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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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At least 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Tensor Network Space-Time Spectral Collocation Method for Time-Dependent Convection-Diffusion-Reaction Equations

Emerging tensor network techniques for solutions of partial differential equations (PDEs), known for their ability to break the curse of dimensionality, deliver new mathematical methods for ultra-fast numerical solutions of high-dimensional problems. Here, we introduce a Tensor Train (TT) Chebyshev spectral collocation method, in both space and time, for the solution of the time-dependent convection-diffusion-reaction (CDR) equation with inhomogeneous boundary conditions, in Cartesian geometry. Previous methods for numerical solution of time-dependent PDEs often used finite difference for time, and a spectral scheme for the spatial dimensions, which led to a slow linear convergence. Spectral collocation space-time methods show exponential convergence; however, for realistic problems they need to solve large four-dimensional systems. We overcome this difficulty by using a TT approach, as its complexity only grows linearly with the number of dimensions. We show that our TT space-time Chebyshev spectral collocation method converges exponentially, when the solution of the CDR is smooth, and demonstrate that it leads to a very high compression of linear operators from terabytes to kilobytes in TT-format, and a speedup of tens of thousands of times when compared to a full-grid space-time spectral method. These advantages allow us to obtain the solutions at much higher resolutions.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Enhancing Drinking Water Quality Modeling: Leveraging Physics Informed Neural Networks for Learning with Imperfect Reaction Models and Partial Data

Chemical kinetics models, typically formulated as systems of ordinary or partial differential equations, are valuable tools for simulating drinking water quality. However, these models often face inaccuracies due to discrepancies between the laboratory and the real-world conditions, as well as limitations in experimental analytical methods, hindering the accurate representation of the true underlying chemical mechanisms. In this study, we propose a Physics Informed Neural Network (PINN), using the eXtreme Theory of Functional Connections, to improve the prediction of chemical concentrations over time. The PINN method accounts for imperfect chemical models and incorporates partial data to improve predictions. Focusing on reactions describing water disinfection residual and disinfectant byproduct formation, which are crucial for public health and regulatory compliance, we demonstrate that the PINN model is able to accurately predict the concentrations of chemical species across various pH values. Notably, the model extends its accuracy to predict concentrations of chemical species not originally included in its training data. The developed method can be extended to a variety of chemical systems, offering a wide array of potential applications.

13 HYDRO ENERGY↗
DOE OSTI2021

New Insights Into Lignification via Network and Multi-Omics Analyses of Arogenate Dehydratase Knock-Out Mutants in Arabidopsis thaliana

Multiple Arabidopsis arogenate dehydratase (ADT) knock-out (KO) mutants, with phenotypes having variable lignin levels (up to circa 70% reduction), were studied to investigate how differential reductions in ADTs perturb its overall plant systems biology. Integrated "omics" analyses (metabolome, transcriptome, and proteome) of wild type (WT), single and multiple ADT KO lines were conducted. Transcriptome and proteome data were collapsed into gene ortholog (GO) data, with this allowing for enzymatic reaction and metabolome cross-comparisons to uncover dominant or likely metabolic biosynthesis reactions affected. Network analysis of enzymes-highly correlated to stem lignin levels-deduced the involvement of novel putative lignin related proteins or processes. These included those associated with ribosomes, the spliceosome, mRNA transport, aminoacyl tRNA biosynthesis, and phosphorylation. While prior work helped explain lignin biosynthesis regulation at the transcriptional level, our data here provide support for a new hypothesis that there are additional post-transcriptional and translational level processes that need to be considered. These findings are anticipated to lead to development of more accurate depictions of lignin/phenylpropanoid biosynthesis models in situ, with new protein targets identified for further biochemical analysis and/or plant bioengineering. Additionally, using KEGG defined functional categorization of proteomics and transcriptomics analyses, we detected significant changes to glucosinolate, a-linolenic acid, nitrogen, carotenoid, aromatic amino acid, phenylpropanoid, and photosynthesis-related metabolic pathways in ADT KO mutants. Metabolomics results also revealed that putative carotenoid and galactolipid levels were generally increased in amount, whereas many glucosinolates and phenylpropanoids (including flavonoids and lignans) were decreased in the KO mutants.

Arabidopsis, arogenate dehydratases, lignin, multi↗
DOE OSTI2026

Kinetic Monte Carlo Framework for Coupled Degradation and Dehydration of Anion Exchange Membranes

Kinetic Monte Carlo (kMC) simulations, augmented with temporal-acceleration schemes, can efficiently handle stiff reaction-transport networks when fast processes rapidly relax to quasi-equilibrium on a fixed lattice. However, in glassy anion-exchange membranes (AEM), rare and irreversible chemical degradation events continuously reshape the nanoscale morphology, and the associated hydration and transport degrees of freedom remain far from a well-defined local equilibrium. This combination of evolving state space and nonequilibrated fast dynamics lies outside the scope of existing kMC acceleration frameworks. Here, to address this challenge, we introduce an auxiliary-particle kinetic Monte Carlo (AP-kMC) scheme. In AP-kMC, short-lived mobile particles spawned at degradation sites execute hop, water-elimination, and decay moves, enforcing rapid local relaxation of the hydration structure while preserving the stochastic rules of kMC. Parameterized with molecular-dynamics morphologies and experimental solution degradation kinetics, AP-kMC reproduces the evolution of ion-exchange capacity, water uptake, and conductivity, and reveals a feedback loop in which poorly hydrated sites degrade first and each degradation event induces further local dehydration. The resulting thinning and fragmentation of water channels cause loss of hydrophilic percolation and abrupt conductivity collapse well before complete charge loss. AP-kMC thus reframes AEM durability as a coupled degradation–drying–percolation problem and provides a transferable strategy to simulate reactive, out-of-equilibrium polymer electrolytes where local solvation controls reactivity.

organic↗
DOE OSTI2022

Computational Aspects of Single-Molecule Kinetics for Coupled Catalytic Cycles: A Spectral Analysis

Catalysis from single active sites is analyzed using methods developed from single molecule kinetics. Using a stochastic Markov state description, the observable properties of general catalytic networks of reactions are expressed using an eigenvalue decomposition of the transition matrix for the Markov process. By the use of a sensitivity analysis, the necessary eigenvalues and eigenvectors are related to the energies of controlling barriers and wells located along the reaction routes. A generalization of the energetic span theory allows the eigenvalues to be computed from several activation energies corresponding to distinct barrier-well pairings. The formalism is demonstrated accurately for model problems for a physically realistic mechanism for an alkene hydrogenation reaction on a single atom catalyst.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

EF-Hand Battle Royale: Hetero-ion Complexation in Lanmodulin

The lanmodulin (LanM) protein has emerged as an effective means for rare earth element (REE) extraction and separation from complex feedstocks without the use of organic solvents. Whereas the binding of LanM to individual REEs has been well characterized, little is known about the thermodynamics of mixed metal binding complexes (i.e., heterogeneous ion complexes), which limits the ability to accurately predict separation performance for a given metal ion mixture. In this paper, we employ the law of mass action to establish a theory of perfect cooperativity for LanM-REE complexation at the two highest-affinity binding sites. The theory is then used to derive an equation that explains the nonintuitive REE binding behavior of LanM, where separation factors for binary pairs of ions vary widely based on the ratio of ions in the aqueous phase, a phenomenon that is distinct from single-ion-binding chemical chelators. We then experimentally validate this theory and perform the first quantitative characterization of LanM complexation with heterogeneous ion pairs using resin-immobilized LanM. Importantly, the resulting homogeneous and heterogeneous constants enable accurate prediction of the equilibrium state of LanM in the presence of mixtures of up to 10 REEs, confirming that the perfect cooperativity model is an accurate mechanistic description of REE complexation by LanM. We further employ the model to simulate separation performance over a range of homogeneous and heterogeneous binding constants, revealing important insights into how mixed binding differentially impacts REE separations based on the relative positioning of the ion pairs within the lanthanide series. In addition to informing REE separation process optimization, these results provide mathematical and experimental insight into competition dynamics in other ubiquitous and medically relevant, cooperative binding proteins, such as calmodulin.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

SODAs: sparse optimization for the discovery of differential and algebraic equations

Differential-algebraic equations (DAEs) integrate ordinary differential equations (ODEs) with algebraic constraints, providing a fundamental framework for developing models of dynamical systems characterized by time-scale separation, conservation laws and physical constraints. While sparse optimization has revolutionized model development by allowing data-driven discovery of parsimonious models from a library of possible equations, existing approaches for dynamical systems assume DAEs can be reduced to ODEs by eliminating variables before model discovery. This assumption limits the applicability of such methods for DAE systems with unknown constraints and time scales. We introduce sparse optimization for differential-algebraic systems (SODAs), a data-driven method for the identification of DAEs in their explicit form. By discovering the algebraic and dynamic components sequentially without prior identification of the algebraic variables, this approach leads to a sequence of convex optimization problems. It has the advantage of discovering interpretable models that preserve the structure of the underlying physical system. To this end, SODAs improves since SODAs is singular numerical stability when handling high correlations between library terms, caused by near-perfect algebraic relationships, by iteratively refining the conditioning of the candidate library. We demonstrate the performance of our method on biological, mechanical and electrical systems, showcasing its robustness to noise in both simulated time series and real-time experimental data.

DAE↗
DOE OSTI2024

Study of electron-induced chemical transformations in polymers

In extreme ultraviolet (EUV) photoresist exposure, the primary and secondary electrons drive chemistry rather than the EUV photons themselves. These electrons have a wide range of energies below approximately 80 eV, which are capable of complex network of reactions during exposure. To better understand the ability of electrons of different energies within the EUV primary and secondary electron range, we want to characterize and compare the chemistry induced in pure polymer films by direct exposure to electrons. Thin films of poly(tert-butyl methacrylate), poly(methyl methacrylate), and poly(4-hydroxystyrene) were exposed to a 20 to 80 eV electron beam. Outgassing during exposure was characterized in-situ using a quadrupole residual gas analyzer. The thickness changes were measured using ellipsometry and chemical bond structure data were collected using Fourier-transform infrared spectroscopy (FTIR) after exposure to compare different exposure conditions. Poly(4-hydroxystyrene) demonstrated stability during electron exposures. Exposures of the other two materials led to outgassing of protecting groups, the intensity of which decayed in time. Outgassing, FTIR, and thickness loss data exhibited approximately linear relationships to each other.

Mueller, Maximillian↗
DOE OSTI2022

Singe Library Review: XCAP Review Report

Singe is a computational library for simulating thermonuclear reactions written in C++14. It was inspired by the earlier Singe library, developed by Joann Campbell. From the beginning of the Singe project, the goal has been to create a library that provides the necessary functionality, runs efficiently, and is flexible enough to connect to a variety of multi-physics host codes. Such a library allows subject-matter experts to focus on development within the library, while library staff can support host-code staff in integrating Singe into their multi-physics codes. A common library used across multiple multi-physics codes allows those subject-matter experts to roll out new features to all host codes simultaneously, and eases the process of code-to-code comparison between codes that are leveraging the same physics in different ways. The Singe effort was established by Joann Campbell, who provided significant guidance on the goals, requirements, and philosophy of the library. It has been developed by Brendan K. Krueger, CJ Solomon, Jesse Canfield, and Terry Adams, with computer science support from Tyler Reddy and Karen Tsai. Discussions with multiple potential stakeholders have been critical to the planning of such a cross-cutting project, including representatives from multiple host codes and Brian Haines who helped define usage and needs for the inertial confinement fusion (ICF) program. This document is based on the XCAP review process. It is an updated version of the initial document distributed to the reviewers, memo XCP-2:21-005. We want to acknowledge the time and effort of the reviewers who participated in this process: Philipp Edelmann, Alan Harrison, Christopher Malone, Edward Norris, Ryosuke Park, Galen Shipman, CJ Solomon, and any others who may have contributed to the feedback that was provided to us.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Using 14 C-acetate Pulse-chase Labeling to Study Fatty Acid and Glycerolipid Metabolism in Plant Leaves

Lipids metabolism is comprised of networks of reactions occurred in different subcellular compartments. Isotopic labeling is a good way to track the transformations and movements of metabolites without perturbing overall cellular metabolism. Fatty acids, the building blocks of membrane lipids and storage triacylglycerols, are synthesized in plastids. The immediate precursor for fatty acid synthesis is acetyl-CoA. Exogenous acetate is rapidly incorporated into fatty acids in leaves and isolated plastids because it can diffuse freely through cellular membranes, enter the plastid where it is rapidly metabolized to acetyl-CoA. Therefore, isotope-labeled acetate is often used as a tracer for the investigation of fatty acid synthesis and complex lipid metabolism in plants and other organisms. The basic principle of isotope labeling and its recent technical advances have been reviewed (Allen et al., 2015). The present protocol describes the use of 14C-labeled acetate to determine rates of fatty acid synthesis and degradation and to track the metabolism of glycerolipids in leaves. This method, which is often referred to as acetate pulse-chase labeling, has been widely used to probe various aspects of lipid metabolism (Allen et al., 2015), including the role of autophagy in membrane lipid turnover (Fan et al., 2019) and the interplay between lipid and starch metabolism pathways (Yu et al., 2018).

14C-acetate pulse-chase↗
DOE OSTI2023

HT Model Dataset

This website contains the dataset that was used for writing the manuscript "HT Model: Using the Molecular Transformer for predicting hydrotreating reactions" (PNNL-SA-186589) The dataset includes a collection of hydrotreating reactions compiled from 41 peer-reviewed literature sources. These sources contain experimental data related to hydrotreating reactions. These reactions involve the reaction of chemical compounds with hydrogen gas in the presence of a catalyst to remove heteroatoms or to convert specific functional groups. The dataset contains reactions both with and without reaction conditions. Reaction conditions refer to the specific parameters under which the reaction takes place, such as temperature and pressure. For each reaction, the dataset includes both SMILES and SELFIES representations. SMILES (Simplified Molecular Input Line Entry System) and SELFIES (SELF-referencIng Embedded Strings) are two popular notations used to represent chemical structures in a compact and standardized format. The dataset was created with the aim of training a predictive model, specifically using the Molecular Transformer architecture.

bioprocessing, hydrotreating, deep learning algori↗
DOE OSTI2024

Catalytic resonance theory: Circumfluence of programmable catalytic loops

Chemical reactions on heterogeneous catalyst surfaces exhibit complex networks of elementary reactions with multiple pathways to fluid phase products, sometimes leading to surface reaction loops consisting of a closed cycle reaction pathway. While conventional catalysts at steady state exhibit zero net flux in either direction around a catalytic loop, the loop turnover frequency of three-species surface loops was evaluated in this work via microkinetic modeling to assess the reaction loop behavior resulting from a catalytic surface oscillating between two or more surface energy states. For dynamic heterogeneous catalysts undergoing applied oscillations of surface energy (i.e., programs), surface reaction loops of three species were shown to exhibit non-zero net flow of molecules around the loop, with the extent of loop turnover frequency varying with the applied frequency and amplitude of a sinusoidal or square wave oscillation. Alternatively, some dynamic surface reactions exhibited oscillations only between two surface species or resulted in surfaces covered by a single species at all times. Here, more complex behavior was observed for dynamic surface programs of three distinct electronic states, with the temporal order of applied states controlling the direction of flow of molecules within a three-molecule surface loop. Catalytic loops have the potential to limit overall catalytic reaction rates and use energy in programmable catalysts, while some applications could purposely impose non-zero loop turnover frequency for improved surface reaction control.

Catalysis↗
DOE OSTI2023

Understanding the Effect of Side Reactions on the Recyclability of Furan–Maleimide Resins Based on Thermoreversible Diels–Alder Network

We studied the effect of side reactions on the reversibility of epoxy with thermoreversible Diels–Alder (DA) cycloadducts based on furan and maleimide chemistry. The most common side reaction is the maleimide homopolymerization which introduces irreversible crosslinking in the network adversely affecting the recyclability. The main challenge is that the temperatures at which maleimide homopolymerization can occur are approximately the same as the temperatures at which retro-DA (rDA) reactions depolymerize the networks. Here we conducted detailed studies on three different strategies to minimize the effect of the side reaction. First, we controlled the ratio of maleimide to furan to reduce the concentration of maleimide groups which diminishes the effects of the side reaction. Second, we applied a radical-reaction inhibitor. Inclusion of hydroquinone, a known free radical scavenger, is found to retard the onset of the side reaction both in the temperature sweep and isothermal measurements. Finally, we employed a new trismaleimide precursor that has a lower maleimide concentration and reduces the rate of the side reaction. Our results provide insights into how to minimize formation of irreversible crosslinking by side reactions in reversible DA materials using maleimides, which is important for their application as novel self-healing, recyclable, and 3D-printable materials.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2020

Fast predictions of liquid-phase acid-catalyzed reaction rates using molecular dynamics simulations and convolutional neural networks

The rates of liquid-phase, acid-catalyzed reactions relevant to the upgrading of biomass into high-value chemicals are highly sensitive to solvent composition and identifying suitable solvent mixtures is theoretically and experimentally challenging. We show that the complex atomistic configurations of reactant–solvent environments generated by classical molecular dynamics simulations can be exploited by 3D convolutional neural networks to enable accurate predictions of Brønsted acid-catalyzed reaction rates for model biomass compounds. We develop a 3D convolutional neural network, which we call SolventNet, and train it to predict acid-catalyzed reaction rates using experimental reaction data and corresponding molecular dynamics simulation data for seven biomass-derived oxygenates in water–cosolvent mixtures. We show that SolventNet can predict reaction rates for additional reactants and solvent systems an order of magnitude faster than prior simulation methods. This combination of machine learning with molecular dynamics enables the rapid, high-throughput screening of solvent systems and identification of improved biomass conversion conditions.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Prediction of local concentration fields in porous media with chemical reaction using a multi scale convolutional neural network

The study of solute transport in porous media is of interest in many chemical engineering systems. Some example applications include packed bed catalytic reactors, filtration devices, and batteries. The pore scale modeling of these systems is time consuming and may require large computing resources, for this reason computational fluid dynamics (CFD) simulations are not practical if a large number of simulations is required, like in multiscale modeling, where a model at a large scale calls for pore scale simulations. It has been shown that neural networks can be trained with a dataset of flow simulations and then predict fields orders of magnitude faster, and with less computational resources, in new domains. However, it is crucial to provide the neural network with an effective description of the domain and the undergoing operating conditions to be able to train models that generalize accurately in unseen samples. Therefore, research is needed to employ neural networks in new complex systems. The appropriate training of a network for predicting coupled flow and solute transport processes is an outstanding problem due to the complex interplay between geometry and operating conditions. In this work, we train a multi scale convolutional neural network (MSNet) with a diverse dataset of simulations of transport and chemical reaction in porous media to predict the local concentration fields in images of porous media. Our dataset contains a wide diversity of sphere pack arrangements under different operating conditions (Péclet and Reynolds numbers). Further, we train a robust model by employing different input descriptors that represent the medium and the different operating conditions of each system. Our trained model is able to provide nearly instantaneous predictions, compared to around twenty hours of the CFD workflow, with less than 3.5% error on new geometries and transport conditions. Thus the model could be easily integrated in a multiscale workflow where fast response is needed.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Determining the Reaction Kinetics and Thermodynamics of a Diels–Alder Network Using Dynamic Gel Criteria

We undertook a detailed rheological investigation to evaluate the kinetic parameters of the forward and reverse Diels–Alder (DA) reactions of a model network cross-linked using a furan prepolymer and a common aromatic bismaleimide. At high temperature where the Winter–Chambon’s criterion of frequency-independence was more applicable, a multiwave technique permitted van’t Hoff analysis and calculation of the reaction thermodynamic parameters, specifically the enthalpy and entropy of the reaction: ΔH° = –38.3 ± 5.2 kJ mol –1 and ΔS° = –94.3 ± 13.4 J mol –1 . At mild temperatures where the G'–G" crossover point is experimentally convenient to measure gelation, isothermal tests were used to obtain reasonable fDA kinetic parameters from Eyring analysis such as the apparent activation enthalpy and entropy of ΔH$^{‡}_{fDA}$ = 76.8 ± 6.9 kJ mol –1 and ΔS$^{‡}_{fDA}$= –82.8 ± 22.2 J mol –1 K –1 . Comparable rheokinetic methods include cross-linking density measurements and stress relaxation tests to calculate effective kinetics, whereas the critical gel conversion was consistently applied here. As a result, rate data are fitted with the Arrhenius equation for comparison purposes and the Eyring equation to demonstrate its broader utility.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Dynamic reaction-induced phase separation in tunable, adaptive covalent networks

A series of catalyst-free, room temperature dynamic bonds derived from a reversible thia-Michael reaction are utilized to access mechanically robust dynamic covalent network films. The equilibrium of the thiol addition to benzalcyanoacetate-based Michael-acceptors can be directly tuned by controlling the electron-donating/withdrawing nature of the Michael-acceptor. By modulating the composition of different Michael-acceptors in a dynamic covalent network, a wide range of mechanical properties and thermal responses can be realized. Additionally, the reported systems phase-separate in a process, coined dynamic reaction-induced phase separation (DRIPS), that yields reconfigurable phase morphologies and reprogrammable shape-memory behaviour as highlighted by the heat-induced folding of a predetermined structure.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗