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Browsing by Author "Rappé, Anthony, committee member"

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    Advancements in organocatalyzed atom transfer radical polymerization by investigation of key mechanistic steps
    (Colorado State University. Libraries, 2022) Corbin, Daniel Andreas, author; Miyake, Garret, advisor; Finke, Richard, committee member; Rappé, Anthony, committee member; Kipper, Matt, committee member
    Organocatalyzed atom transfer radical polymerization (O-ATRP) is a controlled radical polymerization method employing organic photoredox catalysts to mediate the synthesis of well-defined polymers. The success of this method derives from its reversible-deactivation mechanism, where polymers are activated by reduction of a chain-end C-Br bond to generate a reactive radical for chain growth, followed by deactivation of the polymer by reinstallation of the dormant bromide chain-end group. As a result, the polymer chain can be grown by reaction of the polymer radical with alkene-based monomers, but undesirable termination and side reactions can be suppressed by minimization of the radical concentration through deactivation. In this work, key mechanistic steps of O-ATRP are investigated to understand the fundamental limitations of this method and improve upon them. When N,N-diaryl dihydrophenazines were investigated, side reactions were identified in which alkyl radicals add to the phenazine core, leading to new core-substituted PC derivatives with non-equivalent catalytic properties. Employing these core-substituted PCs in O-ATRP showed these side reactions can be eliminated to improve polymerization control. In addition, the deactivation step of O-ATRP and related intermediates were studied, which revealed new side reactions that can limit polymerization efficiency as well as influences on the rate of deactivation. Finally, methods to exert control over the deactivation process were developed as a means of improving polymerization outcomes in challenging systems. For example, the intermediate responsible for deactivation was isolated and added to a polymerization to increase the rate of deactivation and limit side reactions in O-ATRP. Alternatively, a similar outcome could be achieved through in-situ electrolysis to increase the concentration of the desired intermediate during the polymerization. Ultimately, this work has yielded insight into important mechanistic processes in O-ATRP that will continue to benefit the development of this method.
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    Combining mechanistic and statistical models for predicting reaction outcomes in organic synthesis
    (Colorado State University. Libraries, 2023) Gallegos, Liliana Cabrera, author; Paton, Robert S., advisor; McNally, Andrew, committee member; Rappé, Anthony, committee member; Hess, Ann, committee member
    Computational modeling and machine learning tools have assisted in the fundamental challenge of predicting the "over-the-arrow" optimal reaction conditions to maximize the output (e.g., yield and selectivity). The work presented here explores multiple challenging synthetic reactions for reaction optimization ranging from: (i) precise photocatalytic transformations in chemical biology, (ii) new reactivity using organobismuth(V) reagents, (iii) challenging reversible nucleophilic alcohol addition reactions influence at equilibrium, and (iv) a late-stage key reaction step in a total synthesis project. Overall, this dissertation aims to assist in predicting optimal reaction outcomes by understanding and formulating reaction mechanisms from quantum mechanics and statistical methods while using open-source automated workflows to improve transparency and reproducibility within data-chemistry fields. Chapter 1 provides the necessary background to introduce the methods behind computational and statistical models that assist in addressing the challenges faced within the optimization process and the limitations of each strategy. First, there will be a brief overview of the computational protocols to generate and understand reaction mechanisms using quantum mechanical methods. Then, a summary of the data-driven approach introduces the statistical methods and metrics that build relationships to chemical reactivity using computer-readable mechanistically derived molecular descriptions. Chapter 2 tackles the challenge of studying the chemical reactivity in large biological systems (e.g., peptides and proteins) with quantum mechanical methods. First, the precise photocatalytic functionalization at selenocysteine reaction developed by the Payne lab is simulated using a simplified model substrate followed by a more realistic model that generates the final energy profile. Based on the resulting computational analysis, the utility of this late-stage functionalization reaction is later demonstrated on large polypeptide chains. Chapters 3 and 4 embark on a journey into new bismuth chemistry developed by the Ball group. The bismuth arylation reaction published in Nature transformed the following collaborative work discussed here, ranging from the computational protocols implemented in selectivity problems to the versatile chemical reactivity originating from bismuth(V) reagents. From the previously reported but otherwise unexplored DFT integration grid effects, the computed free energies on organobismuth reactions explored here would have led to significant errors and incorrectly predicting selectivities. With the optimal computational protocols, new reactivity using organobismuth reagents is investigated in Chapter 3 to propose a reaction mechanism for the selective arylation of 2- and 4-pyridiones. Chapter 4 describes the mechanistic investigation of the developed palladium-catalyzed cross-coupling reaction to achieve challenging C-C couplings in mild reaction conditions with the amino-bridged bismacycle reagent. A statistical modeling approach using automated workflows discussed in Chapter 7 is applied here to predict an optimal reaction design and capture the origin of the reactivity for various coupling substrates and modified organobismuth(V)-reagents for the developed Bisma-Stille cross-coupling reaction. Chapter 5 describes a mechanistic investigation to optimize a challenging key reaction in the total synthesis of the natural product of allopupukeanane developed by the Sarpong group. The reaction success in late-stage synthetic plans becomes detrimental as the availability of reactants in a multiple-step natural product synthesis becomes limiting. The elementary step influencing the reactivity is identified in the palladium-mediated cascade reaction. Then, a data-driven approach is implemented to screen various ligands and collect mechanistically derived molecular DFT features to incorporate into a Bayesian optimization tool developed by the Doyle lab. Automated workflows discussed in Chapter 7 were utilized to collect the features. This approach successfully identified more suitable and efficient reaction conditions for racemic mixture, byproduct formation, and catalyst decomposition challenges. The overall synthesis plan to access multiple natural products via the bridged bicycle scaffold highlighted in this chapter is an ongoing project by the Sarpong group. Chapter 6 pivots into data-driven approaches to formulate statistical relationships sampled over small and large datasets. First, the collaborative research in section 6.2 dives into building a multivariate linear regression model with a small dataset to explain the reaction performance in various solvents on the challenging reversible nucleophilic alcohol addition reaction developed by the Bandar group. The statistical conclusions provide the bases for modeling the solvent effects via DFT methods. Next, in section 6.3, a machine learning model is trained on a large diverse molecule dataset to predict NMR chemical shifts with high accuracy to DFT-derived NMR values at only a fraction of the cost of DFT methods. Here are two examples where a successful prediction is evaluated based on the research goal to obtain model accuracy or interpretability. Chapter 7 focuses on facilitating the transparency and reproducibility for collecting and generating meaningful statistical models for the data chemist in low- and high-throughput studies. The open-source, automated workflows, DISCO and REGGAE, allowed for the execution of projects mentioned in Chapters 4 to 6 at different stages of the research process (e.g., chemical data collection, feature selection, and then statistical modeling).
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    Part I: Synthesis and characterization of titania and magnesium nanoparticles for hydrogen production and storage. Part II: Characterization and growth of branched silicon nanowires grown via a simultaneous vapor-liquid-solid and vapor-solid-solid mechanism
    (Colorado State University. Libraries, 2015) Shissler, Daniel Jay, author; Prieto, Amy, advisor; Shores, Matthew, committee member; Rappé, Anthony, committee member; Van Orden, Alan, committee member; Dandy, David, committee member
    To view the abstract, please see the full text of the document.
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    Synthesis and characterization of fluorine-containing C60 derivatives and their charge transfer photophysics in organic photovoltaics
    (Colorado State University. Libraries, 2013) Larson, Bryon W., author; Strauss, Steven H., advisor; Rumbles, Garry, advisor; Rappé, Anthony, committee member; Bartels, Randy, committee member; Chen, Eugene, committee member; Robinson, Raymond S., committee member
    Transformative advances in the science of new materials and technological solutions for energy conversion and storage require focused efforts from scientists across different disciplines. One of the major frontiers for modern chemistry is the molecular design of advanced materials from earth-abundant elements with finely tuned chemical, photophysical, and electronic properties. In this work, several highly efficient and, in some cases, highly regioselective synthetic methodologies have been developed for the first time that resulted in a wide array of versatile fullerene-based organic electron acceptors with highly tunable electronic properties. The classes of these newly synthesized and characterized materials include mono-perfluorocarbocyclic C60 derivatives, highly functionalizable ω-X-perfluoroalkylfullerenes (X = SF5, Br, I, COOEt), twenty one new isomers of deca-trifluoromethyl[60]fullerenes, and several new isomers of octa- and hexa-trifluoromethyl[60]fullerenes. Improved synthetic and separation techniques yielding up to multi-gram amounts of difluoromethylene[60]fulleroid and several other classes of technologically important perfluoroalkylfullerenes have also been developed, which enabled several organic photovoltaic-relevant studies using state-of-the art facilities at the National Renewable Energy Laboratory. This included the first experimental determination of an optimal driving force for the relative yield of free carrier generation in a family of polyfluorene polymers by using a series of trifluoromethylfullerene acceptors with a large range of electron affinities synthesized by the author. In another study, a judiciously selected series of acceptors was applied for a time-resolved microwave conductivity (TRMC) study that provided the first compelling experimental evidence that the yield for uncorrelated free charge generation in organic photovoltaic (OPV) device-relevant blends of donor:acceptor active layers is a function of carrier mobility. Finally, a new fullerene acceptor rivaling one of the champion fullerene derivatives, phenyl-C61-butyric acid methyl ester (PCBM), in OPV performance was studied by TRMC and in OPV devices.
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    Synthesis and characterization of iron and copper chalcogenide nanomaterials for photovoltaic applications
    (Colorado State University. Libraries, 2014) Fredrick, Sarah J., author; Prieto, Amy, advisor; Neilson, James, committee member; Rappé, Anthony, committee member; Strauss, Steven, committee member; Williams, John, committee member
    With our current looming energy and climate crises, it is vital that we find alternative forms of energy that have a lower carbon footprint. Solar technology is an excellent candidate for such purposes as the sun is an essentially unlimited source of renewable energy. However, the cost of solar cells is not economically competitive with fossil fuels. Alternatives to the traditional silicon solar modules could be a path toward reducing the cost of solar technology. The topic of this thesis is the synthesis and characterization of such alternatives. Iron and copper-based materials are earth abundant and potentially more cost-effective. Furthermore, processing these materials as nanocrystals, rather than bulk films, can reduce the energy input for fabricating solar absorber layers, and in turn, reduce overall system costs. Iron pyrite (FeS2) and a related material, Fe2GeS4 are two materials with near ideal properties for solar absorption. While there has been a great deal interest in FeS2, Fe2GeS4 is a novel system on which minimal research has been performed. Herein is described the synthesis and characterization of both of these iron chalcogenides with a particular focus on the challenging surface chemistry presented by these systems. Another system of increasingly widespread interest in recent years in the class of earth-abundant photovoltaic materials is Cu2ZnSnS4 (CZTS). A vast body of literature has been developed, but detailed characterization is lacking in much of the work, hindering our fundamental understanding of the properties. The final chapter of this thesis is a perspective work describing common characterization techniques for CZTS. It analyzes their usefulness in determining the formation of the pure CZTS phase, in hopes of improving current understanding of the material.