For life to arise from non-life, a metabolism must emerge and maintain itself, distinct from its environment. One line of research seeking to understand this emergence has focused on models of autocatalytic reaction networks (ARNs) and the conditions that allow them to approximate metabolic behavior. These models have identified reaction parameters from which a proto-metabolism might emerge given an adequate matter-energy flow through the system. This dissertation extends that research by answering the question: can dynamically structured interactions with the environment promote the emergence of ARNs? This question was inspired by theories that place the origin of life in contexts such as diurnal or tidal cycles. To answer it, an artificial chemistry system with ARN potential was implemented in the dissipative particle dynamics (DPD) modeling paradigm. Unlike differential equation (DE) models favored in prior ARN research, the DPD model is able to simulate environmental dynamics interacting with discrete particles, spatial heterogeneity, and rare events. This dissertation first presents a comparison of the DPD model to published DE results, showing qualitative similarity with some interesting differences. Multiple examples are then provided of dynamically changing flows from the environment that promote emergent ARNs more than constant flows. These include specific cycles of energy and mass flux that consistently increase metrics for ARN concentration and mass focusing. The results also demonstrate interesting nonlinear interactions between the system and cycle amplitude and period. These findings demonstrate the relevance that environmental dynamics has to ARN research and the potential for broader application as well.

Lime is one of the largest manufactured chemicals in the United States. The conversion of calcium carbonate into calcium oxide is an endothermic reaction and requires approximately two to four times the theoretical quantity of energy predicted from thermodynamic analysis. With the skyrocketing costs of fossil fuels, how to decrease the energy consumption in the calcination process has become a very important problem in the lime industry. In the present study, many chemicals including lithium carbonate, sodium carbonate, potassium carbonate, lithium chloride, magnesium chloride, and calcium chloride have been proved to be the catalysts to enhance the calcination rate of calcium carbonate. By mixing these chemicals with pure calcium carbonate, these additives can increase the calcination rate of calcium carbonate at constant temperatures; also, they can complete the calcination of calcium carbonate at relatively low temperatures. As a result, the energy required for the calcination of calcium carbonate can be decreased. The present study has aimed at developing a physical model, which is called the extended shell model, to explain the results of the catalytic calcination. In this model, heat transfer and mass transfer are two main factors used to predict the calcination rate of calcium carbonate. By using the extended shell model, not only the catalytic calcination but also the inhibitive calcination of calcium carbonate have been explained.