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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Accelerating Discovery of Solid‐State Thin‐Film Metal Dealloying for 3D Nanoarchitecture Materials Design through Laser Thermal Gradient Treatment

Thin‐film solid‐state metal dealloying (thin‐film SSMD) is a promising method for fabricating nanostructures with controlled morphology and efficiency, offering advantages over conventional bulk materials processing methods for integration into practical applications. Although machine learning (ML) has facilitated the design of dealloying systems, the selection of key thermal treatment parameters for nanostructure formation remains largely unknown and dependent on experimental trial and error. To overcome this challenge, a workflow enabling high‐throughput characterization of thermal treatment parameters is demonstrated using a laser‐based thermal treatment to create temperature gradients on single thin‐film samples of Nb‐Al/Sc and Nb‐Al/Cu. This continuous thermal space enables observation of dealloying transitions and the resulting nanostructures of interest. Through synchrotron X‐ray multimodal and high‐throughput characterization, critical transitions and nanostructures can be rapidly captured and subsequently verified using electron microscopy. The key temperatures driving chemical reactions and morphological evolutions are clearly identified. While the oxidation may influence nanostructure formation during thin‐film treatment, the dealloying process at the dealloying front involves interactions solely between the dealloying elements, highlighting the availability and viability of the selected systems. Further, this approach enables efficient exploration of the dealloying process and validation of ML predictions, thereby accelerating the discovery of thin‐film SSMD systems with targeted nanostructures.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Stability of hydrides in sub-Neptune exoplanets with thick hydrogen-rich atmospheres

Many sub-Neptune exoplanets have been believed to be composed of a thick hydrogen-dominated atmosphere and a high-temperature heavier-element-dominant core. From an assumption that there is no chemical reaction between hydrogen and silicates/metals at the atmosphere–interior boundary, the cores of sub-Neptunes have been modeled with molten silicates and metals (magma) in previous studies. In large sub-Neptunes, pressure at the atmosphere–magma boundary can reach tens of gigapascals where hydrogen is a dense liquid. A recent experiment showed that hydrogen can induce the reduction of Fe 2+ in (Mg,Fe)O to Fe 0 metal at the pressure–temperature conditions relevant to the atmosphere–interior boundary. However, it is unclear whether Mg, one of the abundant heavy elements in the planetary interiors, remains oxidized or can be reduced by H. Our experiments in the laser-heated diamond-anvil cell found that heating of MgO + Fe to 3,500 to 4,900 K (close to or above their melting temperatures) in an H medium leads to the formation of Mg 2 FeH 6 and H 2 O at 8 to 13 GPa. At 26 to 29 GPa, the behavior of the system changes, and Mg–H in an H fluid and H 2 O were detected with separate FeH x . The observations indicate the dissociation of the Mg–O bond by H and subsequent production of hydride and water. Therefore, the atmosphere–magma interaction can lead to a fundamentally different mineralogy for sub-Neptune exoplanets compared with rocky planets. The change in the chemical reaction at the higher pressures can also affect the size demographics (i.e., “radius cliff”) and the atmosphere chemistry of sub-Neptune exoplanets.

58 GEOSCIENCES↗
DOE OSTI2025

Computer-aided design of stability enhanced nicotinamide cofactor biomimetics for cell-free biocatalysis

Cell-free biocatalysis (CFB) is an efficient and environmentally friendly method to synthesize molecules such as pharmaceuticals, biochemicals, and biofuels through the in vitro use of enzyme cascades. These enzymes often require redox cofactors to drive chemical reactions. Natural redox cofactors (NAD(P)H) are expensive to isolate, motivating synthetic nicotinamide cofactor biomimetics (NCBs) as a cost-effective solution. A select handful of NCBs have been identified as potential NAD(P)H alternatives with comparable or improved redox capabilities, however, they display a tendency to degrade in common buffers. In this study, a library of 132 NCB candidates is systematically generated, over 85% of which have not been characterized in the literature, to expand the diversity of currently explored NCBs. The decomposition mechanism of NCBs in phosphate is evaluated using density functional theory (DFT), revealing protonation at the nicotinamide C5 position as a reporter of cofactor stability. Based on this result, we trained a linear regression model on DFT calculated descriptors to predict NCB stability in phosphate buffer, achieving mean absolute error (MAE) and root mean squared error (RMSE) values within computational accuracy. Analysis of key atomic descriptors and qualitative trends in our dataset informed the design of novel NCB candidates we propose with optimized stability. This work enables researchers to predict the relative stability of NCBs before synthesis, thereby streamlining the process to make CFB more affordable and viable at industry scales.

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

The MFiX Particle-in-Cell Method (MFiX-PIC) Theory Guide

MFiX (Multiphase Flow with Interphase eXchanges) is an open-source multiphase flow solver developed at the National Energy Technology Laboratory. Within the code, users have access to a single phase or interpenetrating continua-based multiphase two-fluid model (TFM), a discrete element model (DEM), and a particle-in-cell model (PIC). TFM, DEM, and PIC can all be used to create multiphase simulations that include hydrodynamics, chemical reactions, and heat transfer.

01 COAL, LIGNITE, AND PEAT↗
DOE OSTI2025

Domain Wall Reactions in Multiple-Order Parameter Ferroelectrics

The motion of domain walls is crucial for ferroelectric switching. Conventionally, the switching dynamics is believed to be determined by the motion of one or a few low-energy domain wall types of dominant population. Here, we challenge this conventional idea in multiple-order-parameter ferroelectrics. Using hafnia as example, we show that the multiple-order-parameter nature not only provides various mobile domain walls and defectlike immobile domain walls, but also enables the domain wall reactions. In analogy with chemical reactions where substances react to form new substances, domain walls could also react to form other domain walls during switching. We identify several elementary domain wall reaction types including synthesis, decomposition, and exchange reactions. Domain walls are continually changed by these reactions during switching so that the switching behavior reflects the statistical average of many domain wall types with distinct mobility and stability. These reactions also lead to phenomenon like remanent nuclei and defect-site nucleation that facilitate switching and lower coercive field. Finally, the concept of domain wall reaction is not limited to hafnia but can be generalized to any multiple-order-parameter ferroelectric. As a result, this Letter conceptualizes domain wall reaction, expands the theory of ferroelectric switching, and suggests a practical way for defect engineering to control switching behavior.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Anti-Arrhenius behavior of electron transfer reactions in molecular dimers

Rates of chemical reactions typically accelerate as the temperature rises, following the Arrhenius law. However, electron transfer reactions may exhibit weak temperature dependence or counterintuitive behavior, known as anti-Arrhenius behavior, wherein reaction rates decrease as temperature increases. Solvent reorganization energy and torsion-induced changes in electronic couplings could contribute to this unusual behavior, but how each contributes to the overall temperature dependence is unclear. One can decelerate the charge recombination process in photogenerated radical pairs or charge-separated states by harnessing this often-overlooked phenomenon. This means that we could achieve long-lived radical pairs without relying on conventional cooling. Using a series of homo molecular dimers, we showed that the degree of torsional hindrance dictates temperature-dependent torsion-induced changes in electronic coupling and, therefore, charge recombination rates. The overall temperature dependence is controlled by how changes in electronic coupling and the temperature-dependent solvent reorganization energy contribute to the rates of charge recombination. Our findings pave the way for rationally designing molecules that exhibit anti-Arrhenius behavior to slow down charge recombination, opening possibilities for applications in energy-related and quantum information technologies.

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

GeoMicro3D: A novel numerical model for simulating the reaction process and microstructure formation of alkali-activated slag

Highlights: • The GeoMicro3D model was innovatively developed for simulating the reaction process and microstructure formation of AAS. • In GeoMicro3D, the dissolution of slag and reactions of aqueous ions are fundamentally described in the local lattice cell. • A novel approach is proposed to speed up the nucleation simulations. • GeoMicro3D was implemented and verified with the relevant experimental data and thermodynamic calculation results. For the first time, this study developed a novel model, named GeoMicro3D, to simulate the reaction process and microstructure formation of alkali-activated slag. The GeoMicro3D model consists of four modules that are designed to simulate, respectively: (i) the initial spatial distribution of real-shape slag particles in alkaline activator, (ii) the dissolution of slag and diffusion of ions via the transition state theory and lattice Boltzmann method, respectively, (iii) the spatial distribution of reaction products using a nucleation probability theory, and (iv) the chemical reactions with thermodynamic modelling. Afterwards the GeoMicro3D model was implemented and verified. The simulation results were discussed and compared with the relevant experimental data and thermodynamic calculation results using GEMS. A good agreement was found in the comparisons, showing the strong simulation capability of GeoMicro3D.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Plasmon-Mediated Intramolecular Methyl Migration with Nanoscale Spatial Control

Plasmonic materials interact strongly with light to focus and enhance electromagnetic radiation down to nanoscale volumes. Due to this localized confinement, materials that support localized surface plasmon resonances are capable of driving energetically unfavorable chemical reactions. In certain cases, the plasmonic nanostructures are able to preferentially catalyze the formation of specific photoproducts, which offers an opportunity for the development of solar-driven chemical synthesis. Here, using plasmonic environments, we report inducing an intramolecular methyl migration reaction, forming 4-methylpyridine from N-methylpyridinium. Using both experimental and computational methods, we were able to confirm the identity of the N-methylpyridinium by making spectral comparisons against possible photoproducts. This reaction involves breaking a C–N bond and forming a new C–C bond, highlighting the ability of plasmonic materials to drive complex and selective reactions. Additionally, we observe that the product yield depends strongly on optical illumination conditions. This is likely due to steric hindrance in specific regions on the nanostructured plasmonic substrate, providing an optical handle for driving plasmonic catalysis with spatial specificity. Furthermore, this work adds yet another class of reactions accessible by surface plasmon excitation to the ever-growing library of plasmon-mediated chemical reactions.

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

Catalytic properties of model supported nanoparticles

Nanoparticles of late transition metals are used as catalysts and electrocatalysts for industrial chemical reactions that produce fuels, convert chemical energy to electricity and clean up pollution associated with the generation and use of these fuels. For such applications, metals are usually bonded onto the surfaces of oxide or carbon support materials. To provide the energy needed for sustained economic development, we must develop new and improved solid catalysts and electrocatalysts for a variety of reactions that take better advantage of traditional and alternate energy sources (solar, wind, biomass, etc.) and avoid serious environmental problems. Additionally, since these metals are often expensive and in limited supply (e.g., Pt), we also need to develop catalysts that do not require precious metals. As a consequence, recent years have seen a dramatic increase in research aimed at providing the basic understanding needed to develop new and improved catalysts and electrocatalysts for a variety of reactions that involve nanoparticles of late transition metals supported on oxide or carbon materials. It is therefore timely to present a special issue that focusses on Catalytic Properties of Model Supported Nanoparticles. The collection highlights some of the most exciting new directions for research in this area, and provides in depth examples of some of the most cutting-edge methods, both experimental and theoretical, for studying this class of catalysts, which provide insights into structure, mechanisms, kinetics and energetics with unprecedented accuracy and detail. Finally, as Guest Editors, we present below a few of the many excellent papers published in the collection.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

p-Type BiVO 4 for Solar O 2 Reduction to H 2 O 2

Photoelectrochemical cells (PECs) can directly utilize solar energy to drive chemical reactions to produce fuels and chemicals. Oxide-based photoelectrodes in general exhibit enhanced stability against photocorrosion, which is a critical advantage for building a sustainable PEC. However, most oxide-based semiconductors are n-type, and p-type oxides that can be used as photocathodes are limited. In this study, we report the synthesis, characterization, and application of p-type BiVO 4 with a monoclinic scheelite (ms) structure. ms-BiVO 4 is inherently n-type, and it has been investigated only as a photoanode to date. In this study, we prepared p-type ms-BiVO 4 (bandgap of 2.4 eV) via atomic doping of Ca 2+ at the Bi 3+ site under an O 2 -rich environment and examined its performance as a photocathode. We then demonstrated that the Ca-doped ms-BiVO 4 photocathode can be used for solar O 2 reduction to H 2 O 2 when coupled with appropriate catalysts. Our computational investigation using hybrid density functional theory revealed that holes are stable as polarons in ms-BiVO 4 and have a low self-trapping energy, that may lead to free carriers in the valence band at finite temperature. Our calculations also show that Ca is an effective shallow acceptor dopant with low formation energy and thermal ionization energy leading to p-type conductivity. In conclusion, our joint experimental and computational results provide critical insights into the design of p-type ms-BiVO 4 , enabling its use as a polaronic oxide photocathode.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Data-Driven Kinetic Reaction Networks for Separation Chemistry

Understanding complex, multistep chemical reactions at the molecular level is a major challenge whose solution would greatly benefit the design and optimization of numerous chemical processes. The separation of rare-earth (4f) and actinide (5f) elements is an example where improving our chemical understanding is important for designing and optimizing new chemistries, even with a limited number of observations. Here, in this work, we leverage data-driven artificial intelligence and machine-learning approaches to develop kinetic reaction networks that describe the liquid–liquid extraction mechanism of uranium using N,N-di-2-ethylhexyl-isobutyramide (DEHiBA). Specifically, we compare and contrast the properties of two classes of models: (1) purely data-driven models that are regularized using chemistry-agnostic, L1 regression and (2) chemistry-informed models that are regularized using relative reaction energies provided by quantum mechanical calculations. We observe that purely data-driven models are unbiased, simple, and accurate in their predictions of experimental measurements when provided with sufficient data but are difficult to fully constrain and interpret. In contrast, chemistry-informed models exhibit significantly improved chemical interpretability and consistency, providing a detailed description of the separation process while achieving high accuracy through ensemble averaging. Overall, the dominant species predicted to be extracted into the organic phase is UO 2 (NO 3 ) 2 (DEHiBA) 2 , agreeing with experimental slope analysis, thermodynamic modeling, EXAFS, and crystal structures. This work demonstrates that leveraging the fundamental structure of the problem can lead to efficient learning schemes that provide both accurate predictions and chemical insights at a low computational cost.

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

Alkali-silica reaction expansion model for confined concrete with stress-dependency and casting direction anisotropy

Alkali-silica reaction (ASR) is a deleterious chemical reaction between alkali hydroxyl ions and types of silica found in some aggregates of concrete. Owners and regulators of nuclear power plants aim to ensure the safety of the concrete structures with optimal maintenance strategies. A new model was developed for predicting the expansion of concrete structures affected by alkali-silica reaction. The model includes a novel combination of existing models as an alkali-silica reaction advancement model, a casting direction anisotropic expansion model, a stress-dependent anisotropic expansion model, and a material property evolution model dependent on the degree of ASR expansion. The model parameters were calibrated based on existing literature data and data generated by previous efforts of this study. The calibrated model was then validated with the experiments carried out in previous efforts of this study. The model was shown to accurately predict the ASR-expansion of large-scale reinforced concrete specimens with confinement.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Data-driven prediction of the output composition of an atmospheric pressure plasma jet

Cold atmospheric plasma (CAP) in open air hosts numerous chemical species engaged in thousands of chemical reactions. Comprehensive diagnosis of its chemical composition is important across various fields from medicine, where reactive oxygen and nitrogen play key roles, to surface modification. In applications, a centimeter-scale helium–air jet operates for minutes, featuring micrometer-sized streamers and an atmospheric pressure-induced collision frequency in the hundreds of GHz range. To address this intricate multi-scale issue, we introduce a machine learning approach: using a physics-informed neural network (PINN) to tackle the multi-scale complexities inherent in predicting the complete list of species concentrations, gas temperature, and electron temperature of a CAP jet supplied with a mixture of helium and air. Experimental measurements of O 3 , N 2 O, and NO 2 concentrations downstream of the plasma jet, combined with fundamental physics laws, the conservation of mass and charge, constrain the PINN, enabling it to predict the concentrations of all species that are not available from the experiment, along with gas and electron temperatures. The results, therefore, obey all the physical laws we provided and can have a chemical balance with the measured concentrations. In conclusion, this methodology holds promise for describing and potentially regulating complex systems with limited experimental datasets.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Epitaxial Electrodeposition of Wide Bandgap Semiconductors for Energy Conversion and Storage

The focus of this project was to electrodeposit epitaxial films and nanostructures of inorganic materials. Although electrodeposition is normally associated with the electroplating of metals, the emphasis in this work was the deposition of compound materials such as metal oxides, semiconductors, catalysts, and magnetic materials. An electrochemical/chemical approach was used, in which an electrochemical redox reaction was followed by a chemical reaction to produce the product. The materials were deposited as epitaxial thin films or nanostructures onto a single-crystal substrate that determined their in-plane and out-of-plane orientation. Epitaxial films can have enhanced properties due to the lack of grain boundaries. They can also produce catalytic materials which have enhanced reactivity due to the exposure of the most catalytically active surface. A method was developed to produce inexpensive single-crystal-like metal foils that could be used as the substrate to deposit materials for applications such as flexible electronics. It was also demonstrated that epitaxial films of inorganic materials could be produced by a simple spin-coating technique.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Intense Reactivity in Sulfur–Hydrogen Mixtures at High Pressure under X-ray Irradiation

Superconductivity near room temperature in the sulfur–hydrogen system arises from a sequence of reactions at high pressures, with X-ray diffraction experiments playing a central role in understanding these chemical–structural transformations and the corresponding S:H stoichiometry. Here we document X-ray irradiation acting as both a probe and as a driver of chemical reaction in this dense hydride system. We observe a reaction between molecular hydrogen (H 2 ) and elemental sulfur (S 8 ) under high pressure, induced directly by X-ray illumination, at photon energies of 12 keV using a free electron laser. The rapid synthesis of hydrogen sulfide (H 2 S) at 0.3 GPa was confirmed by optical observations, spectroscopic measurements, and microstructural changes detected by X-ray diffraction. These results document X-ray induced chemical synthesis of superconductor-forming dense hydrides, revealing an alternative production strategy and confirming the disruptive nature of X-ray exposure in studies on high-pressure hydrogen chalcogenides, from water to high-temperature superconductors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

Catalyst: Fast and flexible modeling of reaction networks

We introduce Catalyst.jl, a flexible and feature-filled Julia library for modeling and high-performance simulation of chemical reaction networks (CRNs). Catalyst supports simulating stochastic chemical kinetics (jump process), chemical Langevin equation (stochastic differential equation), and reaction rate equation (ordinary differential equation) representations for CRNs. Through comprehensive benchmarks, we demonstrate that Catalyst simulation runtimes are often one to two orders of magnitude faster than other popular tools. More broadly, Catalyst acts as both a domain-specific language and an intermediate representation for symbolically encoding CRN models as Julia-native objects. This enables a pipeline of symbolically specifying, analyzing, and modifying CRNs; converting Catalyst models to symbolic representations of concrete mathematical models; and generating compiled code for numerical solvers. Leveraging ModelingToolkit.jl and Symbolics.jl, Catalyst models can be analyzed, simplified, and compiled into optimized representations for use in numerical solvers. Finally, we demonstrate Catalyst’s broad extensibility and composability by highlighting how it can compose with a variety of Julia libraries, and how existing open-source biological modeling projects have extended its intermediate representation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Exploring non-adiabaticity to CO reduction reaction through ab initio molecular dynamics simulation

Non-adiabatic chemical reaction refers to the electronic excitation during reactions. This effect cannot be modeled by the ground-state Born-Oppenheimer molecular dynamics (BO-MD), where the electronic structure is at the ground state for every step of ions' movement. Although the non-adiabatic effect has been explored extensively in gas phase reactions, its role in electrochemical reactions, such as water splitting and CO 2 reduction, in electrolyte has been rarely explored. On the other hand, electrochemical reactions usually involve electron transport; thus, a non-adiabatic process can naturally play a significant role. In this work, using one-step CO 2 reduction as an example, we investigated the role of the non-adiabatic effect in the reaction. The reaction barriers were computed by adiabatic BO-MD and non-adiabatic real-time time dependent density functional theory (rt-TDDFT). We found that by including the non-adiabatic effect, rt-TDDFT could increase the reaction barrier up to 6% compared to the BO-MD calculated barrier when the solvent model is used to represent water. Simulations were carried out using explicit water molecules around the reaction site under different overpotentials, and similar non-adiabatic effects were found.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗