not trivial. Well-defined, homogeneous catalysts can offer unparalleled understanding of ligand effects, which proves invaluable in directing redesign strategies. This thesis work focuses on the design of ruthenium complexes for applications in dinitrogen activation and olefin metathesis. The complexes developed create new directions in small-molecule activation and asymmetric catalysis by late-metal complexes. Also examined are the dual challenges, ubiquitous in catalysis, of adequate interrogation of catalyst structure and performance. Insight into both is essential to enable correlation of ligand properties with catalyst activity and/or selectivity. Improved methods for accelerated assessment of catalyst performance are described, which expand high-throughput catalyst screening to encompass parallel acquisition of kinetic data. A final aspect focuses on direct examination of metal complexes, both as isolated species, and under catalytic conditions. Applications of charge-transfer MALDI mass spectrometry to structural elucidation in organometallic chemistry is described, and the technique is employed to gain insight into catalyst decomposition pathways under operating conditions.

A major driving force for development of new catalyst systems is the need for more efficient synthesis of chemical compounds essential to modern life. Catalysts having superior performance offer significant environmental and economic advantages, but their discovery is not trivial. Well-defined, homogeneous catalysts can offer unparalleled understanding of ligand effects, which proves invaluable in directing redesign strategies. This thesis work focuses on the design of ruthenium complexes for applications in dinitrogen activation and olefin metathesis. The complexes developed create new directions in small-molecule activation and asymmetric catalysis by late-metal complexes. Also examined are the dual challenges, ubiquitous in catalysis, of adequate interrogation of catalyst structure and performance. Insight into both is essential to enable correlation of ligand properties with catalyst activity and/or selectivity. Improved methods for accelerated assessment of catalyst performance are described, which expand high-throughput catalyst screening to encompass parallel acquisition of kinetic data. A final aspect focuses on direct examination of metal complexes, both as isolated species, and under catalytic conditions. Applications of charge-transfer MALDI mass spectrometry to structural elucidation in organometallic chemistry is described, and the technique is employed to gain insight into catalyst decomposition pathways under operating conditions.

A major driving force for development of new catalyst systems is the need for more efficient synthesis of chemical compounds essential to modern life. Catalysts having superior performance offer significant environmental and economic advantages, but their discovery is not trivial. Well-defined, homogeneous catalysts can offer unparalleled understanding of ligand effects, which proves invaluable in directing redesign strategies. This thesis work focuses on the design of ruthenium complexes for applications in dinitrogen activation and olefin metathesis. The complexes developed create new directions in small-molecule activation and asymmetric catalysis by late-metal complexes. Also examined are the dual challenges, ubiquitous in catalysis, of adequate interrogation of catalyst structure and performance. Insight into both is essential to enable correlation of ligand properties with catalyst activity and/or selectivity. Improved methods for accelerated assessment of catalyst performance are described, which expand high-throughput catalyst screening to encompass parallel acquisition of kinetic data. A final aspect focuses on direct examination of metal complexes, both as isolated species, and under catalytic conditions. Applications of charge-transfer MALDI mass spectrometry to structural elucidation in organometallic chemistry is described, and the technique is employed to gain insight into catalyst decomposition pathways under operating conditions.

<p>  </p> <p>Aromatic C-N bond formation is critical for synthetic chemistry in pharmaceutical, agrochemical, and natural product synthesis. Due to the prevalence of this bond class, many synthetic routes have been developed over time to meet the demand. The most recent and robust C-N bond formation reaction is the palladium catalyzed Buchwald-Hartwig amination. Considering the importance of the Buchwald-Hartwig amination, a high-throughput experimentation (HTE) campaign was devised to create a library in which chemists can refer to optimal reaction conditions and ligand/catalyst choice based on the nature of their substrates to be coupled. This study showed trends for the appropriate choice of ligand and catalyst, along with what bases, temperatures, stoichiometries, and solvents are appropriate for the selected substrate combination at hand. </p>

Contemporary challenges in synthetic organic chemistry require innovative solutions. The discovery of highly-effective and readily accessible scaffolds drives the ever expanding scope of catalytic transformations. This dissertation outlines the repurposing of 1,5-Diaza-3,7-diphosphacyclooctanes (P₂N₂) ligands, commonly employed in inorganic or coordination chemistry, towards organic cross-coupling reactions. Despite their prominence in energy-storage applications, P₂N₂ ligands have been underexplored in catalytic C-C bond formation reactions. Chapter 1 provides a detailed introduction to late transition metal catalysis and the history of P₂N₂ ligands. Chapter 2 outlines the discovery of P^(Cy)₂N^(ArCF3)₂ as a powerful P₂N₂ ligand for the Ni-catalyzed reductive cross-coupling of aryl iodides with aldehydes. Chapter 3 details the extrapolation of the Ni/P^(Cy)₂N^(ArCF3)₂ catalyst system to the related, but less established, redox-neutral α-arylation of primary alcohols. Chapter 4 highlights the applicability of P₂N₂ ligands towards Ni- and Pd-catalyzed Mizoroki-Heck reactions. High-throughput experimentation (HTE) indicated a range of hits with P₂N₂ ligands compared to established ligands in Heck-type couplings. We discovered that absolute site selectivity of C-C bond formation could be controlled by simply altering the phosphorus substituent on the P₂N₂ ligand for the coupling of aryl triflates with styrenes. Notably, this degree of selectivity was not observed with conventional ligands. Chapter 5 focuses on the preparation of the P₂N₂ ligands. Finally, chapter 6 offers a perspective on future developments of P₂N₂ ligands and the prospective directions of their application in transition metal-catalyzed transformations.

High-throughput experimentation (HTE) is a form of accelerated testing which allows for many hundreds or thousands of experiments to be conducted in parallel or in rapid sequence. Recent advances in chemical reaction miniaturization have enabled HTE application toward chemical reaction exploration, and the resultant datasets present exciting opportunities for the incorporation of machine learning (ML) with organic chemistry to expedite reaction optimization and discovery. Disclosed herein is a modular HTE chemistry reaction platform with rapid and inexpensive data acquisition capabilities for training ML algorithms on organic chemistry. Comprising almost entirely off-the-shelf components and algorithms which will be made open-source, this platform facilitates data democratization through distributed generation. Underpinning this workflow is an innovative titration-based analysis method for semi-automated and quantitative conversion data acquisition at a rate of under fifteen seconds per sample. Requisite to this platform’s success are solutions to solid and liquid reagent distribution, reaction incubation, and fast, quantitative reaction analysis which is demonstrated in a proof-of-concept screening of the SNAr reaction toward the synthesis of proteolysis targeting chimera (PROTACs). It is hoped this platform lowers the barrier for entry to HTE for chemists through its modularity, approachability, and low operating costs. / 2024-06-16T00:00:00Z

Many grand societal challenges are rooted in the need for new materials, such as those related to energy, health, and the environment. However, the traditional way of discovering new materials is basically trial and error. This time-consuming and expensive method can't meet the quickly growing requirements for material discovery. To meet this challenge, the government of the United States started the Materials Genome Initiative (MGI) in 2011. MGI aims at accelerating the pace and reducing the cost of discovering new materials. The success of MGI needs materials innovation infrastructure including data tools, computation tools, and experiment tools. The last decade has witnessed significant progress for MGI, especially with respect to hard materials. However, relatively less attention has been paid to soft materials. One important reason is the lack of experimental tools, especially characterization tools for high-throughput experimentation. This dissertation aims to enrich the toolbox by trying new sensor tools for high-throughput characterization of hydrogels. Piezoelectric-excited millimeter-sized cantilever (PEMC) sensors were used in this dissertation to characterize hydrogels. Their capability to investigate hydrogels was first demonstrated by monitoring the synthesis and stimuli-response of composite hydrogels. The PEMC sensors enabled in-situ study of how the manufacturing process, i.e. bulk vs. layer-by-layer, affects the structure and properties of hydrogels. Afterwards, the PEMC sensors were integrated with robots to develop a method of high-throughput experimentation. Various hydrogels were formulated in a well-plate format and characterized by the sensor tools in an automated manner. High-throughput characterization, especially multi-property characterization enabled optimizing the formulation to achieve tradeoff between different properties. Finally, the sensor-based high-throughput experimentation was combined with active learning for accelerated material discovery. A collaborative learning was used to guide the high-throughput formulation and characterization of hydrogels, which demonstrated rapid discovery of mechanically optimized composite glycogels. Through this dissertation, we hope to provide a new tool for high-throughput characterization of soft materials to accelerate the discovery and optimization of materials. / Doctor of Philosophy / Many grand societal challenges, including those associated with energy and healthcare, are driven by the need for new materials. However, the traditional way of discovering new materials is based on trial and error using low throughput computational and experimental methods. For example, it often takes several years, even decades, to discover and commercialize new materials. The lithium-ion battery is a good example. Traditional time-consuming and expensive methods cannot meet the fast-growing requirements of modern material discovery. With the development of computer science and automation, the idea of using data, artificial intelligence, and robots for accelerated materials discovery has attracted more and more attention. Significant progress has been made in metals and inorganic non-metal materials (e.g., semiconductors) in the past decade under the guidance of machine learning and the assistance of automated robots. However, relatively less progress has been made in materials having complex structures and dynamic properties, such as hydrogels. Hydrogels have wide applications in our daily lives, such as drugs and biomedical devices. One significant barrier to accelerated discovery and engineering of hydrogels is the lack of tools that can rapidly characterize the material's properties. In this dissertation, a sensor-based approach was created to characterize the mechanical properties and stimuli-response of soft materials using low sample volumes. The sensor was integrated with a robot to test materials in high-throughput formats in a rapid and automated measurement format. In combination with machine learning, the high-throughput characterization method was demonstrated to accelerate the engineering and optimization of several hydrogels. Through this dissertation, we hope to provide new tools and methods for rapid engineering of soft materials.