Computational Chemistry Studies of Organometallic Energy Landscapes

Computational chemistry is becoming a widely used tool to investigate the kinetics and thermodynamics of chemical transformations. These investigations are often heavily guided by experiment and require significant mechanistic insight prior to meaningful model development. Recent advances in reaction path finding and automated potential energy surface assessment have enabled faster and easier exploration of complex chemical mechanisms. In combination with mechanistic information, structure energy correspondence provides information which describes how a particular reaction mechanism energetically varies as structure is modulated. Together, the relevant reaction pathways and the structure energy relationships describe the reaction landscape for a given class of reactivity. Chapter 1 introduces the core chemical concepts needed to understand reaction landscapes. The tools and information needed to perform detailed mechanistic exploration via computation are presented and competing methods are summarized. Further discussion of reaction path finding tools is provided through an example involving the reactivity of ammonia borane and carbon dioxide. A discussion of the characteristics which connect potential energy surfaces to quantitative structure activity relationships is used to conclude this chapter. Chapter 2 details the application of an automated reaction path finding tool for the investigation of intuitive and non-intuitive pathways for C(sp3)-N reductive elimination from palladium(IV). This work demonstrates that detailed computational studies using automated reaction path investigation can be used to assess unexpected reaction pathways. These simulations predicted the relative reaction rates with various sulfonamides through consideration of both intuitive and non-intuitive reaction mechanisms. Overall, this chapter demonstrates that combinations of experimental studies and computational tools can provide fundamental mechanistic insights into complex organometallic reaction pathways. This work begins to explore relevant molecular features which appear to trend well with the experimentally observed reactivity. Chapter 3 continues the development of molecular feature based investigation. This chapter was inspired by the possibility of using computational investigations of complex organometallic reaction landscapes to describe structure energy correspondence. This section discusses the development of a thermodynamic landscape to investigate CO2 reduction from cobalt bis(diphosphine) complexes. The construction of a dataset of Co(L)(L’)H2 type complexes from set of commercially available of bis(diphosphines) covering a thermodynamic landscape of over 50 orders of magnitude acidity and hydricity is discussed. These data suggest that relationships between common steric and electronic molecular features are poorly correlated with catalyst thermodynamics. However, a strong correlation between the thermodynamics and Co—H NLMO energy is observed. The landscape provides a clear example of careful electronic balance required for catalytic relevance. The best catalyst identified for future experimental investigations was Co(dCype)H, which is expected to be more acidic and hydridic than previously reported Co(dmpe)2H. While there is still significant work remaining in the development of robust and automated computational chemistry tools, this work outlines some potential applications and details the relevant findings. The final chapter discusses the current limitations and challenges associated with computational reaction discovery. Particular attention is paid to the development of reasonable organometallic computational models for use in reaction landscape investigation.PHDChemistryUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttps://deepblue.lib.umich.edu/bitstream/2027.42/143995/1/ipendlet_1.pd

Quantitative Modeling in Cell Biology: What Is It Good for?

Recently, there has been a surge in the number of pioneering studies combining experiments with quantitative modeling to explain both relatively simple modules of molecular machinery of the cell and to achieve system-level understanding of cellular networks. Here we discuss the utility and methods of modeling and review several current models of cell signaling, cytoskeletal self-organization, nuclear transport, and the cell cycle. We discuss successes of and barriers to modeling in cell biology and its future directions, and we argue, using the field of bacterial chemotaxis as an example, that the closer the complete systematic understanding of cell behavior is, the more important modeling becomes and the more experiment and theory merge

Experimental and modeling investigation of the OH-initiated oxidation of semi-solid and aqueous saccharide aerosols

My research focuses on investigating the impact of moisture-induced and oligomer-induced viscosity changes on OH-initiated oxidation of semi-solid aerosols,and the role of gas-liquid interfaces in regulating aqueous aerosol chemistry. Saccharides, which are a major constituent of aqueous atmospheric aerosols, are chosen as model molecules to form highly oxygenated organic aerosols. The experiments are performed using an atmospheric pressure flow-tube reactor with both online VUV-AMS (Vacuum-Ultraviolet Aerosol Mass Spectrometer) and offline GC-MS analysis techniques. The decay rates of saccharide are determined by measuring the loss signal of saccharide in the particle phase as a function of OH exposure (time-integrated total concentration of OH radical). A reaction-diffusion model is developed to interpret the observed kinetics behavior. These results highlight that the chemical transformation of semi-solid aerosols is kinetically limited by bulk diffusion and that of aqueous aerosol is dependent on surface-bulk partitioning. The kinetics of the OH-initiated oxidation of semi-solid monosaccharide particles are obtained over a range of relative humidity (RH) in order to investigate the impact of moisture-induced viscosity changes on the mechanisms of oxidative aging of semi-solid aerosols. The reactive uptake coefficient of monosaccharide ( increases by a factor of 2.4 as the surrounding RH is increased from 10% to 30%. A reaction-diffusion kinetic model with a constant diffusion coefficient is developed to investigate the impact of bulk molecule diffusion on kinetics behavior of semi-solid aerosols. This study suggests that the diffusion of the bulk reactant from the particle inner core to its surface is the rate-limiting step in oxidation of the semi-solid aerosols. In order to investigate the oligomer-induced viscosity changes on reactive properties of semi-solid aerosols, reactive uptake coefficients are measured over a range of monosaccharide:disaccharide molar ratio ranging between 1:1 and 4:1 at 30% RH. The reactive uptake coefficient of monosaccharide is found to decrease by a factor of 5 as the molar ratio changing from 4:1 to 1:1. The observed decay behaviors can be reproduced by using a simple compositional Vignes relationship to predict the composition-dependent diffusion coefficients of the saccharides. Simulation results suggest that a gradient diffusivity arises due to concentration gradients across the particle through heterogeneous oxidation of semi-solid particles. These findings illustrate the impact of bulk composition on reactant bulk diffusivity, which determines the rate-limiting step during the chemical reaction of semi-solid multi-component particles. For equimolar monosaccharide-disaccharide aqueous aerosols, the reactive uptake coefficient of monosaccharide is 5.02±1.12 and the reactive uptake coefficient of disaccharide is 0.39±0.10. Molecular dynamics simulations of the mixed aqueous solutions reveal the formation of a ~10 Å disaccharide exclusion layer below the water surface. The monosaccharide concentration is predicted to be low at the surface and to increase rapidly within the first 10 Å of the air-water interface. The observed decays are consistent with a poor spatial overlap of the OH radical at the interface with the disaccharide in the particle bulk. These findings highlight the critical importance of partitioning of bulk reactant at the gas-liquid interface in determining the reaction rate of reactive species in aqueous aerosols

Machine learning activation energies of chemical reactions

Application of machine learning (ML) to the prediction of reaction activation barriers is a new and exciting field for these algorithms. The works covered here are specifically those in which ML is trained to predict the activation energies of homogeneous chemical reactions, where the activation energy is given by the energy difference between the reactants and transition state of a reaction. Particular attention is paid to works that have applied ML to directly predict reaction activation energies, the limitations that may be found in these studies, and where comparisons of different types of chemical features for ML models have been made. Also explored are models that have been able to obtain high predictive accuracies, but with reduced datasets, using the Gaussian process regression ML model. In these studies, the chemical reactions for which activation barriers are modeled include those involving small organic molecules, aromatic rings, and organometallic catalysts. Also provided are brief explanations of some of the most popular types of ML models used in chemistry, as a beginner's guide for those unfamiliar

Assessment of the Thermal Degradation of Sodium Lauroyl Isethionate Using Predictive Isoconversional Kinetics and a Temperature-Resolved Analysis of Evolved Gases

Sodium lauroyl isethionate is a popular, milder alternative to traditional soaps and surfactants in personal care formulations. Product performance, efficiency, color, and odor, however, can be compromised by thermal degradation at elevated manufacturing temperatures. Prediction of isothermal degradation rates in both air and N2 for a range of process conditions are determined using the Friedman isoconversional method. The thermal degradation levels in air are found to be 28 times higher than those in N2 over 5 h at 240 °C. Manufacturing under inert conditions, with maximum temperatures of 250 °C, is therefore necessary to avoid degradation levels significantly greater than 1 wt %. Using TGA-FTIR, the evolved gases from the degradation of sodium lauroyl isethionate are identified to be water, carbon dioxide, carbon disulfide, sulfur dioxide, as well as alkyl and carbonyl species. The ensuing temperature-dependent analysis can be used to minimize evolution of undesirable or hazardous gases in isethionate manufacturing processes

Controlo de temperatura de um gasificador de biomassa

In recent history, the growing environmental crisis and the unsustainable overuse of fossil fuels have become a catalyst for the development of environmentally friendly or carbon neutron energy sources. Such fact lead to the reemergence of gasification in the research and development community. This technology was prominent during World War II due to the unavailability of oil existent at that time, mostly using coal as fuel. With the end of the war, so came the end of its development. Initially, the literature will be reviewed in order to assess the instrumentation technologies needed to measure the gasification process’ operational parameters, and thus, allow its monitoring and control. In order to facilitate the analysis of the data from the developed instrumentation system, a visualization tool was developed. The literature was then reviewed again in order to find the most suitable model topology for the gasification process. This revealed neural networks as the most reliable model architecture for such endeavor. A gasification model was then devised using experimental data present in the literature. The devised model was then used to establish a simulation and controller design environment. This enabled the development of Model Predictive Controller to control the temperature inside the gasifier. The devised model showed great potential as a prediction model, in spite of the deterioration presented when used as a simulator. The developed controller was able to stabilize the model generated output for all tested set-points. The develop work constitutes a solid ground for future work.O desenfreado crescimento da crise ambiental e uso insustentável de combustíveis fósseis vivido nas últimas décadas tem vindo a tornar-se num catalisador na busca de soluções carbonicamente neutras de produção de energia. Este facto levou ao ressurgimento dos processos de gasificação, principalmente de biomassa, como um tema na comunidade de pesquisa e desenvolvimento. Esta tecnologia foi predominante durante a segunda guerra mundial, período no qual a dificuldade de obtenção de petróleo levou acréscimo da sua necessidade, sendo carvão o combustível utilizado. Com o fim da guerra, veio também o fim do seu desenvolvimento. Inicialmente, será realizada uma revisão de literatura que culminará na escolha dos instrumentos de medição e atuação necessários para proceder à monitorização e controlo dos parâmetros operacionais do processo de gasificação. De modo a facilitar a analise dos dados presentes nestes sensores foi desenvolvida uma aplicação de visualização de informação. Findada esta etapa procedeu-se a uma nova revisão da literatura focada na procura de um modelo para o processo de gasificação. Esta revisão revelou as redes neuronais como sendo a melhor topologia para descrever o processo. Utilizando dados disponíveis na literatura procedeu-se à identificação do sistema em causa. O modelo desenvolvido foi utilizado para estabelecer um ambiente de simulação e desenho de controladores e assim, desenvolver um controlador preditivo baseado em modelo para controlar a temperatura dentro do gasificador. O modelo desenvolvido apresenta um grande potencial como modelo de predição, apesar da deterioração do seu desempenho quando usado como simulador. O controlador desenvolvido foi capaz de estabilizar a saída gerada pelo modelo de simulação para todos os set-points testados. O trabalho desenvolvido constitui uma base de trabalho bastante completa que deverá facilitar desenvolvimentos futuros.Mestrado em Engenharia Eletrónica e Telecomunicaçõe

Identifying Drug Effects via Pathway Alterations using an Integer Linear Programming Optimization Formulation on Phosphoproteomic Data

Understanding the mechanisms of cell function and drug action is a major endeavor in the pharmaceutical industry. Drug effects are governed by the intrinsic properties of the drug (i.e., selectivity and potency) and the specific signaling transduction network of the host (i.e., normal vs. diseased cells). Here, we describe an unbiased, phosphoproteomicbased approach to identify drug effects by monitoring drug-induced topology alterations. With the proposed method, drug effects are investigated under several conditions on a cell-type specific signaling network. First, starting with a generic pathway made of logical gates, we build a cell-type specific map by constraining it to fit 13 key phopshoprotein signals under 55 experimental cases. Fitting is performed via a formulation as an Integer Linear Program (ILP) and solution by standard ILP solvers; a procedure that drastically outperforms previous fitting schemes. Then, knowing the cell topology, we monitor the same key phopshoprotein signals under the presence of drug and cytokines and we re-optimize the specific map to reveal the drug-induced topology alterations. To prove our case, we make a pathway map for the hepatocytic cell line HepG2 and we evaluate the effects of 4 drugs: 3 selective inhibitors for the Epidermal Growth Factor Receptor (EGFR) and a non selective drug. We confirm effects easily predictable from the drugs’ main target (i.e. EGFR inhibitors blocks the EGFR pathway) but we also uncover unanticipated effects due to either drug promiscuity or the cell’s specific topology. An interesting finding is that the selective EGFR inhibitor Gefitinib is able to inhibit signaling downstream the Interleukin-1alpha (IL-1α) pathway; an effect that cannot be extracted from binding affinity based approaches. Our method represents an unbiased approach to identify drug effects on a small to medium size pathways and is scalable to larger topologies with any type of signaling perturbations (small molecules, 3 RNAi etc). The method is a step towards a better picture of drug effects in pathways, the cornerstone in identifying the mechanisms of drug efficacy and toxicity