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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 541 records · Page 30

Near-cryogenic direct air capture using adsorbents

Direct air capture (DAC) of CO 2 is a key component in the portfolio of negative emissions technologies for mitigating global warming. However, even with the most potent amine sorbents, large-scale DAC deployment remains limited by high energy and capital costs. Recently, adsorbents relying on weak interactions with CO 2 have emerged as a potential alternative, thanks to their rapid adsorption kinetics and superior long-term stability, particularly under sub-ambient conditions (∼253 K). Despite these advantages, their use is hindered by the need for a water-removal process, location-specific constraints, and insufficient working capacity even in cold climates. In this study, we hypothesized that further reducing the adsorption temperature to a near-cryogenic range (160–220 K) could enable cost-effective DAC by utilizing the full potential of physisorbents. We primarily consider integrating DAC with a relatively untapped source of cold energy—liquified natural gas (LNG) regasification—to perform near-cryogenic DAC. From large-scale molecular simulations, Zeolite 13X and CALF-20 were identified as promising candidates. These materials were subsequently examined through experiments, including breakthrough analyses at 195 K. Their high CO 2 sorption capacity (4.5–5.5 mmol g −1 ), combined with a low desorption enthalpy and robust long-term stability, led to a threefold reduction in the levelized cost of capture (down to 68.2 USD per tonne CO 2 ). Estimates of the global LNG regasification resource suggest that LNG–DAC coupling could potentially enable the capture of 103–142 megatonnes of CO 2 annually as of 2050.

Kim, Seo-Yul [Georgia Institute of Technology, Atl↗

Discovery of hydrogen storage molecules using large language models and machine learning

Accelerating the discovery of new molecules with targeted properties is a central challenge in molecular design. In this contribution, we present an AI-driven molecular discovery framework that integrates Large Language Models (LLMs) for generative molecular design with Machine Learning (ML)-based screening to identify novel Liquid Organic Hydrogen Carrier (LOHC) candidates. Using the developed framework, LOHC molecules were systematically generated, evaluated, and refined iteratively, combining LLM-guided molecular generation and ML-predicted hydrogenation enthalpies (Δ H ), under physicochemical property constraints such as optimal melting points (MP), desired hydrogen storage capacity (wt% H 2 ), and synthetic accessibility (SA) scores. This approach enabled the discovery of 42 new LOHC candidates in two distinct campaigns, one seeded with experimentally known and another with previously computationally identified LOHCs, respectively. Although we began with different numbers of starting molecules (31 vs . 7 seed molecules), both runs yielded a comparable number of viable candidates, suggesting an influence of chemically intuitive seed molecule selection for success. Selected LOHC molecules, such as 3-methyl pyridine, 1-ethylnapthalene, 1,1-diphenylethane, and benzofuran, were experimentally tested and compared with benchmark LOHCs (toluene and 9-ethylcarbazole) for hydrogenation using a series of commercial supported metal catalysts. The order of conversion into fully hydrogenated products at 200 °C was 3-methyl pyridine (100%) > 9-ethyl carbazole (86.4%) > 2,3-benzofuran (74%) > 1,1-diphenylethane (66.9%) > 1-ethylnapthalene (66.7%) > toluene (57%), further validating the AI-guided molecular design. This study demonstrates promise of LLM-driven molecular design in conjunction with ML-based screening for accelerated discovery and design of molecules.

Harb, Hassan [Argonne National Laboratory (ANL), A↗

Dynamics in a stellar convective layer and at its boundary: Comparison of five 3D hydrodynamics codes

Our ability to predict the structure and evolution of stars is in part limited by complex, 3D hydrodynamic processes such as convective boundary mixing. Hydrodynamic simulations help us understand the dynamics of stellar convection and convective boundaries. However, the codes used to compute such simulations are usually tested on extremely simple problems and the reliability and reproducibility of their predictions for turbulent flows is unclear. We define a test problem involving turbulent convection in a plane-parallel box, which leads to mass entrainment from, and internal-wave generation in, a stably stratified layer. We compare the outputs from the codes FLASH, MUSIC, PPMSTAR, PROMPI, and SLH, which have been widely employed to study hydrodynamic problems in stellar interiors. The convection is dominated by the largest scales that fit into the simulation box. All time-averaged profiles of velocity components, fluctuation amplitudes, and fluxes of enthalpy and kinetic energy are within ≲3σ of the mean of all simulations on a given grid (128 3 and 256 3 grid cells), where σ describes the statistical variation due to the flow’s time dependence. They also agree well with a 512 3 reference run. The 128 3 and 256 3 simulations agree within 9% and 4%, respectively, on the total mass entrained into the convective layer. The entrainment rate appears to be set by the amount of energy that can be converted to work in our setup and details of the small-scale flows in the boundary layer seem to be largely irrelevant. Our results lend credence to hydrodynamic simulations of flows in stellar interiors. We provide in electronic form all outputs of our simulations as well as all information needed to reproduce or extend our study.

79 ASTRONOMY AND ASTROPHYSICS↗

Investigation of flow regimes in arc plasma–gas interactions using a two-temperature arc in crossflow model

The perpendicular impingement of a gas stream on an electric arc, a configuration known as the arc in crossflow, is of primary relevance in the study of plasma-gas interactions, as well as in industrial applications such as circuit breakers and wire-arc spraying. The flow dynamics in the arc in crossflow are the result of coupled fluid-thermal-electromagnetic phenomena accompanied by large property gradients, which can produce significant deviations from Local Thermodynamic Equilibrium (LTE) among electrons and gas species. These characteristics can lead to the establishment of distinct flow regimes depending on the relative values of the controlling parameters of the system, such as inflow velocity, arc current, and inter-electrode spacing. A two-temperature non-LTE (NLTE) model is used to investigate the arc dynamics and the establishment of flow regimes in the arc in crossflow. The plasma flow model is implemented within a nonlinear Variational Multiscale (VMS) numerical discretization approach that is less dissipative, and hence better suited to capture unstable behavior, than traditional VMS methods commonly used in computational fluid dynamics simulations. The Reynolds and the Enthalpy dimensionless numbers, characterizing the relative flow strength and arc strength, respectively, are chosen as the controlling parameters of the system. Simulation results reveal the onset of dynamic behavior and the establishment of steady, periodic, quasi-periodic, and chaotic or potentially turbulent, regimes, as identified by distinct spatiotemporal fluctuations. The computational findings reveal the role of increasing the relative arc strength on enhancing flow stability by delaying the growth of fluctuating and unstable flow behavior.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

Pressure-induced transformation of Nb 2 O 5 under shock compression from first principles

Ab initio molecular dynamics (AIMD) simulations were carried out to investigate the equation of state of Nb 2 O 5 and its pressure-density relationship under shock conditions. Here, the focus of this study is on the monoclinic B-Nb 2 O 5 (C2/c) polymorph. Enthalpy calculations from AIMD trajectories at 300 K show that the pressure-induced transformation between the thermody-namically most stable crystalline monoclinic parent phase H-Nb 2 O 5 (P2/m) and B-Nb 2 O 5 occurs at ~1.9 GPa. This H→B transition is energetically more favorable than the H→L(Pmm2) pressure-induced transition recently observed at ~5.9-9.0 GPa. The predicted shock properties of Nb 2 O 5 polymorphs are also compared to their Nb and NbO 2 counterparts to assess the impact of niobium oxidation on shock response.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

Intrinsic and extrinsic conduction contributions at nominally neutral domain walls in hexagonal manganites

Conductive and electrostatic atomic force microscopy (cAFM and EFM) are used to investigate the electric conduction at nominally neutral domain walls in hexagonal manganites. The EFM measurements reveal a propensity of mobile charge carriers to accumulate at the nominally neutral domain walls in ErMnO3, which is corroborated by cAFM scans showing locally enhanced direct current conductance. Our findings are explained based on the established segregation enthalpy profiles for oxygen vacancies and interstitials, providing a microscopic model for previous, seemingly disconnected observations ranging from insulating to conducting behavior. In addition, we observe variations in conductance between different nominally neutral walls that we attribute to deviations from the ideal charge-neutral structure within the bulk, leading to a superposition of extrinsic and intrinsic contributions. Our study clarifies the complex transport properties at nominally neutral domain walls in hexagonal manganites and establishes the possibility to tune their electronic response based on oxidation conditions, opening the door for domain-wall-based sensor technology.

Schultheiß, J. (ORCID:0000000173891295)↗

Bond order predictions using deep neural networks

Machine learning is an extremely powerful tool for the modern theoretical chemist since it provides a method for bypassing costly algorithms for solving the Schrödinger equation. Already, it has proven able to infer molecular and atomic properties such as charges, enthalpies, dipoles, excited state energies, and others. Most of these machine learning algorithms proceed by inferring properties of individual atoms, even breaking down total molecular energy into individual atomic contributions. In this paper, we introduce a modified version of the Hierarchically Interacting Particle Neural Network (HIP-NN) capable of making predictions on the bonds between atoms rather than on the atoms themselves. We train the modified HIP-NN to infer bond orders for a large number of small organic molecules as computed via the Natural Bond Orbital package. We demonstrate that the trained model is extensible to molecules much larger than those in the training set by studying its performance on the COMP6 dataset. This method has applications in cheminformatics and force field parameterization and opens a promising future for machine learning models to predict other quantities that are defined between atoms such as density matrix elements, Hamiltonian parameters, and molecular reactivities.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Consequences of sp 2 –sp 3 boron isomerization in supercooled liquid borates

In this study, time-resolved high-energy synchrotron x-ray total scattering measurements on supercooled molten lithium metaborate (LiBO 2 ) reveal an isomerization reaction involving conversion of trigonal sp 2 boron to tetrahedral sp 3 boron during quenching and glass formation. Van't Hoff analysis yields an accurate enthalpy change, ΔH = 21(1) kJ mol –1 boron, from which we develop an analytical model for the sp 3 isomer fraction and its contribution to configurational heat capacity (C p conf ) and entropy as a function of temperature and composition. Isomerization constitutes 40% of the total calorimetric C p conf at the glass transition for LiBO 2 and directly contributes to the observed rise in liquid fragility with the lithium content.

74 ATOMIC AND MOLECULAR PHYSICS↗

First-principles study and experimental characterization of metal incorporation in germanium telluride

Germanium telluride is a well-known phase change material (PCM) used in non-volatile memory cells and radio frequency switches. Controlling the properties of GeTe for improved PCM device performance has sometimes been achieved by doping and/or alloying with metals, often at concentrations greater than 10 at. % and using non-equilibrium methods. Here, since switching PCMs between the low-resistance crystalline and high-resistance amorphous states requires a heating cycle, the stability of metal-incorporated GeTe (Ge 0.5–x M x Te 0.5 ) films is also critical to practical implementation of these materials in electronic and optoelectronic devices. In this work, we use both density-functional theory and experimental characterization methods to probe the solubility and critical properties of Ge 0.5–x M x Te 0.5 films. Using first-principles calculations, we determine the enthalpy of formation for GeTe with 2.08, 4.17, and 6.25 at. % of Cu, Fe, Mn, Mo, and Ti and show trends between the stability of the Ge 0.5–x M x Te 0.5 systems and the atomic position, composition, and distribution of the metal atoms in the GeTe matrix.

36 MATERIALS SCIENCE↗

Ab initio and force field molecular dynamics study of bulk organophosphorus and organochlorine liquid structures

Here, we performed ab initio molecular dynamics (AIMD) simulations to benchmark bulk liquid structures and to evaluate results from all-atom force field molecular dynamics (FFMD) simulations with the generalized Amber force field (GAFF) for organophosphorus (OP) and organochlorine (OC) compounds. Our work also addresses the current and important topic of force field validation, applied here to a set of nonaqueous organic liquids. Our approach differs from standard treatments, which validate force fields based on thermodynamic data. Utilizing radial distribution functions (RDFs), our results show that GAFF reproduces the AIMD-predicted asymmetric liquid structures moderately well for OP compounds that contain bulky alkyl groups. Among the OCs, RDFs obtained from FFMD overlap well with AIMD results, with some offsets in position and peak structuring. However, re-parameterization of GAFF for some OCs is needed to reproduce fully the liquid structures predicted by AIMD. The offsets between AIMD and FFMD peak positions suggest inconsistencies in the developed force fields, but, in general, GAFF is able to capture short-ranged and long-ranged interactions of OPs and OCs observed in AIMD. Along with the local coordination structure, we also compared enthalpies of vaporization. Overall, calculated bulk properties from FFMD compared reasonably well with experimental values, suggesting that small improvements within the FF should focus on parameters that adjust the bulk liquid structures of these compounds.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

The Li–F–H ternary system at high pressures

Evolutionary crystal structure prediction searches have been employed to explore the ternary Li-F-H system at 300 GPa. Metastable phases were uncovered within the static lattice approximation, with LiF 3 H 2 , LiF 2 H, Li 3 F 4 H, LiF 4 H 4 , Li 2 F 3 H and LiF 3 H lying within 50 meV/atom of the 0 K convex hull. All of these phases contain H n F¯ n+1 (n = 1; 2) anions, and Li + cations. Other structural motifs such as LiF slabs, $H$ $^{+}_{3}$ molecules and F δ- ions are present in some of the low enthalpy Li-F-H structures. The bonding within the H n F¯ n+1 molecules, which may be bent or linear, symmetric or asymmetric, is analyzed. The five phases closest to the hull are insulators, while LiF 3 H is metallic and predicted to have a vanishingly small superconducting critical temperature. Li 3 F 4 H is predicted to be stable at zero pressure. Furthermore, this study lays the foundation for future investigations of the role of temperature and anharmonicity on the stability and properties of compounds and alloys in the Li-F-H ternary system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Vapor–liquid equilibrium of water with the MB-pol many-body potential

Among the many existing molecular models of water, the MB-pol many-body potential has emerged as a remarkably accurate model, capable of reproducing thermodynamic, structural, and dynamic properties across water's solid, liquid, and vapor phases. In this work, we assessed the performance of MB-pol with respect to an important set of properties related to vapor-liquid coexistence and interfacial behavior. Through direct coexistence classical molecular dynamics simulations at temperatures of 400 K < T < 600 K, we calculated properties such as equilibrium coexistence densities, vapor-liquid interfacial tension, vapor pressure, and enthalpy of vaporization and compared the MB-pol results to experimental data. We also compared rigid vs fully flexible variants of the MB-pol model and evaluated system size effects for the properties studied. We found that the MB-pol model predictions are in good agreement with experimental data, even for temperatures approaching the vapor-liquid critical point; this agreement was largely insensitive to system sizes or the rigid vs flexible treatment of the intramolecular degrees of freedom. Furthermore, these results attest to the chemical accuracy of MB-pol and its high degree of transferability, thus enabling MB-pol's application across a large swath of water's phase diagram.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

A chemical kinetics perspective on thermoelectric transport

There is a growing community of chemists and materials scientists exploring thermoelectric properties of organic, polymeric, hybrid, and composite materials. Many of these materials are nondegenerate semiconductors, meaning that the Fermi and transport charge transport energy levels are significantly offset. Others are ionic conductors. While the meaning of the essential parameters electrical conductivity (σ) and Seebeck coefficient (S, the voltage difference per degree of temperature difference) are accessible, the origins of S are not readily apparent to one trained mainly in chemistry. The purpose of this manuscript is to illustrate S as being the result of a steady state difference between concentrations of two designations of charge carriers, those on hotter and those on colder sides of a material sample, analogous to the equilibrium among interconverting chemical species that differ in enthalpy and entropy. The ion-based Seebeck coefficient, also known as the Soret effect, can be explained using principles similar to those applicable to electrons and holes. We hope that this analysis leads to wider understanding of the origins of S through an explanation using the language of chemical kinetics and thermodynamics, and appreciation of ways that thermoelectric efficiency can be enhanced in emerging materials.

36 MATERIALS SCIENCE↗

Electronic states and transitions of PrO and PrO + probed by threshold ionization spectroscopy and spin–orbit multiconfiguration perturbation theory

The precise ionization energy of praseodymium oxide (PrO) seeded in supersonic molecular beams is measured with mass-analyzed threshold ionization (MATI) spectroscopy. Here, a total of 33 spin–orbit (SO) states of PrO and 23 SO states of PrO + are predicted by second-order multiconfigurational quasi-degenerate perturbation (MCQDPT2) theory. Electronic transitions from four low-energy SO levels of the neutral molecule to the ground state of the singly charged cation are identified by combining the MATI spectroscopic measurements with the MCQDPT2 calculations. The precise ionization energy is used to reassess the ionization energies and the reaction enthalpies of the Pr + O → PrO + + e – chemi-ionization reaction reported in the literature. An empirical formula that uses atomic electronic parameters is proposed to predict the ionization energies of lanthanide monoxides, and the empirical calculations match well with available precise experimental measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Influence of mass ablation on ignition and burn propagation in layered fusion capsules

After decades of research, recent laser-driven inertial fusion experiments have demonstrated rapid progress toward achieving thermonuclear ignition using capsule designs with cryogenic fuel layers. The ignition physics for these layered capsules involves a complex interplay between the dynamically forming hot spot and the dense surrounding fuel. Using analytic theory and numerical simulations, we demonstrate that the mass ablation rate into the hot spot depends sensitively upon the temperature of the dense fuel, resulting in ablative inflows up to [Formula: see text] faster than previous estimates. This produces an enthalpy flux into the hot spot that plays a critical role in controlling the hot spot temperature, the ignition threshold, and the subsequent burn propagation. The net influence of mass ablation on the ignition threshold is regulated by a dimensionless parameter that depends upon the temperature of the dense fuel. As a consequence, the ignition threshold is sensitive to any mechanism that heats the dense fuel, such as neutrons or radiation emitted from the hot spot. These predictions are confirmed using radiation-hydrodynamic simulations for a series of capsules near ignition conditions. This analysis may have relevance for understanding the variable performance of recent experiments and for guiding new capsule designs toward higher fusion yields.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

A Deep Potential model for liquid–vapor equilibrium and cavitation rates of water

Computational studies of liquid water and its phase transition into vapor have traditionally been performed using classical water models. Here, we utilize the Deep Potential methodology—a machine learning approach—to study this ubiquitous phase transition, starting from the phase diagram in the liquid–vapor coexistence regime. The machine learning model is trained on ab initio energies and forces based on the SCAN density functional, which has been previously shown to reproduce solid phases and other properties of water. Here, we compute the surface tension, saturation pressure, and enthalpy of vaporization for a range of temperatures spanning from 300 to 600 K and evaluate the Deep Potential model performance against experimental results and the semiempirical TIP4P/2005 classical model. Moreover, by employing the seeding technique, we evaluate the free energy barrier and nucleation rate at negative pressures for the isotherm of 296.4 K. Further, we find that the nucleation rates obtained from the Deep Potential model deviate from those computed for the TIP4P/2005 water model due to an underestimation in the surface tension from the Deep Potential model. From analysis of the seeding simulations, we also evaluate the Tolman length for the Deep Potential water model, which is (0.091 ± 0.008) nm at 296.4 K. Finally, we identify that water molecules display a preferential orientation in the liquid–vapor interface, in which H atoms tend to point toward the vapor phase to maximize the enthalpic gain of interfacial molecules. We find that this behavior is more pronounced for planar interfaces than for the curved interfaces in bubbles. This work represents the first application of Deep Potential models to the study of liquid–vapor coexistence and water cavitation.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Boron coordination change in barium borate melts and glasses and its contribution to configurational heat capacity, entropy, and fragility

High-energy x-ray diffraction from molten and glassy BaB 2 O 4 and BaB 4 O 7 has been performed using aerodynamic levitation and laser heating over a wide range of temperatures. Remarkably, even in the presence of a heavy metal modifier dominating x-ray scattering, it was possible to extract accurate values for the tetrahedral, sp 3 , boron fraction, N 4 , which declines with increasing temperature, using bond valence-based mapping from the measured mean B–O bond lengths while accounting for vibrational thermal expansion. These are used within a boron-coordination-change model to extract enthalpies, ΔH, and entropies, ΔS, of isomerization between sp 2 and sp 3 boron. The results for BaB 4 O 7 , ΔH = 22(3) kJ mol -1 boron, ΔS = 19(2) J mol -1 boron K -1 , agree quantitatively with those found previously for Na 2 B 4 O 7 . Analytical expressions for N 4 (J, T) and associated configurational heat capacity, $C_{P}^{conf}$(J, T), and entropy, S conf (J, T), contributions are extended to cover a wide composition range 0 ≤ J = BaO/B 2 O 3 ≤ 3 using a model for ΔH(J) and ΔS(J) derived empirically for lithium borates. Maxima in the $C_{P}^{conf}$(J, T g ) and fragility index contributions are thereby predicted for J ≲ 1, higher than the maximum observed and predicted in N 4 (J, T g ) at J ≃ 0.6. We discuss the utility of the boron-coordination-change isomerization model in the context of borate liquids containing other modifiers and the prospect of neutron diffraction to aid in empirical determinations of modifier-dependent effects, illustrated by new neutron diffraction data on Ba 11 B 4 O 7 glass, its well-known α-polymorph, and lesser-known δ-phase.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Predicted thermophysical properties of UN, PuN, and (U,Pu)N

Molecular dynamics and density functional theory simulations are used to predict the lattice and electronic contributions of thermophysical properties for UN, PuN, and mixed (U,Pu)N systems. The properties predicted include the lattice parameter, linear thermal expansion, enthalpy, and specific heat capacity, as a function of temperature. The simulation predictions for high temperature specific heat capacity are compared against experimental measurements to understand the behavior, and why differences in the experimental measurements are observed. The influence of adding U vacancies, N interstitials, and Pu to UN is also examined. For this, a new PuN potential parameter set is developed and used with the Kocevski UN potential, enabling the dynamics of mixed (U,Pu)N systems to be studied. How defects impact the thermophysical properties is important for understanding fuel behavior under different reactor conditions, and these mechanistic predictions can be used to support fuel performance codes where data is scarce.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗