A Study on the Role of Electric Field in Low-Temperature Plasma Catalytic Ammonia Synthesis via Integrated Density Functional Theory and Microkinetic Modeling.

Low-temperature plasma catalysis has shown promise for various chemical processes such as light hydrocarbon conversion, volatile organic compounds removal, and ammonia synthesis. Plasma-catalytic ammonia synthesis has the potential advantages of leveraging renewable energy and distributed manufacturing principles to mitigate the pressing environmental challenges of the energy-intensive Haber-Bosh process, towards sustainable ammonia production. However, lack of foundational understanding of plasma-catalyst interactions poses a key challenge to optimizing plasma-catalytic processes. Recent studies suggest electro- and photoeffects, such as electric field and charge, can play an important role in enhancing surface reactions. These studies mostly rely on using density functional theory (DFT) to investigate surface reactions under these effects. However, integration of DFT with microkinetic modeling in plasma catalysis, which is crucial for establishing a comprehensive understanding of the interplay between the gas-phase chemistry and surface reactions, remains largely unexplored. This paper presents a first-principles framework coupling DFT calculations and microkinetic modeling to investigate the role of electric field on plasma-catalytic ammonia synthesis. The DFT-microkinetic model shows more consistent predictions with experimental observations, as compared to the case wherein the variable effects of plasma process parameters on surface reactions are neglected. In particular, predictions of the DFT-microkinetic model indicate electric field can have a notable effect on surface reactions relative to other process parameters. A global sensitivity analysis is performed to investigate how ammonia synthesis pathways will change in relation to different plasma process parameters. The DFT-microkinetic model is then used in conjunction with active learning to systematically explore the complex parameter space of the plasma-catalytic ammonia synthesis to maximize the amount of produced ammonia while inhibiting reactions dissipating energy, such as the recombination of H2 through gas-phase H radicals and surface-adsorbed H. This paper demonstrates the importance of accounting for the effects of electric field on surface reactions when investigating and optimizing the performance of plasma-catalytic processes.

Ammonia Retention in Biowaste via Low-Temperature-Plasma-Synthesized Nitrogen Oxyacids: Toward Sustainable Upcycling of Animal Waste.

Sustainable fertilizer production is a pressing challenge due to a growing human population. The manufacture of synthetic nitrogen fertilizer involves intensive emissions of greenhouse gases. The synthetic nitrogen that ends up in biowaste such as animal waste perturbs the nitrogen cycle through significant nitrogen losses in the form of ammonia volatilization, a major human health and environmental hazard. Low-temperature air-plasma treatment of animal waste holds promise for sustainable fertilizer production on farmlands by enabling nitrogen fixation via ionization, forming nitrogen oxyacids. Although the formation of nitrogen oxyacids in plasma treatment of water is well-established, the extent of nitrogen oxyanion enrichment in animal waste and its downstream effects on acidifying the waste remain elusive because many compounds found in complex biowaste media may interfere with absorbed NOx species. This work aims to establish that plasma treatment of dairy manure can suppress ammonia loss by volatilization via acidification of animal waste while enriching the waste in total nitrogen due to nitrogen retained in ammonia as well as adding nitrogen oxyacids by reacting NOx with the aqueous slurry. To this end, air-plasma effluent containing NOx is bubbled through dairy manure, which is then analyzed for changes in the nitrogen oxyanion content and pH. Increasing the plasma treatment time results in more acidic manure, reduced ammonium content in the downstream acid trap, and increased nitrogen oxyanion content, where the yield of nitrogen oxyanion from absorbed NOx species is approximately 100%. Increased plasma treatment also led to an increase in the total Kjeldahl nitrogen and the total nitrogen. These results indicate that plasma treatment of animal waste can significantly suppress ammonia pollution from animal husbandry facilities such as dairy farms while upcycling animal waste as a rich organic source of nitrogen.

Data-driven flow-map models for data-efficient discovery of dynamics and fast uncertainty quantification of biological and biochemical systems.

Computational models are increasingly used to investigate and predict the complex dynamics of biological and biochemical systems. Nevertheless, governing equations of a biochemical system may not be (fully) known, which would necessitate learning the system dynamics directly from, often limited and noisy, observed data. On the other hand, when expensive models are available, systematic and efficient quantification of the effects of model uncertainties on quantities of interest can be an arduous task. This paper leverages the notion of flow-map (de)compositions to present a framework that can address both of these challenges via learning data-driven models useful for capturing the dynamical behavior of biochemical systems. Data-driven flow-map models seek to directly learn the integration operators of the governing differential equations in a black-box manner, irrespective of structure of the underlying equations. As such, they can serve as a flexible approach for deriving fast-to-evaluate surrogates for expensive computational models of system dynamics, or, alternatively, for reconstructing the long-term system dynamics via experimental observations. We present a data-efficient approach to data-driven flow-map modeling based on polynomial chaos Kriging. The approach is demonstrated for discovery of the dynamics of various benchmark systems and a coculture bioreactor subject to external forcing, as well as for uncertainty quantification of a microbial electrosynthesis reactor. Such data-driven models and analyses of dynamical systems can be paramount in the design and optimization of bioprocesses and integrated biomanufacturing systems.

A Deep Learning Framework Discovers Compositional Order and Self-Assembly Pathways in Binary Colloidal Mixtures.

Binary colloidal superlattices (BSLs) have demonstrated enormous potential for the design of advanced multifunctional materials that can be synthesized via colloidal self-assembly. However, mechanistic understanding of the three-dimensional self-assembly of BSLs is largely limited due to a lack of tractable strategies for characterizing the many two-component structures that can appear during the self-assembly process. To address this gap, we present a framework for colloidal crystal structure characterization that uses branched graphlet decomposition with deep learning to systematically and quantitatively describe the self-assembly of BSLs at the single-particle level. Branched graphlet decomposition is used to evaluate local structure via high-dimensional neighborhood graphs that quantify both structural order (e.g., body-centered-cubic vs face-centered-cubic) and compositional order (e.g., substitutional defects) of each individual particle. Deep autoencoders are then used to efficiently translate these neighborhood graphs into low-dimensional manifolds from which relationships among neighborhood graphs can be more easily inferred. We demonstrate the framework on in silico systems of DNA-functionalized particles, in which two well-recognized design parameters, particle size ratio and interparticle potential well depth can be adjusted independently. The framework reveals that binary colloidal mixtures with small interparticle size disparities (i.e., A- and B-type particle radius ratios of r A/r B = 0.8 to r A/r B = 0.95) can promote the self-assembly of defect-free BSLs much more effectively than systems of identically sized particles, as nearly defect-free BCC-CsCl, FCC-CuAu, and IrV crystals are observed in the former case. The framework additionally reveals that size-disparate colloidal mixtures can undergo nonclassical nucleation pathways where BSLs evolve from dense amorphous precursors, instead of directly nucleating from dilute solution. These findings illustrate that the presented characterization framework can assist in enhancing mechanistic understanding of the self-assembly of binary colloidal mixtures, which in turn can pave the way for engineering the growth of defect-free BSLs.

There is Only One Valid Definition of Clearance: Critical Examination of Clearance Concepts Reveals the Potential for Errors in Clinical Drug Dosing Decisions.

Drug dosing decisions in clinical medicine and in introducing a drug to market for the past 60 years are based on the pharmacokinetic/clinical pharmacology concept of clearance. We used chemical reaction engineering models to demonstrate the limitations of presently employed clearance measurements based upon systemic blood concentration in reflecting organ clearance. The belief for the last 49 years that in vivo clearance is independent of the mechanistic model for organ clearance is incorrect. There is only one valid definition of clearance. Defining organ clearance solely on the basis of systemic blood concentrations can lead to drug dosing errors when drug effect sites reside either in an eliminating organ exhibiting incremental clearance or in a non-eliminating organ where intraorgan concentration is governed by transporter actions. Attempts to predict clearance are presently hampered by the lack of recognition that what we are trying to predict is a well-stirred model clearance.

Deep learning for characterizing the self-assembly of three-dimensional colloidal systems.

Creating a systematic framework to characterize the structural states of colloidal self-assembly systems is crucial for unraveling the fundamental understanding of these systems' stochastic and non-linear behavior. The most accurate characterization methods create high-dimensional neighborhood graphs that may not provide useful information about structures unless these are well-defined reference crystalline structures. Dimensionality reduction methods are thus required to translate the neighborhood graphs into a low-dimensional space that can be easily interpreted and used to characterize non-reference structures. We investigate a framework for colloidal system state characterization that employs deep learning methods to reduce the dimensionality of neighborhood graphs. The framework next uses agglomerative hierarchical clustering techniques to partition the low-dimensional space and assign physically meaningful classifications to the resulting partitions. We first demonstrate the proposed colloidal self-assembly state characterization framework on a three-dimensional in silico system of 500 multi-flavored colloids that self-assemble under isothermal conditions. We next investigate the generalizability of the characterization framework by applying the framework to several independent self-assembly trajectories, including a three-dimensional in silico system of 2052 colloidal particles that undergo evaporation-induced self-assembly.

Fast uncertainty quantification for dynamic flux balance analysis using non-smooth polynomial chaos expansions.

We present a novel surrogate modeling method that can be used to accelerate the solution of uncertainty quantification (UQ) problems arising in nonlinear and non-smooth models of biological systems. In particular, we focus on dynamic flux balance analysis (DFBA) models that couple intracellular fluxes, found from the solution of a constrained metabolic network model of the cellular metabolism, to the time-varying nature of the extracellular substrate and product concentrations. DFBA models are generally computationally expensive and present unique challenges to UQ, as they entail dynamic simulations with discrete events that correspond to switches in the active set of the solution of the constrained intracellular model. The proposed non-smooth polynomial chaos expansion (nsPCE) method is an extension of traditional PCE that can effectively capture singularities in the DFBA model response due to the occurrence of these discrete events. The key idea in nsPCE is to use a model of the singularity time to partition the parameter space into two elements on which the model response behaves smoothly. Separate PCE models are then fit in both elements using a basis-adaptive sparse regression approach that is known to scale well with respect to the number of uncertain parameters. We demonstrate the effectiveness of nsPCE on a DFBA model of an E. coli monoculture that consists of 1075 reactions and 761 metabolites. We first illustrate how traditional PCE is unable to handle problems of this level of complexity. We demonstrate that over 800-fold savings in computational cost of uncertainty propagation and Bayesian estimation of parameters in the substrate uptake kinetics can be achieved by using the nsPCE surrogates in place of the full DFBA model simulations. We then investigate the scalability of the nsPCE method by utilizing it for global sensitivity analysis and maximum a posteriori estimation in a synthetic metabolic network problem with a larger number of parameters related to both intracellular and extracellular quantities.

Kinetic Study of Acetone-Butanol-Ethanol Fermentation in Continuous Culture.

Acetone-butanol-ethanol (ABE) fermentation by clostridia has shown promise for industrial-scale production of biobutanol. However, the continuous ABE fermentation suffers from low product yield, titer, and productivity. Systems analysis of the continuous ABE fermentation will offer insights into its metabolic pathway as well as into optimal fermentation design and operation. For the ABE fermentation in continuous Clostridium acetobutylicum culture, this paper presents a kinetic model that includes the effects of key metabolic intermediates and enzymes as well as culture pH, product inhibition, and glucose inhibition. The kinetic model is used for elucidating the behavior of the ABE fermentation under the conditions that are most relevant to continuous cultures. To this end, dynamic sensitivity analysis is performed to systematically investigate the effects of culture conditions, reaction kinetics, and enzymes on the dynamics of the ABE production pathway. The analysis provides guidance for future metabolic engineering and fermentation optimization studies.