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Predictive Modeling of Photo-Oxidative Reaction and Diffusion in Glow Discharge Polymer Capsules
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Implementation of a Model Predictive Control Strategy to Regulate Temperature Inside Plug-Flow Solar Reactor With Countercurrent Flow
Abstract Solar-driven thermochemical energy storage systems are proven to be promising energy carriers (solar fuels) to utilize solar energy by using reactive solid-state pellets. However, the production of solar fuel requires a quasi-steady-state process temperature, which represents the main challenge due to the transient nature of solar power. In this work, an adaptive model predictive controller (MPC) is presented to regulate the temperature inside a tubular solar reactor to produce solid-state solar fuel for long-term thermal storage systems. The solar reactor system consists of a vertical tube heated circumferentially over a segment of its length by concentrated solar power, and the reactive pellets (MgMn2O4) are fed from the top end and flow downwards through the heated tube. A countercurrent flowing gas supplied from the lower end interacts with flowing pellets to reduce it thermochemically at a temperature range of 1000—1500 °C. A low-order physical model was developed to simulate the dynamics of the solar reactor including the reaction kinetics, and the proposed model was validated numerically by using a 7-kW electric furnace. The numerical model then was utilized to design the MPC controller, where the control system consists of an MPC code linked to an adaptive system identification code that updates system parameters online to ensure system robustness against external disturbances (sudden change in the flow inside the reactor), model mismatches, and uncertainty. The MPC controller parameters are tuned to enhance the system performance with minimum steady-state error and overshoot. The controller is tested to track different temperature ranges between 500 °C and 1400 °C with different particles/gas mass flowrates and ramping temperature profiles. Results show that the MPC controller successfully regulated the reactor temperature within ± 1 °C of its setpoint and maintained robust performance with minimum input effort when subjected to sudden changes in the amount of flowing media and the presence of chemical reaction.
Chromium Tolerant, Highly Active and Stable Electrocatalytic Internal Surface Coating for Cathode of Commercial SOFCs (Final Report)
This project is aimed to develop a chromium (Cr) tolerant, highly active, and stable coating layer on the internal surfaces of the porous composite cathode from commercially available SOFCs. Such coating layer was developed using the additive manufacturing process of Atomic Layer Deposition (ALD) and has been applied on the cathode consisting of either an electronic conductor of LaxSr 1-x MnyO 3-δ (LSM) or mixed ionic and electronic conducting La x Sr 1-x Co y Fe 1-y O 3-δ (LSCF). PI's work has demonstrated that the internal surface of cathode from the commercial cells, can be further tailored using ALD coating to dramatically enhance the cell performance. For instance, ALD layer consisting heterostructured nano composite of nano-Pt and nano-(Mn 0.8 Co 0.2 ) 3 O 4 oxide on the internal surface of porous LSM/YSZ cathode from SOFCs, has resulted in the large reduction of the cell polarizations resistance by up to 53%, and enormous increase of power density over 370%. For the cells with LSCF/Sm 2 O 3 doped CeO 2 (SDC) cathode, the conformal layer of nano-composite consisting of superjacent CoOx and subjacent minimum amount of Pt nano-grains has resulted in the power density enhancement by 126% for the large scale industry tubular cells at 750°C, and both the performance enhancement and nanostructure of the ALD layer are stable over ~ 2000 h continuous operation performed at industry test station. In the meanwhile, those ALD coating layer developed by PI's work is also inherently Cr-tolerant, and could act as physical barrier for preventing Cr diffusion into the cathode backbone, so as to mitigate the Cr poisoning effect on the cathode. In this project, the impact of Cr on the performance of those ALD coated commercial cells has been evaluated. Based on evolution of the cell performance, the ALD coating layer chemistry and ALD coating layer thickness has been optimized to maximize the overall Cr tolerance, cell power density and cell longevity. Different ALD coating has been applied onto the internal surface of LSM/YSZ and LSCF/SDC backbone respectively. The architecture/scaffold structures on the internal surface of different cathode, designed by this project, was catalogued and analyzed using High Resolution Transmission Electron Microscopy (HRTEM), and cell power/durability performance are assured via comprehensive electrochemical performance testing in the industry operation relevant conditions. The impact of the electrochemical operation current density, the water humidity, the cell operation temperature, and cell operation duration on the Cr tolerance of ALD coated cells has been systematically investigated. There is completely different nanostructure degradation mechanisms between LSM and LSCF cells induced by Cr contamination. For the LSCF/SDC baseline cell, With the Cr source, there is no apparent Sr surface segregation phase even for the baseline cell operated for 3000 h at 750 °C. With the Cr source, there is significant amorphous (SrCr)Ox phase accumulated in the original pore region. For the commercial baseline cells, Cr contaminants on the LSM electrode severely impacted the entire cell's electrochemical performance and nanostructure degradation. Those degradations include (1). Peak power density loss of 64 % after 109 h of operation. The dramatic increase in Rp (2). They are cracking at LSM/SSZ interface, LSM grains. SSZ remains intact but with (CrMn)Ox. By contrast, ALD coating (MnCo)Ox/Pt dramatically improves the Cr resistance, as follows (1). ALD-coated cell with a power density is 280-380 % of the baseline cell, depending on the ALD layer thickness. (2). For a cell with a 20 nm thick ALD layer, there is a large performance enhancement (> 200 % power density) induced by ALD coating of Cr-tolerant Mn 0.8 Co 0.2 Ox. (3). For a cell with a 20 nm thick ALD layer, after 168 h at 750 °C power density of the ALD-coated cell is ~ 600% of that baseline cell upon operation with Cr contamination for 109 h. The ALD coating on the internal surface of cathode developed by this project integrated multi-functions. Those multi-functions include (1). Dramatically improving the cell power density for the commercial cells; (2). Dramatically improving contamination resistance of the cathode, for being an excellent protection coating layer sealing off Cr contamination. (3). Dramatically increasing the cell longevity by potentially preventing the microstructure evolution and grain coarsening of the cathode. Overall, this project will provide a simple solution to simultaneously enhance power density and increase the reliability, robustness, and endurance of commercial SOFCs, over the entire operating temperature range of 650-800 °C. For the inherently functional SOFC, the ALD coating of LSM based cathode mitigate the Cr-contamination. Power density of ALD-coated cell is ~ 600% of that baseline cell upon operation with Cr contamination. In addition to SOFCs, the novel on-demand design approach and creation of multifunctional heterogeneous architecture on the electrode surface presented in this work opens further research for their application in other types of fuel cells, batteries, and sensors for which electrochemical reactions on the surface are similarly critical.
Bayesian learning of orthogonal embeddings for multi-fidelity Gaussian Processes
Uncertainty propagation in complex engineering systems often poses significant computational challenges related to modeling and quantifying probability distributions of model outputs, as those emerge as the result of various sources of uncertainty that are inherent in the system under investigation. Gaussian Processes regression (GPs) is a robust meta-modeling technique that allows for fast model prediction and exploration of response surfaces. Multi-fidelity variations of GPs further leverage information from cheap and low fidelity model simulations in order to improve their predictive performance on the high fidelity model. In order to cope with the high volume of data required to train GPs in high dimensional design spaces, a common practice is to introduce latent design variables that are typically projections of the original input space to a lower dimensional subspace, and therefore substitute the problem of learning the initial high dimensional mapping, with that of training a GP on a low dimensional space. Here in this paper, we present a Bayesian approach to identify optimal transformations that map the input points to low dimensional latent variables. The \projection" mapping consists of an orthonormal matrix that is considered a priori unknown and needs to be inferred jointly with the GP parameters, conditioned on the available training data. The proposed Bayesian inference scheme relies on a two-step iterative algorithm that samples from the marginal posteriors of the GP parameters and the projection matrix respectively, both using Markov Chain Monte Carlo (MCMC) sampling. In order to take into account the orthogonality constraints imposed on the orthonormal projection matrix, a Geodesic Monte Carlo sampling algorithm is employed, that is suitable for exploiting probability measures on manifolds. We extend the proposed framework to multi-fidelity models using GPs including the scenarios of training multiple outputs together. We validate our framework on three synthetic problems with a known lower-dimensional subspace. The benefits of our proposed framework, are illustrated on the computationally challenging aerodynamic optimization of a last-stage blade for an industrial gas turbine, where we study the effect of an 85-dimensional shape parameterization of a three-dimensional airfoil on two output quantities of interest, specifically on the aerodynamic efficiency and the degree of reaction
Three-dimensional Zn-based alloys for dendrite-free aqueous Zn battery in dual-cation electrolytes
Aqueous zinc-ion batteries, in terms of integration with high safety, environmental benignity, and low cost, have attracted much attention for powering electronic devices and storage systems. However, the interface instability issues at the Zn anode caused by detrimental side reactions such as dendrite growth, hydrogen evolution, and metal corrosion at the solid (anode)/liquid (electrolyte) interface impede their practical applications in the fields requiring long-term performance persistence. Despite the rapid progress in suppressing the side reactions at the materials interface, the mechanism of ion storage and dendrite formation in practical aqueous zinc-ion batteries with dual-cation aqueous electrolytes is still unclear. Herein, we design an interface material consisting of forest-like three-dimensional zinc-copper alloy with engineered surfaces to explore the Zn plating/stripping mode in dual-cation electrolytes. The three-dimensional nanostructured surface of zinc-copper alloy is demonstrated to be in favor of effectively regulating the reaction kinetics of Zn plating/stripping processes. The developed interface materials suppress the dendrite growth on the anode surface towards high-performance persistent aqueous zinc-ion batteries in the aqueous electrolytes containing single and dual cations. This work remarkably enhances the fundamental understanding of dual-cation intercalation chemistry in aqueous electrochemical systems and provides a guide for exploring high-performance aqueous zinc-ion batteries and beyond.
Catalytic Enhancement of Inductively Heated Fe 3 O 4 Nanoparticles by Removal of Surface Ligands
Abstract Heat management in catalysis is limited by each material's heat transfer efficiencies, resulting in energy losses despite current thermal engineering strategies. In contrast, induction heating of magnetic nanoparticles (NPs) generates heat at the surface of the catalyst where the reaction occurs, reducing waste heat via dissipation. However, the synthesis of magnetic NPs with optimal heat generation requires interfacial ligands, such as oleic acid, which act as heat sinks. Surface treatments using tetramethylammonium hydroxide (TMAOH) or pyridine are used to remove these ligands before applications in hydrophilic media. In this study, Fe 3 O 4 NPs are surface treated to study the effect of induction heating on the catalytic oxidation of 1‐octanol. Whereas TMAOH was unsuccessful in removing oleic acid, pyridine treatment resulted in a roughly 2.5‐fold increase in heat generation and product yield. Therefore, efficient surfactant removal has profound implications in induction heating catalysis by increasing the heat transfer and available surface sites.
Light‐driven Transformation of Carbon Monoxide into Hydrocarbons using CdS@ZnS : VFe Protein Biohybrids
Enzymatic Fisher-Tropsch (FT) process catalyzed by vanadium (V)-nitrogenase can convert carbon monoxide (CO) to longer-chain hydrocarbons (>C2) under ambient conditions, although this process requires high-cost reducing agent(s) and/or the ATP-dependent reductase as electron and energy sources. Using visible light-activated CdS@ZnS (CZS) core-shell quantum dots (QDs) as alternative reducing equivalent for the catalytic component (VFe protein) of V-nitrogenase, we first report a CZS : VFe biohybrid system that enables effective photo-enzymatic C−C coupling reactions, hydrogenating CO into hydrocarbon fuels (up to C4) that can be hardly achieved with conventional inorganic photocatalysts. Surface ligand engineering optimizes molecular and opto-electronic coupling between QDs and the VFe protein, realizing high efficiency (internal quantum yield >56 %), ATP-independent, photon-to-fuel production, achieving an electron turnover number of >900, that is 72 % compared to the natural ATP-coupled transformation of CO into hydrocarbons by V-nitrogenase. The selectivity of products can be controlled by irradiation conditions, with higher photon flux favoring (longer-chain) hydrocarbon generation. The CZS : VFe biohybrids not only can find applications in industrial CO removal for high-value-added chemical production by using the cheap, renewable solar energy, but also will inspire related research interests in understanding the molecular and electronic processes in photo-biocatalytic systems.
Multi-omics profiling of the cold tolerant Monoraphidium minutum 26B-AM in response to abiotic stress
Microalgae that are of interest for biofuel production must be able to tolerate environmental changes that occur in outdoor cultivation systems. While algal cultures may experience daily temperature fluctuations and seasonal environmental changes, the underlying mechanisms that control and regulate physiological responses and adaptation to environmental pressures are largely unknown. Systems-level characterization enabled by functional genomics can help identify biochemical pathways that promote stability and productivity of algae in various environmental conditions. Monoraphidium minutum 26B-AM, a freshwater green microalga, was identified as a top performer in biomass production in winter season screens. We sequenced the genome of M. minutum 26B-AM and applied our multi-omics pipeline to profile this high potential strain under high salt and cold temperature perturbations. Through comparative analysis, including other green algae in the class Chlorophyceae, we identified gene families unique to the genus Monoraphidium, including a desaturase that has been linked to cold tolerance in plants. We observed that osmolytes, such as trehalose, proline and betaine, accumulate under salt stress, coinciding with upregulation of genes involved in biosynthesis of these metabolites. From the genome annotation, we reconstructed a metabolic model to provide a detailed map of the metabolic pathways and can be used to simulate growth and reaction fluxes. This multi-omics analysis provides a foundation to explore algal strain potential for biofuel applications, guides strain engineering, and expands our understanding of metabolic and regulatory mechanisms of algae in applied systems.
Adapting isotopic tracer and metabolic flux analysis approaches to study C1 metabolism
Single-carbon (C1, or one-carbon) substrates are promising feedstocks for sustainable biofuel and biochemical production. Crucial to the goal of engineering C1-utilizing strains for improved production is a quantitative understanding of the organization, regulation and rates of the reactions that underpin C1 metabolism. 13 C Metabolic flux analysis (MFA) is a well-established platform for interrogating these questions with multi-carbon substrates, and uses the differential labeling of metabolites that results from feeding a substrate with position-specific incorporation of 13 C in order to infer quantitative fluxes and pathway topology. Furthermore, adapting isotopic tracer approaches to C1 metabolism, where position-specific substrate labeling is impossible, requires additional experimental considerations. Here we review recent studies that have developed isotopic tracer approaches to overcome the challenge of uniform metabolite labeling and provide quantitative insight into C1 metabolism.
Metabolic flux and resource balance in the oleaginous yeast Rhodotorula toruloides
The yeast Rhodotorula toruloides is a promising bioproduction organism due to its high lipid yields and ability to grow on cheap and abundant substrates. Quantitative, systems-level assessment of its metabolic activity is accordingly merited. Resource-balance analysis (RBA) models capture not only reaction stoichiometry but also enzyme requirements for catalysis, providing valuable tools for understanding metabolic trade-offs and optimizing metabolic engineering strategies. Here, in this work, we present systems-level measurements of R. toruloides metabolic flux based on isotope tracing and metabolic flux analysis. In combination with new proteomic measurements, these flux data are used to parameterize a genome-scale resource balance model rtRBA. We find that S. cerevisiae and R. toruloides grow at nearly indistinguishable rates using similar biosynthetic but dramatically different central metabolic programs. R. toruloides consumes one-fifth as much glucose, which it metabolizes primarily via the pentose phosphate pathway and TCA cycle unlike primarily glycolysis in S. cerevisiae . Overall, across these two divergent yeasts, protein abundances aligned more closely than metabolic flux. Resource balance modeling of these metabolic programs predicts superior theoretical yields but lower productivities in R. toruloides than S. cerevisiae for industrial chemicals, highlighting the value of rapid glucose uptake for productivity but respiratory metabolism for yields.
Modulation of recombination zone position for white perovskite/organic emitter hybrid light-emitting devices
We report metal halide perovskites present specific challenges as emitters in large area, surface emission lighting devices. Among these challenges is the vast difference in carrier mobilities between the perovskite and many organic buffer layers typically used in such device fabrication as transport and blocking layers. This can make it difficult to engineer recombination to achieve white emitting devices generally. However, in this work, we introduce unique modulation of excitonic confinement within the perovskite layer of the device stack to control overall placement of the recombination zone. This results in a white light emitter that is bright and highly tunable, providing a path to realize white perovskite related light-emitting devices.
Attempts to develop an enzyme converting DHIV to KIV
Abstract Dihydroxy-acid dehydratase (DHAD) catalyzes the dehydration of R-2,3-dihydroxyisovalerate (DHIV) to 2-ketoisovalerate (KIV) using an Fe-S cluster as a cofactor, which is sensitive to oxidation and expensive to synthesize. In contrast, sugar acid dehydratases catalyze the same chemical reactions using a magnesium ion. Here, we attempted to substitute the high-cost DHAD with a cost-efficient engineered sugar acid dehydratase using computational protein design (CPD). First, we tried without success to modify the binding pocket of a sugar acid dehydratase to accommodate the smaller, more hydrophobic DHIV. Then, we used a chemically activated substrate analog to react with sugar acid dehydratases or other enolase superfamily enzymes. Mandelate racemase from Pseudomonas putida (PpManR) and the putative sugar acid dehydratase from Salmonella typhimurium (StPutD) showed beta-elimination activity towards chlorolactate (CLD). CPD combined with medium-throughput selection improved the PpManR kcat/KM for CLD by four-fold. However, these enzyme variants did not show dehydration activity towards DHIV. Lastly, assuming phosphorylation could also be a good activation mechanism, we found that mevalonate-3-kinase (M3K) from Picrophilus torridus (PtM3K) exhibited adenosine triphosphate (ATP) hydrolysis activity when mixed with DHIV, indicating phosphorylation activity towards DHIV. Engineering PpManR or StPutD to accept 3-phospho-DHIV as a substrate was performed, but no variants with the desired activity were obtained.
Structural analysis of wild-type and Val120Thr mutant Candida boidinii formate dehydrogenase by X-ray crystallography
Candida boidinii NAD + -dependent formate dehydrogenase (CbFDH) has gained significant attention for its potential application in the production of biofuels and various industrial chemicals from inorganic carbon dioxide. The present study reports the atomic X-ray crystal structures of wild-type CbFDH at cryogenic and ambient temperatures, as well as that of the Val120Thr mutant at cryogenic temperature, determined at the Turkish Light Source ‘Turkish DeLight’. The structures reveal new hydrogen bonds between Thr120 and water molecules in the active site of the mutant CbFDH, suggesting increased stability of the active site and more efficient electron transfer during the reaction. Further experimental data is needed to test these hypotheses. Collectively, these findings provide invaluable insights into future protein-engineering efforts that could potentially enhance the efficiency and effectiveness of CbFDH.
Convergence Criteria for Multiphysics Simulations
The behavior of engineered systems is often influenced by multiple physical phenomena, such as mechanical deformation, heat transfer, and chemical species transport and reactions. There are often strong interactions between these phenomena, and there is increasing interest in applying coupled-physics models to improve understanding of physical behavior under complex environmental conditions. Multiple simulation frameworks that facilitate coupled-physics simulations are in widespread use, and these employ a variety of techniques to account for interactions between those physics. Many frameworks solve the physics models independently and transfer results between them. Alternatively, a single monolithic system of equations for every physics model can be formed and solved. Each of these approaches has its benefits and drawbacks, and the optimal approach varies depending on the nature of the problem. The open-source MOOSE framework was developed targeting solution of large-scale multiphysics problems. Although it provides options for all these coupling approaches, its standard approach for multiphysics solutions is to form and solve a single monolithic system of equations containing the unknowns for all physics models. MOOSE provides a streamlined approach for users to define the solution variables, the terms in the partial differential equations pertaining to each variable, and interactions between solution variables. One aspect of the monolithic solution approach that can be problematic, however, is defining appropriate convergence criteria for the nonlinear system. A standard approach is to determine convergence is to simply take a norm of the residual vector corresponding to the full vector of unknowns. However, if the residual vector contains variables for multiple physics models, the magnitudes of those variables can differ significantly, and the variables can converge at significantly different rates from each other. It is important to ensure that the variables for each of the physics are converged, and also ensure that the convergence criteria are not excessively stringent in cases when there is little change in the solution. This talk presents representative multiphysics problems to highlight these issues, and shows strategies for convergence criteria in MOOSE that are robust for multiphysics models under a variety of conditions.
Interface synergism and engineering of Pd/Co@N-C for direct ethanol fuel cells
Direct ethanol fuel cells have been widely investigated as nontoxic and low-corrosive energy conversion devices with high energy and power densities. It is still challenging to develop high-activity and durable catalysts for a complete ethanol oxidation reaction on the anode and accelerated oxygen reduction reaction on the cathode. The materials’ physics and chemistry at the catalytic interface play a vital role in determining the overall performance of the catalysts. Herein, we propose a Pd/Co@N-C catalyst that can be used as a model system to study the synergism and engineering at the solid-solid interface. Particularly, the transformation of amorphous carbon to highly graphitic carbon promoted by cobalt nanoparticles helps achieve the spatial confinement effect, which prevents structural degradation of the catalysts. The strong catalyst-support and electronic effects at the interface between palladium and Co@N-C endow the electron-deficient state of palladium, which enhances the electron transfer and improved activity/durability. The Pd/Co@N-C delivers a maximum power density of 438 mW cm -2 in direct ethanol fuel cells and can be operated stably for more than 1000 hours. This work presents a strategy for the ingenious catalyst structural design that will promote the development of fuel cells and other sustainable energy-related technologies.
Molecular Engineering of Emissive Molecular Qubits Based on Spin-Correlated Radical Pairs
Spin chemistry of photogenerated spin-correlated radical pairs (SCRPs) offers a practical approach to control chemical reactions and molecular emissions by using weak magnetic fields. This capability to harness magnetic field effects (MFEs) paves the way for developing SCRPs-based molecular qubits. Here, in this work, we introduce a new series of donor–chiral bridge–acceptor (D–χ–A) molecules that demonstrate significant MFEs on fluorescence intensity and lifetime in solution at room temperature—critical for quantum sensing. By precisely tuning the donor site through torsional locking, distance extension, and planarization, we achieved remarkable control over key quantum properties, including field-response range and line width. In the most responsive systems, emission lifetimes increased by over 200%, and the total emission intensity was modulated by up to 30%. This level of tunability shows the power of synthetic spin chemistry. The rational design principle of optically addressable SCRP-based molecular systems, presented in this work, represents a major leap toward functional synthetic molecular qubits, advancing the field of molecular quantum technologies.
Controlled Growth and Chemical Engineering of FeSe‐Based Superconducting Films
Abstract Chemical engineering tailors functional materials to meet the demands of physical and chemical properties, accelerating materials discovery and design in a multidisciplinary field. However, stereotyped synthetic paradigms in physical and chemical approaches obstruct the effective integration of multiple advantages of respective materials exploration methods. Solid state reaction is often considered separately with wet chemistry synthesis route, such as film growth using physical vapor deposition and hydrothermal methods. Here, the controlled growth of FeSe thin films by physical deposition techniques followed by the chemical engineering to create heterogenous interface in FeSe films via a solution‐based environment is demonstrated. The two‐step route allows an expanded crystal structure and enhanced superconducting transition temperature from 3.6 to 38 K, providing a new strategy to design functional hybrid materials.