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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Photochemical Phosphorus-Hydrogen-Oxygen Network for Hydrogen-dominated Exoplanet Atmospheres

Due to the detection of phosphine (PH 3 ) in the solar system gas giants Jupiter and Saturn, PH 3 has long been suggested to be detectable in exosolar substellar atmospheres too. However, to date, direct detection of phosphine has proven to be elusive in exoplanet atmosphere surveys. We construct an updated phosphorus-hydrogen-oxygen (PHO) photochemical network suitable for the simulation of gas giant hydrogen-dominated atmospheres. Using this network, we examine PHO photochemistry in hot Jupiter and warm Neptune exoplanet atmospheres at solar and enriched metallicities. Our results show for HD 189733b-like hot Jupiters that HOPO, PO, and P 2 are typically the dominant P carriers at pressures important for transit and emission spectra, rather than PH 3 . For GJ1214b-like warm Neptune atmospheres our results suggest that at solar metallicity PH 3 is dominant in the absence of photochemistry, but is generally not in high abundance for all other chemical environments. At 10 and 100 times solar, small oxygenated phosphorus molecules such as HOPO and PO dominate for both thermochemical and photochemical simulations. The network is able to reproduce well the observed PH 3 abundances on Jupiter and Saturn. Despite progress in improving the accuracy of the PHO network, large portions of the reaction rate data remain with approximate, uncertain, or missing values, which could change the conclusions of the current study significantly. Improving understanding of the kinetics of phosphorus-bearing chemical reactions will be a key undertaking for astronomers aiming to detect phosphine and other phosphorus species in both rocky and gaseous exoplanetary atmospheres in the near future.

atmospheric composition↗
DOE OSTI2025

Direct study of changes in catalyst structure-kinetic properties during redox transitions using a new time-resolved technique

Direct study of changes in catalyst structure-kinetic properties during redox transitions using a new time-resolved technique The Temporal Analysis of Products (TAP) pulse response methodology1 is a transient technique offering the time scale needed to deconvolve many reaction steps from the complex network typical to industrial catalytic processes. The traditional TAP measurement observes the gas phase dynamic response but, hereto now, has been devoid of any direct measurement of change in the catalyst itself. The development of an operando technique that couples gas phase transient kinetics to dynamic metal centers and surface species is presented. Significance Unification of the TAP methodology with time-resolved spectroscopic measurement can offer unprecedented insight into the complex kinetic phenomena regulated by the solid catalyst.

03 - NATURAL GAS↗
DOE OSTI2025

Direct Study of Changes in Catalyst Structure-Kinetic Properties During Redox Transitions

The Temporal Analysis of Products (TAP) pulse response methodology is a transient technique that provides the time resolution needed to deconvolve reaction steps from the complex networks typical in industrial catalytic processes. Traditionally, TAP measurements observe gas phase dynamics at the reactor exit but lack direct measurements of changes in the catalyst itself. Recently, a new operando technique was developed that couples gas phase transients to dynamic changes in metal centers with the precise TAP methodology for nanomole titration. Using an industrial CrOx/Al2O3 catalyst used for propane dehydrogenation, we demonstrate the capabilities of this unique device to reveal key catalytic processes: 1) total propane oxidation not associated with chromia centers, 2) reduction of Cr6+ to Cr3+ correlated with selective product formation, and 3) subsequent carbon accumulation. By utilizing incremental pulsing in a diffusion-only transport regime, the spectrokinetic device allows us to resolve detailed changes in catalyst structure, composition, and kinetic function that are otherwise indistinguishable in conventional operando devices. The unification of the TAP methodology with time-resolved spectroscopic measurements offers new and unique insights into the complex kinetic phenomena regulated by solid catalyst surfaces.

03 - NATURAL GAS↗
DOE OSTI2020

Learning and meta-learning of stochastic advection–diffusion–reaction systems from sparse measurements

Physics-informed neural networks (PINNs) were recently proposed in [18] as an alternative way to solve partial differential equations (PDEs). A neural network (NN) represents the solution, while a PDE-induced NN is coupled to the solution NN, and all differential operators are treated using automatic differentiation. Here, we first employ the standard PINN and a stochastic version, sPINN, to solve forward and inverse problems governed by a non-linear advection–diffusion–reaction (ADR) equation, assuming we have some sparse measurements of the concentration field at random or pre-selected locations. Subsequently, we attempt to optimise the hyper-parameters of sPINN by using the Bayesian optimisation method (meta-learning) and compare the results with the empirically selected hyper-parameters of sPINN. In particular, for the first part in solving the inverse deterministic ADR, we assume that we only have a few high-fidelity measurements, whereas the rest of the data is of lower fidelity. Hence, the PINN is trained using a composite multi-fidelity network, first introduced in [12], that learns the correlations between the multi-fidelity data and predicts the unknown values of diffusivity, transport velocity and two reaction constants as well as the concentration field. For the stochastic ADR, we employ a Karhunen–Loève (KL) expansion to represent the stochastic diffusivity, and arbitrary polynomial chaos (aPC) to represent the stochastic solution. Correspondingly, we design multiple NNs to represent the mean of the solution and learn each aPC mode separately, whereas we employ a separate NN to represent the mean of diffusivity and another NN to learn all modes of the KL expansion. For the inverse problem, in addition to stochastic diffusivity and concentration fields, we also aim to obtain the (unknown) deterministic values of transport velocity and reaction constants. The available data correspond to 7spatial points for the diffusivity and 20 space–time points for the solution, both sampled 2000 times. We obtain good accuracy for the deterministic parameters of the order of 1–2% and excellent accuracy for the mean and variance of the stochastic fields, better than three digits of accuracy. In the second part, we consider the previous stochastic inverse problem, and we use Bayesian optimisation to find five hyper-parameters of sPINN, namely the width, depth and learning rate of two NNs for learning the modes. Here, we obtain much deeper and wider optimal NNs compared to the manual tuning, leading to even better accuracy, i.e., errors less than 1% for the deterministic values, and about an order of magnitude less for the stochastic fields.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

A new way to unravel the $^{12}$C($\alpha $,$\gamma $)$^{16}$O cross section components using neural networks

The $^{12}$C($\alpha $,$\gamma $)$^{16}$O reaction rate is crucial in determining the carbon-to-oxygen abundance ratio in stellar nucleosynthesis. Measuring this reaction’s cross section at stellar energies is challenging due to its extremely small value, approximately 10 -17 barn at E c.m. = 300 keV. To address this, R-matrix calculations are employed to extrapolate data to lower energies, requiring a comprehensive understanding of each contribution to the cross section. The dominant contributions to the cross section at stellar energies arise from electric dipole (E1) and electric quadrupole (E2) transitions to the ground state of 16 O, along with a significant cascade contribution. Traditionally, these contributions have been separated using the γ-ray angular distribution. In this work, we propose a novel technique using the energy distribution of the 16 O recoils at the focal plane. This method involves a neural network trained on detailed Monte Carlo simulations of the energy distribution of recoils transported through the recoil mass separator ERNA. This approach enables the simultaneous determination of all three contributions with errors around 10% in the energy range E c.m. = 1.0–2.2 MeV. In conclusion, by employing this new technique, we aim to significantly improve the accuracy of determining the cross section of the $^{12}$C($\alpha $,$\gamma $)$^{16}$O reaction at astrophysical energies.

Astrochemistry↗
NASA NTRS1984

Performance and cost of energy transport and storage systems for dish applications using reversible chemical reactions

The use of reversible chemical reactions for energy transport and storage for parabolic dish networks is considered. Performance and cost characteristics are estimated for systems using three reactions (sulfur-trioxide decomposition, steam reforming of methane, and carbon-dioxide reforming of methane). Systems are considered with and without storage, and in several energy-delivery configurations that give different profiles of energy delivered versus temperature. Cost estimates are derived assuming the use of metal components and of advanced ceramics. (The latter reduces the costs by three- to five-fold). The process that led to the selection of the three reactions is described, and the effects of varying temperatures, pressures, and heat exchanger sizes are addressed. A state-of-the-art survey was performed as part of this study. As a result of this survey, it appears that formidable technical risks exist for any attempt to implement the systems analyzed in this study, especially in the area of reactor design and performance. The behavior of all components and complete systems under thermal energy transients is very poorly understood. This study indicates that thermochemical storage systems that store reactants as liquids have efficiencies below 60%, which is in agreement with the findings of earlier investigators.

Schredder, J. M.↗
DOE OSTI2025

HEPOM: Using Graph Neural Networks for the Accelerated Predictions of Hydrolysis Free Energies in Different pH Conditions

Hydrolysis is a fundamental family of chemical reactions where water facilitates the cleavage of bonds. The process is ubiquitous in biological and chemical systems, owing to water’s remarkable versatility as a solvent. However, accurately predicting the feasibility of hydrolysis through computational techniques is a difficult task, as subtle changes in reactant structure like heteroatom substitutions or neighboring functional groups can influence the reaction outcome. Furthermore, hydrolysis is sensitive to the pH of the aqueous medium, and the same reaction can have different reaction properties at different pH conditions. In this work, we have combined reaction templates and high-throughput ab initio calculations to construct a diverse data set of hydrolysis free energies. The developed framework automatically identifies reaction centers, generates hydrolysis products, and utilizes a trained graph neural network (GNN) model to predict ΔG values for all potential hydrolysis reactions in a given molecule. The long-term goal of the work is to develop a data-driven, computational tool for high-throughput screening of pH-specific hydrolytic stability and the rapid prediction of reaction products, which can then be applied in a wide array of applications including chemical recycling of polymers and ion-conducting membranes for clean energy generation and storage.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Understanding why poly(acrylic acid) works: decarbonylation and cross-linking provide an ionically conductive passivation layer in silicon anodes

Poly(acrylic acid) (PAA) is commonly used as a binder for fabricating silicon (Si) anode active materials in lithium-ion batteries due to its useful properties including high polar solvent solubility, good rheology, and strong adhesive properties. However, the role and evolution of PAA during electrode fabrication, cycling, and calendar aging are not well understood. In this work, we reveal the evolution of PAA during electrode curing and relate its chemical change to the final electrode properties and performance. These studies are made possible using two types of in situ attenuated total reflectance-infrared Fourier transform (ATR-FTIR) spectroscopy: thermal ATR-FTIR to probe the cross-linking reaction, and ATR-FTIR spectroelectrochemistry of three-dimensional composite electrodes, a unique technique developed herein that probes the solvation dynamics of lithium ions at the silicon anode interface under electrochemical polarization. Specifically, we show that PAA undergoes a thermally-mediated, cross-linking decarbonylation reaction to form an ether-based network polymer. To show the importance of the polyether moieties, we synthesize partially esterified PAA analogues that do not undergo this cross-linking decarbonylation reaction and correlate the degree of cross-linking to half-cell performance metrics. Finally, we unveil the mechanism of the polyether binder performance through in situ ATR-FTIR spectroelectrochemistry and show that PAA acts as an interfacial material that conducts lithium-ions, limits solvent molecule access to the Si surface, and stabilizes the electrode against parasitic lithium inventory loss at high state of charge for an extended period of time.

25 ENERGY STORAGE↗
DOE OSTI2022

Nonlocal Kernel Network (NKN): a Stable and Resolution-Independent Deep Neural Network.

Neural operators have recently become popular tools for designing solution maps between function spaces in the form of neural networks. Differently from classical scientific machine learning approaches that learn parameters of a known partial differential equation (PDE) for a single instance of the input parameters at a fixed resolution, neural operators approximate the solution map of a family of PDEs [6, 7]. Despite their success, the uses of neural operators are so far restricted to relatively shallow neural networks and confined to learning hidden governing laws. In this work, we propose a novel nonlocal neural operator, which we refer to as nonlocal kernel network (NKN), that is resolution independent, characterized by deep neural networks, and capable of handling a variety of tasks such as learning governing equations and classifying images. Our NKN stems from the interpretation of the neural network as a discrete nonlocal diffusion reaction equation that, in the limit of infinite layers, is equivalent to a parabolic nonlocal equation, whose stability is analyzed via nonlocal vector calculus. The resemblance with integral forms of neural operators allows NKNs to capture long-range dependencies in the feature space, while the continuous treatment of node-to-node interactions makes NKNs resolution independent. The resemblance with neural ODEs, reinterpreted in a nonlocal sense, and the stable network dynamics between layers allow for generalization of NKN’s optimal parameters from shallow to deep networks. This fact enables the use of shallow-to-deep initialization techniques [8]. Our tests show that NKNs outperform baseline methods in both learning governing equations and image classification tasks and generalize well to different resolutions and depths.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Synthesis, Hole Doping, and Electrical Properties of a Semiconducting Azatriangulene-Based Covalent Organic Framework

Two-dimensional covalent organic frameworks (2D COFs) containing heterotriangulenes have been theoretically identified as semiconductors with tunable, Dirac-cone-like band structures, which are expected to afford high charge-carrier mobilities ideal for next-generation flexible electronics. However, few bulk syntheses of these materials have been reported, and existing synthetic methods provide limited control of network purity and morphology. Here, we report transimination reactions between benzophenone-imine-protected azatriangulenes (OTPA) and benzodithiophene dialdehydes (BDT), which afforded a new semiconducting COF network, OTPA-BDT. The COFs were prepared as both polycrystalline powders and thin films with controlled crystallite orientation. Here, the azatriangulene nodes are readily oxidized to stable radical cations upon exposure to an appropriate p type dopant, tris(4-bromophenyl)ammoniumyl hexachloroantimonate, after which the network’s crystallinity and orientation are maintained. Oriented, hole-doped OTPA-BDT COF films exhibit electrical conductivities of up to 1.2 × 10 –1 S cm –1 , which are among the highest reported for imine-linked 2D COFs to date.

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

Spatial modeling algorithms for reactions and transport in biological cells

Biological cells rely on precise spatiotemporal coordination of biochemical reactions to control their functions. Such cell signaling networks have been a common focus for mathematical models, but they remain challenging to simulate, particularly in realistic cell geometries. Here we present Spatial Modeling Algorithms for Reactions and Transport (SMART), a software package that takes in high-level user specifications about cell signaling networks and then assembles and solves the associated mathematical systems. SMART uses state-of-the-art finite element analysis, via the FEniCS Project software, to efficiently and accurately resolve cell signaling events over discretized cellular and subcellular geometries. We demonstrate its application to several different biological systems, including yes-associated protein (YAP)/PDZ-binding motif (TAZ) mechanotransduction, calcium signaling in neurons and cardiomyocytes, and ATP generation in mitochondria. Throughout, we utilize experimentally derived realistic cellular geometries represented by well-conditioned tetrahedral meshes. These scenarios demonstrate the applicability, flexibility, accuracy and efficiency of SMART across a range of temporal and spatial scales.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Redox-enabled electronic interrogation and feedback control of hierarchical and networked biological systems

Abstract Microelectronic devices can directly communicate with biology, as electronic information can be transmitted via redox reactions within biological systems. By engineering biology’s native redox networks, we enable electronic interrogation and control of biological systems at several hierarchical levels: proteins, cells, and cell consortia. First, electro-biofabrication facilitates on-device biological component assembly. Then, electrode-actuated redox data transmission and redox-linked synthetic biology allows programming of enzyme activity and closed-loop electrogenetic control of cellular function. Specifically, horseradish peroxidase is assembled onto interdigitated electrodes where electrode-generated hydrogen peroxide controls its activity. E. coli ’s stress response regulon, oxyRS , is rewired to enable algorithm-based feedback control of gene expression, including an eCRISPR module that switches cell-cell quorum sensing communication from one autoinducer to another—creating an electronically controlled ‘bilingual’ cell. Then, these disparate redox-guided devices are wirelessly connected, enabling real-time communication and user-based control. We suggest these methodologies will help us to better understand and develop sophisticated control for biology.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Interrogating biomineralization one amino acid at a time: amplification of mutational effects in protein-aided titania morphogenesis through reaction-diffusion control

To emulate the control that biomineralizing organisms exert over reactant transport, we construct a countercurrent reaction-diffusion chamber in which an agarose hydrogel regulates the fluxes of inorganic precursor and precipitating solid-binding protein. Furthermore, we show that the morphology of the bioprecipitated titania can be changed from monolithic to interconnected particle networks and dispersed nanoparticles either by decreasing reaction time or by increasing agarose weight percentage at constant precursor and protein concentrations. More strikingly, protein variants with one or two substitutions in their metal oxide-binding domain yield unique peripheral morphologies (needles, threads, plates, and peapods) with distinct crystallography and photocatalytic activity. Our results suggest that diffusional control can magnify otherwise subtle mutational effects in biomineralizing proteins and provide a path for the green synthesis of morphologically and functionally diverse inorganic materials.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Toward transferable empirical valence bonds: Making classical force fields reactive

The empirical valence bond technique allows classical force fields to model reactive processes. However, parametrization from experimental data or quantum mechanical calculations is required for each reaction present in the simulation. We show that the parameters present in the empirical valence bond method can be predicted using a neural network model and the SMILES strings describing a reaction. This removes the need for quantum calculations in the parametrization of the empirical valence bond technique. In doing so, we have taken the first steps toward defining a new procedure for enabling reactive atomistic simulations. This procedure would allow researchers to use existing classical force fields for reactive simulations, without performing additional quantum mechanical calculations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale

The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by 13 C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Gas-Phase Synthesis of Coronene through Stepwise Directed Ring Annulation

Molecular beam experiments together with electronic structure calculations provide the first evidence of a complex network of elementary gas-phase reactions culminating in the bottom-up preparation of the 24π aromatic coronene (C 24 H 12 ) molecule–a representative peri-fused polycyclic aromatic hydrocarbon (PAH) central to the complex chemistry of combustion systems and circumstellar envelopes of carbon stars. The gas-phase synthesis of coronene proceeds via aryl radical-mediated ring annulations through benzo[e]pyrene (C 20 H 12 ) and benzo[ghi]perylene (C 22 H 12 ) involving armchair-, zigzag-, and arm-zig-edged aromatic intermediates, highlighting the chemical diversity of molecular mass growth processes to polycyclic aromatic hydrocarbons. Furthermore, the isomer-selective identification of five- to six-ringed aromatics culminating with the detection of coronene is accomplished through photoionization and is based upon photoionization efficiency curves along with photoion mass-selected threshold photoelectron spectra, providing a versatile concept of molecular mass growth processes via aromatic and resonantly stabilized free radical intermediates to two-dimensional carbonaceous nanostructures.

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

Link Between CO 2 –Induced Wettability and Pore Architecture Alteration

Changes in pore (throat) size, surface roughness and mineralogy induced by supercritical-CO 2 -water-rock reactions impact petrophysical properties such as porosity, permeability, and especially wettability. Herein, we show that these changes directly impact relative permeability and capillary pressure curves, a fact rarely studied in the literature. In this work, we show that CO 2 contact angle changes emerge after Madison Limestone samples were soaked for 400 hours in CO 2 -enriched brine. Coreflooding results show that the water production rate and cumulative water production increased after the rock was exposed to carbonic acid. Moreover, the mercury capillary pressure decreased in meso- and macro-pores, indicating the increase of size in these pores due to reactions. This compounded wettability and pore network alteration can directly affect CO 2 injectivity, migration and storage capacity. Furthermore, this fundamental insight into CO 2 geological storage processes should aid practitioners to reduce uncertainties in forecasting CO 2 distribution via injection simulation.

58 GEOSCIENCES↗
DOE OSTI2023

The evolution of HCO + in molecular clouds using a novel chemical post-processing algorithm

Modelling the chemistry of molecular clouds is critical to accurately simulating their evolution. To reduce computational cost, 3D simulations generally restrict their chemistry to species with strong heating and cooling effects. Time-dependent information about the evolution of other species is therefore often neglected. We address this gap by post-processing tracer particles in the SILCC-Zoom molecular cloud simulations. Using a chemical network of 39 species and 301 reactions (including freeze-out of CO and H 2 O) and a novel algorithm to reconstruct a density grid from sparse tracer particle data, we produce time-dependent density distributions for various species. We focus upon the evolution of HCO + , which is a critical formation reactant of CO but is not typically modelled on the fly. We find that ∼ 90 per cent of the HCO + content of the cold molecular gas forms in situ around n HCO + ∼ 10 3 –10 4 cm −3 , over a time-scale of approximately 1 Myr. The remaining ∼ 10 per cent forms at high extinction sites, with minimal turbulent mixing out into the less dense gas. We further show that the dominant HCO + formation pathway is dependent on the visual extinction, with the reaction H 3 + + CO contributing 90 per cent of the total HCO + production above A V, 3D = 3. We produce the very first maps of the HCO + column density, N(HCO + ), and show that it reaches values as high as 10 15 cm −2 . We find that 50 per cent of the HCO + mass is located within AV ∼ 10–30 in a density range of 10 3.5 –10 4.5 cm −3 . Our maps of N(HCO + ) are shown to be in good agreement with recent observations of the W49A star-forming region.

79 ASTRONOMY AND ASTROPHYSICS↗