Systems metabolic engineering of microbial cell factories for the synthesis of value-added chemicals

Varman, Arul Mozhy Washington University in St. Louis 2013 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Microbial cell factories offer us an excellent opportunity for the conversion of many different cheaply available raw materials into valuable chemicals. Systems metabolic engineering aims at developing rational strategies for the engineering of microbial hosts by providing global level information of a cell. This dissertation focuses on metabolic engineering, bioprocess modeling and pathway analysis, to develop robust microbial cell factories for the synthesis of value-added chemicals. The following research tasks were completed in this regard. First, statistical models were developed for the prediction of product yields in engineered microbial cell factories - Saccharomyces cerevisiae and Escherichia coli (Chapter 2). A large space of experimental data for chemical production from recent references was collected and a statistics-based model was developed to calculate production yield. The input variables (numerical or categorical variables) for the model represented the number of enzymatic steps in the biosynthetic pathway of interest, metabolic modifications, cultivation modes, nutrition and oxygen availability. In addition, the use of 13C-isotopomer analysis method was proposed for the accurate determination of product yields in engineered microbes under complex cultivation conditions (Chapter 3). Second, metabolic engineering of the cyanobacterium, Synechocystis sp. PCC 6803 was performed for synthesizing isobutanol under phototrophic conditions (Chapter 4). With the expression of the heterologous genes from the Ehrlich Pathway, by incorporating an in situ isobutanol harvesting system, and also by employing mixotrophic conditions, the engineered Synechocystis 6803 strain accumulated a maximum of ~300 mg/L of isobutanol in a 21 day culture. In addition, Synechocystis 6803 was engineered for the synthesis of D-lactic acid (Chapter 5), via overexpression of a novel D-lactate dehydrogenase (encoded by gldA101). The production of D-lactate was further improved by employing three strategies: (i) cofactor balancing, (ii) codon optimization, and (iii) process optimization. The engineered Synechocystis 6803 produced 2.2 g/L D-lactate under photoautotrophic conditions with acetate, the highest reported lactate titer among all known cyanobacterial strains. Finally, an E. coli cell factory was engineered to study the fermentation kinetics for scaled-up isobutanol production (Chapter 6). Through kinetic modeling (to describe the dynamics of biomass, products and glucose concentration) and isotopomer analysis, we have also offered metabolic insights into the performance trade-off between two engineered isobutanol producing E. coli strains (a high performance and a low performance strain). The kinetic model can also predict isobutanol production under different fermentation conditions. I and my colleagues have also demonstrated that E. coli cell factory can also be used for converting waste acetate into free fatty acids through metabolic engineering. In conclusion, the opportunities and commercial limitations with current biotechnology as well as the role of systems metabolic engineering for the development of high performance microbial cell factories were discussed (Chapter 7).

Optimization of Electrochemical Devices for Sustainable Chemical Manufacturing

Frey, Daniel New York University Tandon School of Engineering P 2022 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Our increased efforts to curb global warming have led to a drastic surge in the deployment of renewable electricity sources, such as wind and solar power. However, as these sources form a larger fraction of the energy in the grid, their intermittency has started to cause supply instability and large fluctuations in energy prices. Electrochemical energy storage devices have started to enter the utility-scale energy storage market to address this need, but high costs associated with manufacturing and maintain these devices have limited their impact, especially for long-duration energy storage. As an alternative to using battery systems for storage, electrochemically produced fuels from water and/or CO2 have been proposed as viable storage opportunities, especially for seasonal energy storage requirements. H2 has gained significant attention as a promising energy vector for a renewable-rich energy future given its high gravimetric energy density that makes it desirable for both stationary and mobile applications. Despite the fact that clean H2 can be produced electrochemically through water electrolysis, the high cost of this method has limited its deployment, mainly due to the electricity costs. In the same way, CO2 has the ability to be reduced to many useful products such as carbon monoxide and ethylene, but the selectivity and energy efficiency of these devices need to be improved. The work presented in this dissertation aims at providing technological solutions to these challenges that preclude the deployment of electrochemical technologies at scale. In particular, I describe the development and implementation of Bayesian learning methods to optimize electrochemical devices that go beyond the more common Edisonian approaches used in the development of these devices. Edisonian search approaches are widely used in the chemical sciences to discover reactions, process conditions, material compositions, or product formulations with optimal performance for their intended application. These experimental design methods rely on the generation of grids of variables where experimentally accessible conditions are systematically and/or combinatorically explored. While these methods are simple to implement, they often evaluate a suboptimal parameter space where the quality of information derived depends on the numbers of combinations of variables explored, slowing and sometimes preventing the identification of optimal conditions. These shortcomings represent significant impediments for expensive experimental campaigns or those with large design spaces that can only afford the implementation of coarse experimental grids, underscoring the need for more efficient experimental optimization methods. In order to implement the electrochemical devices described above, it is important to be able to improve the speed of experimental campaigns to ultimately implement these systems when they can make the most impact.The following chapters cover the characterization and optimization of two renewable fuel systems, as well as a physics-based Bayesian optimization method for improved optimization of these electrochemical systems. In Chapter 2, a cerium-mediated energy storage and hydrogen production system is introduced and characterized. In Chapter 3, a technoeconomic analysis is performed that optimized the operation schedule and sizing of the system to minimize hydrogen production cost. In Chapter 4, a study and optimization of potential pulses on the cerium(III) oxidation reaction is presented with the goal of improving the efficiency of this reaction. In Chapter 5, the optimization of potential pulses on a CO2 electroreduction device is described. Finally, in Chapter 6 a chemically-informed Bayesian optimization algorithm is introduced.

Assembly and characterization of quantum-dot solar cells

Leschkies, Kurtis Siegfried University of Minnesota 2009 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Environmentally clean renewable energy resources such as solar energy have gained significant attention due to a continual increase in worldwide energy demand. A variety of technologies have been developed to harness solar energy. For example, photovoltaic (or solar) cells based on silicon wafers can convert solar energy directly into electricity with high efficiency, however they are expensive to manufacture, and thus unattractive for widespread use. As the need for low-cost, solar-derived energy becomes more dire, strategies are underway to identify materials and photovoltaic device architectures that are inexpensive yet efficient compared to traditional silicon solar cells. Nanotechnology enables novel approaches to solar-to-electric energy conversion that may provide both high efficiencies and simpler manufacturing methods. For example, nanometer-size semiconductor crystallites, or semiconductor quantum dots (QDs), can be used as photoactive materials in solar cells to potentially achieve a maximum theoretical power conversion efficiency which exceeds that of current mainstay solar technology at a much lower cost. However, the novel concepts of quantum dot solar cells and their energy conversion designs are still very much in their infancy, as a general understanding of their assembly and operation is limited. This thesis introduces various innovative and novel solar cell architectures based on semiconductor QDs and provides a fundamental understanding of the operating principles that govern the performance of these solar cells. Such effort may lead to the advancement of current nanotechnology-based solar power technologies and perhaps new initiatives in nextgeneration solar energy conversion devices. We assemble QD-based solar cells by depositing photoactive QDs directly onto thin ZnO films or ZnO nanowires. In one scheme, we combine CdSe QDs and single-crystal ZnO nanowires to demonstrate a new type of quantum-dot-sensitized solar cell (QDSSC). An array of ZnO nanowires was grown vertically from a fluorine-doped-tin-oxide conducting substrate and decorated with an ensemble of CdSe QDs, capped with mercaptopropionic acid. When illuminated with visible light, the CdSe QDs absorb photons and inject electrons into the ZnO nanowires. The morphology of the nanowires then provided these photoinjected electrons with a direct and efficient electrical pathway to the photoanode. When using a liquid electrolyte as the hole transport medium, our quantum-dot-sensitized nanowire solar cells exhibited short-circuit current densities up to 2.1 mA/cm 2 and open-circuit voltages between 0.6--0.65 V when illuminated with 100 mW/cm2 of simulated AM1.5 light. Our QDSSCs also demonstrated internal quantum efficiencies as high as 50--60%, comparable to those reported for dye-sensitized solar cells made using similar nanowires. We found that the overall power conversion efficiency of these QDSSCs is largely limited by the surface area of the nanowires available for QD adsorption. Unfortunately, the QDs used to make these devices corrode in the presence of the liquid electrolyte and QDSSC performance degrades after several hours. Consequently, further improvements on the efficiency and stability of these QDSSCs required development of an optimal hole transport medium and a transition away from the liquid electrolyte. Towards improving the reliability of semiconductor QDs in solar cells, we developed a new type of all-solid-based solar cell based on heterojunctions between PbSe QDs and thin ZnO films. We found that the photovoltage obtained in these devices depends on QD size and increases linearly with the QD effective bandgap energy. Thus, these solar cells resemble traditional photovoltaic devices based on a semiconductor--semiconductor heterojunction but with the important difference that the bandgap energy of one of the semiconductors, and consequently the cell's photovoltage, can be varied by changing the size of the QDs. Under simulated 100 mW/cm2 AM1.5 illumination, these QD-based solar cells exhibit short-circuit current densities as high as 15 mA/cm2 and open-circuit voltages up to 0.45 V, larger than that achieved with solar cells based on junctions between PbSe QDs and metal films. Moreover, we found that incident-photon-to-current-conversion efficiency in these solar cells can be increased by replacing the ZnO films with a vertically-oriented array of single crystal ZnO nanowires, separated by distances comparable to the exciton diffusion length, and infiltrating this array with colloidal PbSe QDs. In this scheme, photogenerated excitons can encounter a donor--acceptor junction before they recombine. Thus, we were able to construct solar cells with thick QD absorber layers that were still capable of efficiently extracting charge despite short exciton or charge carrier diffusion lengths. When illuminated with the AM1.5 spectrum, these nanowire-based quantum-dot solar cells exhibited power conversion efficiencies approaching 2%, approximately three times higher than that achieved with thin film ZnO devices constructed with the same amount of QDs. Supporting experiments using field-effect transistors made from the PbSe QDs as well as the sensitivity of these transistors to nitrogen and oxygen gas show that the solar cells described above are unlikely to be operating like traditional p--n heterojunction solar cells. All data, including significant improvements in both photocurrent and power conversion efficiency with increasing nanowire length, suggest that these photovoltaic devices operate as excitonic solar cells.

Aerosol Processes Enabling Solar Energy Applications

An, Woo Jin Washington University in St. Louis 2012 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Development of alternative energy sources is essential to satisfy future energy demands. While remaining fossil fuel resources may substantially meet these demands, anthropogenic carbon dioxide (CO2) emissions from combustion processes will continue accumulating in the atmosphere and thus exacerbate climate change. There are a variety of carbon-free energy sources, including hydroelectricity, tidal energy, wind energy, biomass, and solar energy. Among those, solar energy would allow for a natural resolution to global energy problems if harvested effectively and used efficiently. Although solar energy can produce clean power in a renewable and sustainable manner, its expensive unit cost, which is mostly attributed to its high production cost, makes people hesitate to switch their energy sources. Artificial photosynthesis, which borrows partial steps from natural photosynthesis, suggests promising ways to produce clean energy: electron generation by light-harvesting molecules, hydrogen (H2) formation by water photolysis, and transformation of CO2 into hydrocarbon fuels. Photocatalytic metal oxides are commoly used for those energy production processes. Photocatalytic metal oxides, such as titanium dioxide (TiO2), are attractive materials for solar energy applications, since they are low-cost materials and are a plentiful resource. The applicability of photocatalytic metal oxides will be enhanced if an economically viable process for synthesis of highly efficient metal oxide thin films is developed. In the work reported here, Aerosol processes, which are simple operations that can be easily scaled up, were used to fabricate metal oxide-based solar cell devices. Aerosol-chemical vapor deposition (ACVD), a simple, one-step process operating at atmospheric pressure, was developed to deposit nanostructured metal oxide films with controlled morphologies. The as-synthesized nanostructured thin films were used as photoanodes for both dye-sensitized solar cells and water photolysis. One problem is that the fast recombination of photogenerated electron-hole pairs suppresses the electrochemical reaction. It was found that noble metal nanoparticles with specific sizes can delay the recombination of electron-holes by forming junctions with metal oxides. Long lived photogenerated electrons improved device performance in both water photolysis and photocatalytic CO2 reduction. Along with the fast recombination of electron-hole pairs, another critical problem is limited light absorption of photocatalytic metal oxide, especially TiO 2. Quantum dots (QDs) are promising solid-state photosensitizers, whose optical properties can be tuned by controlling their sizes. An electrospray system was employed to deposit QDs onto the nanostructured metal oxide films to enhance light absorption in the visible regime. Unlike existing methods such as chemical linking and chemical bath deposition, the electrospray method rapidly deposits QDs in a controlled manner. Solar irradiance mostly lies in the visible as well as the near infrared (NIR) regime. Natural light-harvesting complexes inspired us to develop a concept of a bio-hybrid solar device. The device could harvest photons over a wide range of visible and NIR light by combining chlorosomes as a light antenna system and lead sulfide (PbS) QDs as artificial reaction centers for charge separation. In conclusion, ACVD and an electrospray system was used to fabricate highly efficient photoelectrodes for solar energy applications. These aerosol processes are expected to reduce the production cost of solar energy devices, and eventually will accelerate the wide utilization of solar energy.

Overcoming the Limits of Methane Bioconversion: Computational Tools for the Industrialization of Polyhydroxybutyrate (PHB) from Methane

Meraz, Jorge Luis Stanford University ProQuest Dissertations & These 2023 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Humanity's dependency on fossil-derived products has led to global pollution across our land, water, and air environments. In particular, petroleum-based plastic pollution is pervasive across all environments, with significant contributions to global oceanic plastic pollution and greenhouse gas emissions. Current trends in conventional fossil carbon-based plastics manufacturing continue polluting at an alarming rate, requiring sustainable alternatives to chemical-based plastics. Sustainable material needs, both in quantity and quality, can be met via use of biological organisms. Methanotrophic organisms are a promising biotechnology that can address environmental concerns of plastics pollution and greenhouse gas emissions. Methanotrophs grow by consuming a potent greenhouse gas, methane, as their sole source of carbon and energy. As these organisms grow, they can eventually transform excess methane into a biodegradable plastic, polyhydroxybutyrate (PHB). PHB has similar material properties to conventional chemical-based plastics, showing promise as a replacement to fossil fuel-derived plastics. Using methane as a carbon feedstock for PHB is attractive due to its relative abundance, low-cost, and climate mitigation potential. Methane has a global warming potential that is over 20 times that of carbon dioxide (CO2), removing it before it enters the atmosphere is essential mitigate continued climate change impacts. This dissertation investigates the potential of methanotrophic PHB production via a critical review and a series of computational studies that include equilibrium, dynamic, and techno-economic analysis (TEA) models.This thesis comprises 3 research chapters that synthesize and evaluate the potential of methane based PHB production. Chapter 2 highlights methane's potential as a feedstock, synthesizing current trends in engineered systems that utilize or have the potential to utilize methane effectively as a substrate. In addition, chapter 2 summarizes methanotrophic organisms' metabolic diversity and broad range of observed microbial kinetic parameters (e.g., specific growth rate, yield). Finally, in chapter 2, using fundamentals of biotechnology and chemical engineering, an equilibrium model is developed to assess rate limitations of methanotrophic growth across a range of observed mass transfer and volumetric consumption rates. Next, in Chapter 3, a dynamic first principles PHB accumulation model is developed. The model considers physical, chemical, and biological kinetics of a methanotrophic bioreactor and is used to evaluate PHB productivity and energy efficiency considering a broad range of bioreactor operating conditions. Chapter 4 is techno-economic analysis model that investigates how the design of industrial scale bioreactor systems impact PHB cost, in addition to considering the social cost of carbon (SCC) from the PHB production process. The model is used to analyze cost and SCC impacts considering various physical design approaches that include the size of the fermenter, the rate of mixing, whether the system is pressurized, and the size and type of centrifuge and dryer. Additionally, the model incorporates the process impacts of methanotrophic microbial kinetics. Together, these research chapters highlight the potential of methane and methanotrophs as a robust technology platform that can produce sustainable, biodegradable alternatives to chemical-based plastics, while mitigating the release of a potent greenhouse gas.

Energetics of Small Molecules and Molecular Fragments on Model Catalyst Surfaces: Adsorption Calorimetry on Pt(111) and Cu(111)

Ruehl, Griffin University of Washington ProQuest Dissertations & 2022 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Heterogeneous catalysis is essential for the development and support of modern society, with the vast majority of chemical production processes reliant on catalysts. New catalysts and catalytic reactions constitute promising pathways forward in combatting the effects of climate change and transitioning human society off of our reliance on fossil fuels. However, there is an absence of a complete fundamental understanding of observed differences and trends in catalytic behavior that impedes the rapid, strategic development of new catalytic processes.Computational modeling methods, such as Density Functional Theory (DFT), constitute powerful tools for the rapid screening of catalyst materials, but these methods have large errors in energy accuracy which severely limit their quantitative predictive abilities. These methods are dependent on experimentally determined benchmarks to guide modifications for improving their energy accuracy. The technique of single crystal adsorption calorimetry (SCAC) is uniquely able to study the energetics of irreversible adsorption processes on well-defined surface sites. SCAC can therefore provide these key benchmarks and fundamental understandings of the energetics of molecular and dissociative adsorption into molecular fragments and other key surface reaction intermediates commonly seen in industrial catalytic applications.This dissertation presents experimental SCAC results for the study of the energetics of adsorption of small molecules and molecular fragments on model catalyst surfaces, namely Pt(111) and Cu(111). This work builds upon previous efforts from the Campbell group to develop a systematic understanding of trends and observed differences in catalytic behavior on late-transition metal catalysts. Additionally, by employing models recently developed by this group, we are able to estimate the adhesion energies of liquid solvents to clean, single-crystal metal surfaces from the experimental calorimetry results. This allows for the estimation of the effects of each solvent on the energetics of adsorption and desorption for surface reactants and intermediates of interest.The study of the energetics of acetonitrile and n-decane adsorption on Pt(111), two solvents of particular interest, are reported here. Acetonitrile an important solvent due to its unique, desirable properties which make it of particular interest for electrochemical applications and the engineering of mixed solvent environments. n-Decane is similarly of interest in catalysis as linear alkanes of that and similar size are commonly used as solvents in catalytic reactions over Pt-group metals. From the experimentally determined heat of adsorption versus coverage we estimate adhesion energies of these liquid solvents to the Pt(111) surface to be Eadh = 0.198 J/m2 for acetonitrile and Eadh = 0.148 J/m2 for n-decane. Additionally, the adhesion energy of liquid formic acid to Cu(111) is estimated to be Eadh = 0.271 J/m2. These values can be used to quantify the solvent effects of these species on the local surface reaction environment.The calorimetrically measured heats of adsorption versus coverage are reported here for acetonitrile on Pt(111) at 100 K and 180 K, n-decane adsorption on Pt(111) at 150 K, azulene adsorption on Pt(111) at 150 K, and for both the molecular and dissociative adsorption of formic acid on clean and oxygen-precovered Cu(111). In combination with previously reported experimental results and DFT simulations of these systems, a number of important fundamental insights are drawn. The analysis of the n-decane heats of adsorption in comparison to a previous TPD study of shorter linear alkanes extends the observed trends to larger species such as n-decane that desorb irreversibly. Namely, we report that the adsorption energy increases nearly proportionally to carbon number, and the adhesion energy remains nearly constant (for a given surface).Naphthalene and azulene are of particular interest as representative molecules for the regular structure of graphene and the most common defect found in graphene sheets, respectively. Therefore the study of their adsorption energetics can inform experimental and computational systems involving graphene more broadly. Comparison of the heats of adsorption for azulene on Pt(111) first presented here with previous results for naphthalene and DFT simulations of both show that azulene binds significantly stronger to Pt(111) (by ~100 kJ/mol) than its isomer naphthalene. We show that DFT accurately predicts the adsorption energy of azulene but overestimates the binding energy of naphthalene, indicating that DFT is not accurately modeling the energy differences between these two systems.We report here the dissociative adsorption of formic acid on oxygen-precovered Cu(111), which results in the formation of adsorbed bidentate formate and gaseous water at 240 K. Formic acid and formate are common intermediates in a variety of reactions on late transition metals, ranging from well-established industrial reactions to emergent clean energy technologies. From the heats of this dissociative adsorption reaction, we extract a bond enthalpy of bidentate formate to Cu(111) of 335 kJ/mol, and an enthalpy of formation of bidentate formate on Cu(111) of -465 kJ/mol. We show that these enthalpies are slightly greater than those on Ni(111) (by ~15 kJ/mol) and significantly greater than those on Pt(111) (by ~85 kJ/mol). This is in opposition to the predicted order of bond strength from DFT, where Ni is predicted to bind formate more strongly than Cu, and indicates that DFT is not accurately modeling this trend in adsorption between these three surfaces. This study also constitutes the first experimental measurement of the energetics of any adsorbed molecular fragment on any Cu surface. In comparison to previous results on Pt(111) and Ni(111) this allows for the direct comparison of a single molecular fragment on all three surfaces for the first time. This forms a suite of key experimental benchmarks for improving the energy accuracy of computational models like DFT, as well as crucial fundamental insights into trends and observed differences in catalysis on late-transition metal surfaces.Lastly, we report a detailed kinetics study of the aqueous-phase hydrogenation of phenol and benzaldehyde on Pt, Pd, and Rh using small-scale thermal and electrocatalytic reactors. These molecules represent common intermediates in the process of breaking down biomass and converting its constituents into biofuels and other value-added chemicals. This work shows that the observed catalytic behavior is well fit by a Langmuir-Hinshelwood mechanism with competitive adsorption (organic versus hydrogen adsorption) on terrace, or (111)-like, sites. Additionally, we report that adsorbed benzaldehyde inhibits the formation of a bulk Pd-hydride whereas phenol does not, explaining the extreme differences in observed catalytic activity between these two systems. This work informs efforts to correlate molecular structure of biomass intermediates of interest with catalytic activity on late-transition metal catalysts.

Ensiling corn stover with enzymes as a feedstock preservation method for bioconversion

Chen, Qin The Pennsylvania State University 2009 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Energy sustainability and environmental protection are two great challenges that face humanity. Biofuels from lignocellulosic biomass have been recognized as a potential solution for both of these interrelated issues. However, there is a serious bottleneck to economical and efficient ethanol production: the recalcitrance of lignocellulosic biomass due to lignin. This bottleneck has to be solved before cellulosic biofuels can play a significant role in a renewable energy society. To produce biofuels sustainably from lignocellulosic biomass, it will be necessary to store preserve large amounts of feedstock from seasonally harvested fields. Wet storage, and specifically ensilage, could serve as a promising platform for biological pretreatment, since saccharification of the cell wall occurs naturally by organic acids and amended enzymes during the ensilage process. In this study, the impact of the interaction between corn stover harvest seasons and cell wall degrading enzymes was investigated. These investigations included both experimental studies and model simulations of the impacts of feedstock, storage conditions and enzymes, including cellulase and hemicellulase or laccase, on the characteristics of stover silage. The objectives were to obtain a low pH, minimize dry matter losses, and create beneficial biochemical changes in the stover that would facilitate downstream pretreatment and bioconversion. In temperate climates, the corn stover harvest can extend from early fall to early winter, during which time the chemical composition of stover varies significantly and influences the ensilage process. Identifying the optimum harvest period can help maximize utilization of stover as a feedstock for bioethanol. The first investigations explored the effects of harvest date and enzyme addition along with possible interactions on the characteristics of corn stover silage. Corn stover was harvested five times in 2005 and eight times in 2006 throughout early fall and early winter. Samples 500g were subsequently ensiled at 37°C with and without the enzyme treatments at both field moisture and 60% moisture (w.b.). Dry matter loss, pH, water soluble carbohydrate and monosaccharides were analyzed on days 0, 1, 7, and 21. Samples were also subjected to reduced severity dilute acid pretreatment to quantify the conversion to simple sugars. Results demonstrated that harvest date had a significant impact on the quality of stover silage for bioconversion. The moisture content of corn stover, cob and corn were significantly influenced by harvest date, and at later harvest dates, moisture addition was critical for obtaining high quality silage. Results indicated that early fall was the best harvest time in terms of pH, dry matter, water soluble carbohydrate and monosaccharide as well as xylan conversion percentage. With respect to corn stover silage, the addition of enzymes significantly enhanced the positive effects. The presence of lignin in ligninocellulosic biomass constrains and challenges the improvement of bioconversion techniques. A second set of investigations were performed to explore the influence of the lignin-degrading enzyme, laccase, on enzymatic ensiled corn stover. Tetramethylammonium hydroxide (TMAH) thermochemolysis and Gas Chromatography - Mass Spectroscopy (GC-MS) results documented molecular signals of lignin decomposition in laccase-treated stover. Cellulose conversion through enzymatic hydrolysis improved with an increase in the laccase loading rate. This enhanced cellulose digestibility is believed to result from better exposure of cellulose to cellulase through structural changes of lignin, which makes more cellulase available for cellulose hydrolysis. The findings suggest that ensilage might provide a platform for biological pretreatment platform, partially hydrolyzing cellulose and hemicellulose into soluble sugars during the enzymatic ensilage process, and thus facilitating laccase penetration into complex biomass to enhance lignin degradation. These results serve as a first step to understanding the addition of multiple enzyme combinations during ensilage to maximize the utilization of corn stover as a biofuel and biochemical feedstock. The final quality of enzymatically ensiled corn stover was significantly affected by its initial chemical composition, microbial population dynamics, enzyme activities, and thermal and physical conditions of silage fermentations. A comprehensive experimental study to better understand how the interactions of these factors govern silage quality requires a large number of trials and intensive analysis. A predictive enzymatic ensilage model, which was initially developed in a series of papers by Pitt and his colleagues in the late 1980s, was enhanced and then applied to simulate the dynamic behavior of pH, water soluble carbohydrates (WSC), cellulose, and the effects of enzyme additives on the major biochemical and microbial changes during the ensilage process. Estimated final pH, WSC and cellulose concentrations are in agreement with enzymatic silage experimental results. The simulation results also demonstrated that the cellulose loading rate had a significant positive effect on the change of WSC. Results showed enzyme additives in the silage process enhanced the stability of long term storage. The optimal experimental conditions to obtain a high quality enzymatic corn stover silage can be achieved by adjusting the cellulase loading rate and operation temperature. The enhanced model could serve as a guide in designing silage systems (with or without enzyme additives) for large amounts of plant-based biomass. In conclusion, this study demonstrated harvest seasons and cell wall degrading enzymes have strong effects on the characteristics of stover silage, and that ensilage technology was an effective preservation and pretreatment strategy for bioconversion of corn stover biomass. In order to further improve ensilage as a partial substitute for expensive and energy-intensive thermal and chemical pretreatment technologies, future work should focus on using biological strategies to deconstruct the recalcitrant structure of lignocellulosic biomass. If successful, such efforts could thereby improve bioconversion efficiency dramatically. Key words: corn stover, enzyme, laccase, ensilage, harvest date, lignin depolymerization, pretreatment, bioconversion, modeling.

Investigation of iron oxidation kinetics for solar-fuel production via chemical looping

Stehle, Richard Craig University of Florida 2013 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Solar driven production of fuels by means of an intermediate reactive metal for species splitting has provided a practical and efficient pathway for disassociating molecules at significantly lower thermal energies. The fuels of interest are of or derive from the separation of oxygen from H 2O and CO2 to form hydrogen and carbon monoxide. The concept of utilizing thermochemical processes as a means of storing solar energy in the form of an energy carrier demonstrates enormous potential for application in engineering systems. The following study focuses on iron oxidation through water and CO 2 splitting to explore the fundamental reaction kinetics and kinetic rates that are relevant to these processes. Monolith designed laboratory scaled reactors were implemented to investigate reaction temperatures that range from 600 K to 1400 K, In order to properly characterize the reactive metal potential and to optimize a solar reactor scaled-up system. The formation of oxide layers on the iron monoliths is concluded to follow a Cabrera-Mott model for oxidation of metals. In addition, the oxide phase analysis from micro-Raman spectroscopy is consistent with magnetite (Fe3O 4) growth. Kinetic rates where measured using real-time mass spectrometry to calculate kinetic constants and estimate oxide layer thicknesses. Activation energies of 47.3 kJ/mol and 32.8 kJ/mol were found for water-splitting and CO 2 splitting, respectively, which are consistent with the disassociation energies for chemisorbed water and CO2. The oxide layer structures were processed with high resolution SEM and Electron Dispersion Spectroscopy (EDS) for morphology considerations. The result revealed limitations for consistent oxide growth at substantial oxidation thickness ( ≳ 10 microm), attributed to species spallation. This dependency on extent of oxide layer growth suggests short controlled oxidation steps during redox cycling for continued material integrity. The conclusions of the independent oxidation reactions where applied to experimental results for syngas (H 2-CO) production to demonstrate ideal process characteristics. Finally, an analysis of the economic impact of solar derived fuels is presented.

Advances in the Optimization of Energy Systems and Machine Learning Hyperparameters

Tso, William Weikang Texas A&M University ProQuest Dissertations & Thes 2020 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Intensifying public concern about climate change risks has accelerated the push for more tangible action in the transition toward low-carbon or carbon-neutral energy. Concurrently, the energy industry is also undergoing a digital transformation with the explosion in available data and computational power. To address these challenges, systematic decision-making strategies are necessary to analyze the vast array of technology options and information sources while navigating this energy transition. In this work, mathematical optimization is utilized to answer some of the outstanding issues around designing cleaner processes from resources such as natural gas and renewables, operating the logistics of these energy systems, and statistical modeling from data.First, exploiting natural gas to produce lower emission liquid transportation fuels is investigated through an optimization-based process synthesis. This extends previous studies by incorporating chemical looping as an alternative syngas production method for the first time. Second, a similar process synthesis approach is implemented for the optimal design of a novel biomass-based process that coproduces ammonia and methanol, improving their production flexibility and profit margins.Next, operational difficulties with solar and wind energies due to their temporal intermittency and uneven geographical distribution are tackled with a supply chain optimization model and a clustering decomposition algorithm. The former describes power generation through energy carriers (hydrogen-rich chemicals) connecting resource-dense rural areas to resource-deficient urban centers. Results show the potential of energy carriers for long-term storage. The latter is developed to identify the appropriate number of representative time periods for approximating an optimization problem with time series data, instead of using a full time horizon. This algorithm is applied to the simultaneous design and scheduling of a renewable power system with battery storage.Finally, building machine learning models from data is commonly performed through k-fold cross-validation. From recasting this as a bilevel optimization, the exact solution to hyperparameter optimization is obtainable through parametric programming for machine learning models that are LP/QP. This extends previous results in statistics to a broader class of machine learning models.

Theoretical Electrocatalysis for Renewable Fuels and Chemicals

Montoya, Joseph Harold ProQuest Dissertations & Theses Stanford Universit 2015 해외박사(DDOD)

소속기관이 구독 중이 아닌 경우 오후 4시부터 익일 오전 9시까지 원문보기가 가능합니다.

Energy storage is a key concern to the grid-scale use of intermittent renewable sources like solar and wind. Electrolysis of such compounds as CO2, N2, and H2O into higher chemical potential products represents a possible route towards this goal, yet the conversion process is often severely limited due to insufficient catalysis of the associated chemical reactions. In this work, the electrocatalytic conversion of these three molecules is explored using density functional theory (DFT) methods with the goals of both explaining existing trends in experiment and determining criteria for the design of new systems with improved efficiency. CO2 electroreduction into ethylene and ethanol is highly attractive, since higher hydrocarbons are essential to much of our current fuel and chemical economy. In the first section, the formation of C-C bonds in CO2 electroreduction is discussed. The primarily focus of this section is copper, a catalyst known to convert CO2 into C2 products, and scaling relations for the coupling of *CO to its first hydrogenated derivative, *CHO, can rationalize why it is uniquely suited to do so. Insights into the mechanism of CO dimerization in alkaline conditions on Cu 100, as postulated previously from experiment, are also reported. Water-splitting into hydrogen and oxygen is another possible electrochemical energy storage method, and the oxidation of water into gaseous oxygen is typically coupled to other electroreductions discussed herein as well. In the second section, DFT-predicted oxygen evolution activities on perovskite oxides are correlated with the electronic structure of the catalytic surface. In addition, predicted OER and HER overpotentials on photoabsorbing perovskites suggest that materials with optical properties suitable for water splitting likely do not possess the surface chemistry for efficient catalysis, thus motivating the need for co-catalytic systems in photoelectrochemical water splitting. The last section concerns trends in the theoretical overpotentials for the electroreduction of nitrogen gas to ammonia. Nitrogen electroreduction is severely limited in overpotential by the reductive adsorption of N2 to form *N2H on most materials, and may be limited by reductive desorption of NH and NH2 on more reactive materials. By scaling these two reaction energies on a 2-D volcano, we show that no single transition-metal catalyst is likely to produce ammonia efficiently. This scaling relation does provide a strategy, however, for making new catalysts that might be less limited by these steps, since the selective stabilization of *N2H or selective destabilization of *NH2 should then result in less negative overpotential requirements. In summary, this dissertation uses DFT to describe and rationalize trends in electrocatalysis for three key reactions relevant to the conversion of electricity into chemical fuels. It is our hope that the principles outlined herein may guide the design of new catalytic systems that may ultimately realize the goal of efficient storage of renewable energy.