MODEL-BASED CONTROL WITH STOCHASTIC SIMULATORS: BUILDING PROCESS DESIGN AND CONTROL SOFTWARE FOR CATALYTICALLY ENHANCED MICROSYSTEMS

The production, characteristics, dynamics, and economics of microreactors were studied in this report. Overall it was found that the best microfabrication techniques for small scale processes were laser ablation, the LIGA process, soft lithography, and anisotropic wet chemical etching, roughly in ascending order of effectiveness. One of the few viable bonding techniques was found to be diffusion bonding followed by microlamination, whereas many coating methods -- such as solgel coating, modified anodic oxidation, and electrophoretic deposition -- were effective in μTAS integration. The high surface area to volume ratio of microreactors enables precise control of the temperature of the reactor along its axial dimension. Taking advantage of this feature in the design of microreactors leads to better control of complex reaction networks and generates more valuable effluent streams. A model predictive controller was implemented for the common, archetypical reaction network involving the hydrogenation and dehydrogenation of cyclohexene with various control objectives. It was found that the highest rate of production of benzene and cyclohexane occurred at 600 K while the most pure stream of benzene occurred at 200 K. Model predictive control was found to be highly resistant to the inherent stochasticity of small scale processes. The market for a software-based controller for microreactors was surveyed and found to still be in the early stages of development. A profitability analysis was conducted for a start-up company using microreactors to make cyclohexane. A price of $18,000 for the product was found to be a reasonable selling price yet allowed the start-up to remain profitable

Unveiling the key factor for the phase reconstruction and exsolved metallic particle distribution in perovskites

To significantly increase the amount of exsolved particles, the complete phase reconstruction from simple perovskite to Ruddlesden-Popper (R-P) perovskite is greatly desirable. However, a comprehensive understanding of key parameters affecting the phase reconstruction to R-P perovskite is still unexplored. Herein, we propose the Gibbs free energy for oxygen vacancy formation in Pr-0.5(Ba/Sr)(0.5)TO3-delta (T = Mn, Fe, Co, and Ni) as the important factor in determining the type of phase reconstruction. Furthermore, using in-situ temperature & environment-controlled X-ray diffraction measurements, we report the phase diagram and optimum 'x' range required for the complete phase reconstruction to R-P perovskite in Pr0.5Ba0.5-xSrxFeO3-delta system. Among the Pr0.5Ba0.5-xSrxFeO3-delta, (Pr0.5Ba0.2Sr0.3)(2)FeO4+delta - Fe metal demonstrates the smallest size of exsolved Fe metal particles when the phase reconstruction occurs under reducing condition. The exsolved nano-Fe metal particles exhibit high particle density and are well-distributed on the perovskite surface, showing great catalytic activity in fuel cell and syngas production. The complete phase reconstruction to Ruddlesden-Popper perovskite is greatly desirable to increase the exsolved particle distribution. Here, the authors report a key factor for the complete phase reconstruction in perovskites, leading to good catalytic activity in fuel cell and syngas production

Investigating Transition and Rare Earth Metal Oxide Relative Energetics Predictions for Improving Materials Selection Processes in Clean Energy Applications Using First-Principles Methods

The implementation of transition-metal oxides in clean energy applications requires the precise characterization of multiple properties of those materials, such that materials selection processes can appropriately choose materials for particular applications. One of the most extensive sets of properties required in materials characterization for these applications consists of the reaction energetics involving those materials, such as the oxygen vacancy formation energetics of perovskites and the formation energetics associated with phase transformations between different metal oxide polymorphs. In order to select a material for a particular application that satisfies multiple criteria associated with several properties, a large materials set should be considered, the size of which can be constrained by the expense of experimental procedures used to calculate those material properties. In order to address this concern, computational studies can be completed via the first-principles method of Density Functional Theory (DFT), which can be implemented to both calculate the values of experimentally known material properties within precision and – using the methodologies for calculating these known properties – predict the values of material properties for which adequate experimental data is unavailable. Given that comprehensive knowledge of error sources affecting the calculation of known and prediction of unknown material properties in DFT is not available, the validation of calculations of known materials is assessed with precision criteria involving relative energetics, determining the impact of accounting for potential error sources on relative energetic ordering to assess their significance. In this thesis, the prediction of largely unknown oxygen vacancy formation energetics of perovskite materials is completed within the criterion of relative energetic ordering of adjacent systems in energetic trends, evaluating the impact of oxygen vacancy concentration, crystal structure, magnetism, and electronic structure method variation on those trends. Given known information on the relative energetic ordering of TiO2 polymorphs, the impact of varying pseudopotentials, functionals, and other factors in energetic trends is also evaluated. Using previously resolved information concerning the identification of errors in perovskite and BO2 polymorph systems, the evaluation of differences between calculated and experimental formation energies of a broader set of binary metal oxide systems featuring transition or rare earth metal cations with incomplete d or f-shells was completed, in order to evaluate the physical or first-principles causes affecting multiple systems within that set

Investigating the Energetic Ordering of Stable and Metastable TiO₂ Polymorphs Using DFT+<i>U</i> and Hybrid Functionals

Prediction of transition metal oxide BO₂ (B = Ti, V, etc.) polymorph energetic properties is critical to tunable material design and identifying thermodynamically accessible structures. Determining procedures capable of synthesizing particular polymorphs minimally requires prior knowledge of their relative energetic favorability. Information concerning TiO₂ polymorph relative energetic favorability has been ascertained from experimental research. In this study, the consistency of first-principles predictions and experimental results involving the relative energetic ordering of stable (rutile), metastable (anatase and brookite), and unstable (columbite) TiO₂ polymorphs is assessed via density functional theory (DFT). Considering the issues involving electron–electron interaction and charge delocalization in TiO₂ calculations, relative energetic ordering predictions are evaluated over trends varying Ti Hubbard <i>U</i>_3d or exact exchange fraction parameter values. Energetic trends formed from varying <i>U</i>_3d predict experimentally consistent energetic ordering over <i>U</i>_3d intervals when using GGA-based functionals, regardless of pseudopotential selection. Given pertinent linear response calculated Hubbard <i>U</i> values, these results enable TiO₂ polymorph energetic ordering prediction. Hybrid functional calculations involving rutile–anatase relative energetics, though demonstrating experimentally consistent energetic ordering over exact exchange fraction ranges, are not accompanied by predicted fractions, for a first-principles methodology capable of calculating exact exchange fractions precisely predicting TiO₂ polymorph energetic ordering is not available

Effects of Concentration, Crystal Structure, Magnetism, and Electronic Structure Method on First-Principles Oxygen Vacancy Formation Energy Trends in Perovskites

Systematic prediction of the redox reaction energetics of large sets of 3d transition metal oxides is imperative to the selection of oxygen carrier candidates in applications ranging from chemical looping to solid oxide fuel cell (SOFC) cathode design. In particular, the energetic study of oxygen vacancy formation in unmixed perovskites with La, alkali, and alkaline A-site metal cationsas well as 3d transition metal B-site cationsis a crucial first step in understanding the energetic tunability afforded by cation doping in ABO₃ materials. An assessment of the relative oxygen vacancy formation energetics of LaBO₃, SrBO₃, and similar materials that serve as a guideline for predicting energetics in related systems is completed below using density functional theory (DFT). This assessment illustrates which simplifications can be made in the prediction of energetics trends without affecting trend order. The independent consideration of oxygen vacancy concentration, crystal structure, and antiferromagnetic (AFM) magnetism revealed that these factors in DFT calculations had no effect on trend order. However, the ferromagnetic (FM) SrBO₃ trend order was affected between SrMnO₃ and SrFeO₃ as a function of defect concentration. Moreover, energetic trends were also formed by adding constant, incremental values of the Hubbard <i>U</i> parameter contributing to the 3d orbitals of perovskite B-sites. Calculation of <i>U</i> parameters was done by linear response theory or by a literature review of previous research

Engineering Single Atom Catalysts to Tune Properties for Electrochemical Reduction and Evolution Reactions

Electrocatalysis is important to the conversion and storage of renewable energy resources, including fuel cells, water electrolysers, and batteries. Engineering metal-based nano-architectures and their atomic-scale surfaces is a promising approach for designing electrocatalysts. Single metal atom interactions with substrates and reaction environments crucially modulate the surface electronic properties of active metal centers, yielding controllable scaling relationships and transitions between different reaction mechanisms that improve catalytic activity. Single-atom catalysts (SACs) allow activity and selectivity tuning while maintaining relatively consistent morphologies. SACs have well-defined configurations and active centers within homogeneous single-atom dispersions, producing exceptional selectivities, activities, and stabilities. Furthermore, SACs with high per-atom utilization efficiencies, well-controlled substrate compositions, and engineered surface structures develop single atom active sites for molecular reactions, enhancing mass activities. Recent developments in different metal-based SAC nanostructures are discussed to explain their remarkable bi-functional electrocatalytic activities and high mechanical flexibility, especially in the oxygen evolution reaction, oxygen reduction reaction, carbon dioxide reduction reaction, hydrogen evolution reaction, and in battery applications. Existing barriers to and future insights into improving SAC performance are addressed. This study develops practical and fundamental insights on single atom electrocatalysts directed towards tuning their electrocatalytic activities and enhancing their stabilities. Electrocatalysis is important to the conversion and storage of renewable energy resources, including fuel cells, water electrolysers, and batteries. Engineering metal-based nano-architectures and their atomic-scale surfaces is a promising approach for designing electrocatalysts. Single metal atom interactions with substrates and reaction environments crucially modulate the surface electronic properties of active metal centers, yielding controllable scaling relationships and transitions between different reaction mechanisms that improve catalytic activity. Single-atom catalysts (SACs) allow activity and selectivity tuning while maintaining relatively consistent morphologies. SACs have well-defined configurations and active centers within homogeneous single-atom dispersions, producing exceptional selectivities, activities, and stabilities. Furthermore, SACs with high per-atom utilization efficiencies, well-controlled substrate compositions, and engineered surface structures develop single atom active sites for molecular reactions, enhancing mass activities. Recent developments in different metal-based SAC nanostructures are discussed to explain their remarkable bi-functional electrocatalytic activities and high mechanical flexibility, especially in the oxygen evolution reaction, oxygen reduction reaction, carbon dioxide reduction reaction, hydrogen evolution reaction, and in battery applications. Existing barriers to and future insights into improving SAC performance are addressed. This study develops practical and fundamental insights on single atom electrocatalysts directed towards tuning their electrocatalytic activities and enhancing their stabilities.11Nsciescopu

Universally characterizing atomistic strain via simulation, statistics, and machine learning: Low-angle grain boundaries

When applied to catalysis and related materials phenomena, grain boundary (GB) engineering optimizes over many currently disparately defined properties. Such properties include GB mobility, solute diffusivity, and catalytic footprints correlating current density with dislocation-induced strain. A recent universalizing framework has systematically classified low-Σ GBs in relation to analogous high-angle references, distinguishing them using footprints formed from the directional straining needed to reversibly yield bicrystals from their separated grains. Correlating the elastic work profiles derived from this thermodynamic process with matching changes in GB dislocations, strain footprints can comprehensively link formerly disparate catalytic properties and materials phenomena. This research investigates such structure-energy correlations to evaluate differences between low-angle (LAGBs) and high-angle (HAGBs) GBs, systematically delineating LAGB-HAGB transitions, explaining their origins, and connecting transitions to materials phenomena. A hierarchical statistical model, nesting GB degrees of freedom within one another, systematically detects such transitions via simplified strain footprints without failure of a single unique GB structure and material combination. A more comprehensive analysis of footprint directional components and discontinuities links transitions to catalytically relevant materials phenomena, describing thermal grooving, shear coupling, complexions, and defect migration under a single universal atomistic framework. With machine learning and spatially generalized strain footprints, this framework reconciles such phenomena via more comprehensive geometry-energy correlations.11Nsciescopu