Dataset and kinetic model reaction compilation for the radical-induced degradation of formic acid and formate in aqueous solution
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SEARCH · Engineering Papers
Results for “analytical solution”
Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.
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Potentials of mean force fail to describe chemical bond-breaking in solution
Many liquid phase studies assume that the potential energy surfaces of reacting molecules are the same as in the gas phase, neglecting complex solvent dynamics that can completely alter the nature of chemical reactivity. Even studies that include solvent effects typically only consider them in an average, equilibrium way as part of a potential of mean force (PMF). In this work, we use mixed quantum/classical simulations to compare how equilibrium and non-equilibrium solvent motions affect the photodissociation of a simple diatomic molecule, NaK + , in liquid tetrahydrofuran. A PMF analysis shows that as the excited-state molecule dissociates with the solvent at equilibrium, the bonding electron remains associated with K + at short bond distances but eventually localizes on Na + at the end of dissociation. When we examine non-equilibrium dynamical photodissociation trajectories, however, we find that they fall into three distinct categories: about a quarter of them have the bonding electron mainly associated with Na + , another quarter stay mainly associated with K + , and about half have the bonding electron shared roughly equally between the two ions. The results show that equilibrium PMFs cannot accurately describe the dynamics of bond-breaking chemical reactions in solution because there is insufficient time for the solvent to reach equilibrium on the time scale over which bond dissociation occurs. Furthermore, our analysis shows that the solvent coupling between the electronic energy surfaces is similar at and away from equilibrium, suggesting that other factors, such as solute velocity-driven solvent memory effects, play a more important role in explaining the failure of the equilibrium PMF to predict the non-equilibrium dynamics.
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Precise Modeling of a Complex Solenoidal Magnetic Field Using a Combination of Analytic Functions and a PINN
We demonstrate an iterative approach to modeling a sparsely measured magnetic field in a large-bore solenoid. This approach uses a hybrid of traditional and machine learning techniques. The traditional technique is a linear least-squares fit using a series solution to Laplace's equation, while the machine learning technique involves the training of a physics-informed neural network (PINN) on the least-squares fit residuals. We use a newly defined activation function "DELTAsnake," a modification to the snake activation function proposed by Ziyin et al. that allows for stronger curvature and non-monotonicity. The combined model approximately obeys Maxwell's equations to a level sufficient for producing high quality physics simulations and analysis. Our approach is applied to a highly realistic calculation of the expected magnetic field in the Mu2e experiment's Detector Solenoid which includes a simple model for the expected statistical measurement uncertainties. Using ten toy measurement simulations, we demonstrate the capabilities of our model in comparison to the least-squares method alone; the least-squares method alone results in a reduced chi-squared statistic of ${2.15 \pm 0.01}$, while our approach improves the reduced chi-square to ${1.034 \pm 0.005}$. Furthermore, for an average toy simulation, we show that the range of the RMS of the three field component residuals reduces from ${0.07-0.37}$ Gauss to ${0.05-0.07}$ Gauss. We find that this novel method is robust against a realistic systematic uncertainty deriving from Hall probe calibration bias and can be used to significantly reduce the number of measurements required to achieve an accurate model.
Kampa, Cole [Caltech] (ORCID:0000000192972920)↗
Non‐Linear Optical Properties of the ( RE ) 3 CuGeS 7 Family of Compounds
Abstract Non‐linear optical materials must possess a balanced combination of laser‐induced damage threshold (LDT) and second‐harmonic generation (SHG) and be phase matchable. In our previous work, chiral and polar La 3 CuGeS 7 was identified as a promising non‐linear optical material. Herein, we report the optimization of non‐linear optical properties through replacement of La with smaller lanthanides. It is determined that Gd 3 CuGeS 7 exhibits the best combination of SHG (1.6× AgGaS 2 at 88–105 μm particle size) and LDT (3× AgGaS 2 , 89 MW/cm 2 ) and is phase matchable. Based on changes in metal‐sulfur bond lengths and angles, we further propose structural optimization through solid‐solution formation and doping.
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Knowledge-matching based computational framework for genome-scale metabolic model refinement
Genome-scale metabolic models (GEMs) are mathematically structured knowledge base reconstructed from annotated genome of different organisms. With the advancement of next-generation sequencing technology, many organisms have had their genomes sequenced. However, obtaining a high-quality GEM is highly time-consuming, even with the introduction of several genome-scale reconstruction tools that offer automated draft network generation and gap filling. It has been recognized that the iterative process of manual curation and refinement is the limiting step of GEM development, and how to expedite the GEM refinement is still an open question. As cellular metabolism is a complex system with very high degree of freedom and redundancy, the principles and techniques developed in process systems engineering can be adapted to expedite GEM refinement. In this paper we present a knowledge-matching based computation framework for GEM refinement, and demonstrate the effectiveness of the proposed solution using the refinement of a GEM for Clostridium tyrobutyricum.
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Realizing the data-driven, computational discovery of metal-organic framework catalysts
Metal-organic frameworks (MOFs) have been widely investigated for challenging catalytic transformations due to their well-defined structures and high degree of synthetic tunability. These features, at least in principle, make MOFs ideally suited for a computational approach towards catalyst design and discovery. Nonetheless, the widespread use of data science and machine learning to accelerate the discovery of MOF catalysts has yet to be substantially realized. In this review, we provide an overview of recent work that sets the stage for future high-throughput computational screening and machine learning studies involving MOF catalysts. This is followed by a discussion of several challenges currently facing the broad adoption of data-centric approaches in MOF computational catalysis, and we share possible solutions that can help propel the field forward.
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An asymptotic-preserving 2D-2P relativistic Drift-Kinetic-Equation solver for runaway electron simulations in axisymmetric tokamaks
We propose an asymptotic-preserving (AP), uniformly convergent numerical scheme for the relativistic collisional Drift-Kinetic Equation (rDKE) to simulate runaway electrons in axisymmetric toroidal magnetic field geometries typical of tokamak devices. The approach is derived from an exact Green's function solution with numerical approximations of quantifiable impact, and results in a simple, two-step operator-split algorithm, consisting of a collisional Eulerian step, and a Lagrangian orbit-integration step with analytically prescribed kernels. The AP character of the approach is demonstrated by analysis of the dominant numerical errors, as well as by numerical experiments. We demonstrate the ability of the algorithm to provide accurate answers regardless of plasma collisionality on a circular axisymmetric tokamak geometry.
97 MATHEMATICS AND COMPUTING↗
Poly(cyclosilane) Connectivity Tunes Optical Absorbance
We report herein the influence of skeletal connectivity on the conformation-dependent optical properties of cyclosilane homo- and copolymers. 1,3-Linked cyclosilanes were bathochromically shifted by 20 nm in solution relative to 1,4-linked cyclosilanes, an effect reproduced by quantum chemical calculations on oligomeric model systems. Polysilane optical properties are conformation-dependent, and 1,3-linked cyclosilanes were hypothesized to adopt a favorable conformation unavailable to 1,4-linked cyclosilanes constrained to an endocyclic gauche conformation. Copolymerization of the isomeric cyclosilanes 1,3Si 6 and 1,4Si 6 afforded linear statistical copolymers, as characterized by 1 H and 29 Si NMR spectroscopies. The distinct connectivity of each comonomer was found to give rise to tunable absorption spectra, where the position of the absorption band systematically increased with the increased corporation of 1,3Si 6 . Computational studies pointed to conformation-dependent changes in orbital symmetry in shifting the most intense transition from the low-energy highest occupied molecular orbital (HOMO) → lowest unoccupied molecular orbital (LUMO) transition to a higher-energy HOMO → LUMO + n transition. Here, the results of these studies demonstrate for the first time the role of silicon skeletal connectivity in controlling conformation and optoelectronic properties and provide new insight into the structure-based design of solution-processable silicon-based polymeric materials.
36 MATERIALS SCIENCE↗
Reactive transport modeling of organic carbon degradation in marine methane hydrate systems
Abstract Natural methane hydrate has often been observed in sand layers that contain no particulate organic carbon (POC), but are surrounded by organic-rich, fine-grained marine muds. In this paper, we develop a reactive transport model (RTM) of a microbially-mediated set of POC degradation reactions, including hydrolysis of POC driven by extracellular enzymes, fermentation of the resulting high-molecular weight dissolved organic carbon (HMW-DOC), and methanogenesis that consumes low-molecular weight dissolved organic carbon (LMW-DOC). These processes are mediated by two groups of microbes, fermenters and methanogens that are heterogeneously distributed in different lithologies, with the largest numbers of microbes in the large pores of coarse-grained layers. We find that the RTM can reproduce methane hydrate occurrences observed in two different geological environments, at Walker Ridge Site 313-H (Gulf of Mexico) and IODP Site U1325 (Cascadia Margin). We also find that microbes can degrade POC even if they are physically separated, as extracellular enzymes and DOC can diffuse away from where they are produced by microbes. Microbial activity is highest at relatively early times after burial at shallow depths and near lithological boundaries, where concentration gradients transport solutes to intervals that contain the most microbes.
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Quantitative U=O bond activation in uranyl complexes via silyl radical transfer
Reductive silylation of the uranyl dication with 1,4-bis(trimethylsilyl)dihydropyrazine, or “Mashima's reagent”, is detailed. The reagent simultaneously delivers silylium ions and electrons to multiple uranyl complexes, i.e. a pyridine dipyrrolide uranyl complex and a common uranyl-containing starting material, UO 2 Cl 2 (OPPh 3 ) 2 . This reaction results in quantitative activation of thermodynamically robust U=O bonds. Furthermore, the reductive functionalization of environmentally persistent actinyl species with Mashima's reagent is seen as a promising solution for nuclear waste remediation.
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Iterative subspace algorithms for finite-temperature solution of Dyson equation
One-particle Green’s functions obtained from the self-consistent solution of the Dyson equation can be employed in the evaluation of spectroscopic and thermodynamic properties for both molecules and solids. However, typical acceleration techniques used in the traditional quantum chemistry self-consistent algorithms cannot be easily deployed for the Green’s function methods because of a non-convex grand potential functional and a non-idempotent density matrix. Moreover, the optimization problem can become more challenging due to the inclusion of correlation effects, changing chemical potential, and fluctuations of the number of particles. In this paper, we study acceleration techniques to target the self-consistent solution of the Dyson equation directly. We use the direct inversion in the iterative subspace (DIIS), the least-squared commutator in the iterative subspace (LCIIS), and the Krylov space accelerated inexact Newton method (KAIN). We observe that the definition of the residual has a significant impact on the convergence of the iterative procedure. Based on the Dyson equation, we generalize the concept of the commutator residual used in DIIS and LCIIS and compare it with the difference residual used in DIIS and KAIN. The commutator residuals outperform the difference residuals for all considered molecular and solid systems within both GW and GF2. For a number of bond-breaking problems, we found that an easily obtained high-temperature solution with effectively suppressed correlations is a very effective starting point for reaching convergence of the problematic low-temperature solutions through a sequential reduction of temperature during calculations.
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RuralAI in Tomato Farming: Integrated Sensor System, Distributed Computing, and Hierarchical Federated Learning for Crop Health Monitoring
Precision horticulture is evolving due to scalable sensor deployment and machine learning (ML) integration. These advancements boost the operational efficiency of individual farms, balancing the benefits of analytics with autonomy requirements. However, given concerns that affect wide geographic regions (e.g., climate change), there is a need to apply models that span farms. Federated learning (FL) has emerged as a potential solution. FL enables decentralized ML across different farms without sharing private data. Traditional FL assumes simple two-tier network topologies and, thus, falls short of operating on more complex networks found in real-world agricultural scenarios. Networks vary across crops and farms and encompass various sensor data modes, extending across jurisdictions. New hierarchical FL (HFL) approaches are needed for more efficient and context-sensitive model sharing, accommodating regulations across multiple jurisdictions. Here, we present the RuralAI architecture deployment for tomato crop monitoring, featuring sensor field units for soil, crop, and weather data collection. HFL with personalization is used to offer localized and adaptive insights. Model management, aggregation, and transfers are facilitated via a flexible approach, enabling seamless communication between local devices, edge nodes, and the cloud.
60 APPLIED LIFE SCIENCES↗
Global Pathway Selection with Zero-RK
Global Pathway Selection (GPS) is an algorithm to effectively generates reduced (skeletal) chemistry mechanisms, which speeds up simulations and can be used as a systematic analytics tool to extract insights from complex reacting system. This release is an extension of the original code to run in parallel and to use LLNL's Zero-RK solver for fast solution of chemical problems.
Whitesides, RussellA↗
Global Pathway Selection with Zero-RK v0.5
Global Pathway Selection (GPS) is an algorithm to effectively generates reduced (skeletal) chemistry mechanisms, which speeds up simulations and can be used as a systematic analytics tool to extract insights from complex reacting system. This release is an extension of the original code to run in parallel and to use LLNL's Zero-RK solver for fast solution of chemical problems.
Whitesides, RussellA [Lawrence Livermore National ↗
Microbial potentiometric sensor technology for real-time detecting and monitoring of toxic metals in aquatic matrices
Considering that toxic metals can affect metabolic processes in microorganisms adversely, it can be hypothesized that these metals in water matrices would induce a decrease in metabolic activity of the biofilm microorganisms populating the surface of a sensing electrode, which could be registered as a change in the open-circuit potential (OCP) generated by the biofilm microorganisms. The goal of this study was to test this hypothesis and demonstrate the underlying principle that microbial potentiometric sensor (MPS) technology could be used for long-term and real-time monitoring and detection of rapid changes in metal concentrations in realistic aquatic environments. To address the goal, four objective were addressed: (1) a batch reactor with three graphite-based MPS electrodes was fabricated; (2) a set of single-ion solutions and one multiple ion solution were prepared reflecting realistic concentrations of metals found in electroplating wastewaters; (3) the responses of the MPS to the simultaneous presence of multiple toxic metal ions in a single solution were measured; and (4) the changes of the MPS signals to the presence of individual metal ion solutions were examined. While the hypothesis was validated, the study also revealed that the MPS was sufficiently sensitive to not only detect, but also quantify, toxic metal ion concentrations in aqueous solutions. The coefficients of determination, which were R 2 > 0.995, and responsiveness of <1 μmol/L for some toxic metal cations, strongly support the performance of MPS technology in the echelons of expensive analytical tools capable detecting and measuring trace elements.The magnitude of the MPS response was toxic metal specific. When the molar concertation normalizes the inhibition portion of the signal area, the assessed sensitivity order was: Se>Cd>Pb>Ag>Ni> Zn. The study provides valuable information for enforcement agents, environmental professionals, and wastewater treatment operators, so toxic metal pollution and its detrimental impacts can be prevented and mitigated.
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Compositions and methods for the dissolution and depolymerization of lignin
The present invention provides for a method for dissolving and/or depolymerizing lignin comprising: (a) providing a composition comprising lignin, (b) contacting the composition with a strong hydrogen donor, such as a polyol, to form a first solution, (c) incubating the first solution at a temperature equal to or less than 100° C., whereby at least 20% by weight of the lignin is dissolved, (d) optionally introducing an oxidation agent to the first solution to form a second solution, wherein the temperature of the second solution is equal to or less than 100° C., whereby lignin is depolymerized, and (e) optionally introducing an anti-solvent to the second solution to precipitate the depolymerized lignin.
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Photodissociation of aqueous I3− observed with liquid-phase ultrafast mega-electron-volt electron diffraction
Developing femtosecond resolution methods for directly observing structural dynamics is critical to understanding complex photochemical reaction mechanisms in solution. We have used two recent developments, ultrafast mega-electron-volt electron sources and vacuum compatible sub-micron thick liquid sheet jets, to enable liquid-phase ultrafast electron diffraction (LUED). We have demonstrated the viability of LUED by investigating the photodissociation of tri-iodide initiated with a 400 nm laser pulse. This has enabled the average speed of the bond expansion to be measured during the first 750 fs of dissociation and the geminate recombination to be directly captured on the picosecond time scale.
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WaterTAP3 (The Water Technoeconomic Assessment Pipe-Parity Platform)
The Water Technoeconomic Assessment Pipe-Parity Platform (WaterTAP3) was developed under the National Alliance for Water Innovation (NAWI) to facilitate consistent technoeconomic assessments of desalination treatment trains. The WaterTAP3 is an analytically robust modeling tool that can be used to evaluate water technology cost, energy, environmental, and resiliency tradeoffs across different water sources, sectors, and scales. The model simulates steady-state water treatment train performance and costs including flow and constituent mass balance across unit processes, based on source water conditions, configurations of treatment technologies, and system-level techno-economic assumptions. Users can build a new treatment train by connecting any number of unit processes, specific for their context and system, or selecting a train from the treatment train library. The model contains various technical and cost parameter options for a range of treatment processes and a library of influent water quality characteristics for a variety of source waters and case studies. Users can customize water quality parameters to evaluate the technology performance in their context. The model can be set up for different assessment needs including simulation, optimization, and uncertainty and sensitivity analyses. The results from WaterTAP3 can help identify trade-offs among the different system performance metrics, with insight on how particular technologies or systems promote pipe-parity. The flexibility and comprehensive scope of the tool makes it a promising solution to industry-wide water technoeconomic evaluations, leading to more informed water investment decisions and technologies. As a user-friendly, open-source platform, WaterTAP3 can be used by industry, academia, policymakers, planners, and those with or without extensive analytical experience.
Miara, Ariel↗