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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Helfrich-Hurault elastic instabilities driven by geometrical frustration

Here, the Helfrich-Hurault (HH) elastic instability is a well-known mechanism behind patterns that form as a result of strain upon liquid crystal systems with periodic ground states. In the HH model, layered structures undulate and buckle in response to local, geometric incompatibilities in order to maintain the preferred layer spacing. Classic HH systems include cholesteric liquid crystals under electromagnetic field distortions and smectic liquid crystals under mechanical strains, where both materials are confined between rigid substrates. However, richer phenomena are observed when undulation instabilities occur in the presence of deformable interfaces and variable boundary conditions. Understanding how the HH instability is affected by deformable surfaces is imperative for applying the instability to a broader range of materials. In this review, the HH mechanism is reexamined and special focus is given to how the boundary conditions influence the response of lamellar systems to geometrical frustration. Lamellar liquid crystals confined within a spherical shell geometry are used as the model system. Made possible by the relatively recent advances in microfluidics within the past 15 years, liquid crystal shells are composed entirely of fluid interfaces and have boundary conditions that can be dynamically controlled at will. Past and recent work that exemplifies how topological constraints, molecular anchoring conditions, and boundary curvature can trigger the HH mechanism in liquid crystals with periodic ground states is examined. The review ends by identifying similar phenomena across a wide variety of materials, both biological and synthetic. The fact that the HH mechanism is a generic and often overlooked response of periodic materials to geometrical frustration is highlighted.

42 ENGINEERING↗
DOE OSTI2026

The Use of Machine Learning Models for Predicting the Dielectric Strength of Gases

Technological advancements in high voltage systems have pushed sulfur hexafluoride (SF6) to its operational limits. Furthermore, this gas has other drawbacks including a high liquefaction temperature and a high global warming potential. Therefore, there has been an urgent need to find alternative gases with high dielectric strength (DS). In this work, density functional theory (DFT) is used to calculate molecular descriptors that are fed into an artificial neural network (ANN) and a random forest (RF). These machine learning (ML) models are then used to predict the DS for hundreds of molecules. A finite element model (FEM) is also used to calculate the electric field profile of multiple simple electrode geometries as the applied voltage to the system is increased. Results indicate that the random forest model has better generalization to unseen data than the neural network. The highest DS value predicted by the RF was 2.16 relative to the experimental DS of SF6. The results also demonstrate how choosing a gas with a higher DS and a geometry with minimal edges and corners can significantly increase the operating voltage of an electrical system. Due to its superior generalization, the RF represents the most promising path toward an accurate DS predictor once sufficient experimental data are available.

Mileski, Matthew [AFIT]↗
DOE OSTI2020

Synthesis and structural characterization of bis (2-pyridylthio)( p -tolylthio)methyl zinc complexes and the catalytic hydrosilylation of CO 2

Bis(2-pyridylthio)(p-tolylthio)methane, [Bptm STol ]H, has been synthesized by treatment of a 2:1 mixture of 2-mercaptopyridine and p-thiocresol with NaH, followed by addition of CHI3. Access to zinc complexes is provided by the reaction of [Bptm STol ]H with Zn[N(SiMe 3 ) 2 ] 2 to afford [Bptm STol ]ZnN(SiMe 3 ) 2 , from which the halide derivatives [Bptm STol ]ZnX (X = Cl, Br, I) are obtained via reaction with Me 3 SiX (X = Cl, Br, I), and the formate complex, [Bptm STol ]ZnO 2 CH, is obtained by treatment with CO 2 in the presence of PhSiH 3 . Here, the molecular structures of [Bptm STol ]ZnN(SiMe 3 ) 2 , [Bptm STol ]ZnX (X = Cl, Br, I) and [Bptm STol ]ZnO 2 CH have been determined by single crystal X-ray diffraction, which indicate that the four coordinate zinc centers adopt structures that are intermediate between idealized trigonal monopyramidal and seesaw geometries. [Bptm STol ]ZnO 2 CH is a catalyst for the hydrosilylation of CO 2 by (RO) 3 SiH at room temperature to afford the silyl formate, HCO 2 Si(OR) 3 .

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

Crystal structure of a seven-substitution mutant of hydroxynitrile lyase from rubber tree

The α/β-hydrolase fold superfamily includes esterases and hydroxynitrile lyases which, despite catalyzing different reactions, share a Ser–His–Asp catalytic triad. We report a 1.99 Å resolution crystal structure of HNL6V, an engineered variant of hydroxynitrile lyase fromHevea brasiliensis(HbHNL) containing seven amino-acid substitutions (T11G, E79H, C81L, H103V, N104A, G176S and K236M). The structure reveals that HNL6V maintains the characteristic α/β-hydrolase fold while exhibiting systematic shifts in backbone and catalytic atom positions. Compared with wild-typeHbHNL, the C α positions in HNL6V differ by a mean of 0.2 ± 0.1 Å, representing a statistically significant displacement. Importantly, the catalytic triad and oxyanion-hole atoms have moved 0.2–0.8 Å closer to their corresponding positions in SABP2, although they remain 0.3–1.1 Å from fully achieving the configuration of SABP2. The substitutions also increase local flexibility, particularly in the lid domain covering the active site. This structural characterization demonstrates that targeted amino-acid substitutions can systematically shift catalytic geometries towards those of evolutionarily related enzymes.

Biochemistry & Molecular Biology↗
DOE OSTI2024

Ultrafast studies of elusive chemical reactions in the gas phase

The chemical composition of the interstellar medium and planetary atmospheres is constantly in flux as atoms and molecules collide and interact with high-energy particles such as electrons, protons, and photons. These transformative processes ultimately lead to the coalescence of molecules and eventually the birth of stars. Our understanding of these chemical ecosystems relies on models that synthesize data from gas-phase experiments, providing insights into reaction cross sections. This Review examines efforts to delve into the fundamental bond-forming and bond-breaking dynamics that occur during bimolecular and electron-initiated reactions. Furthermore, these experiments involve clever approaches to establish a time reference and the collision geometry necessary for tracking atomic motion with femtosecond time resolution. Findings from these efforts enhance present models and improve predictions for molecule-molecule and electron-molecule collisions.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2020

FiXR : a framework to reconstruct fiber cross-sections from X-ray fiber diffraction experiments

Ab initio reconstruction methods have revolutionized the capabilities of small-angle X-ray scattering (SAXS), allowing the data-driven discovery of previously unknown molecular conformations, exploiting optimization heuristics and assumptions behind the composition of globular molecules. While these methods have been successful for the analysis of small particles, their impact on fibrillar assemblies has been more limited. The micrometre-range size of these assemblies and the complex interaction of their periodicities in their scattering profiles indicate that the discovery of fibril structures from SAXS measurements requires novel approaches beyond extending existing tools for molecular discovery. In this work, it is proposed to use SAXS measurements, together with diffraction theory, to infer the electron distribution of the average cross-section of a fiber. This cross-section is modeled as a discrete electron density with continuous support, allowing representations beyond binary distributions. Additional constraints, such as non-negativity or smoothness/connectedness, can also be added to the framework. The proposed approach is tested using simulated SAXS data from amyloid β fibril models and using measured data of Tobacco mosaic virus from SAXS experiments, recovering the geometry and density of the cross-sections in all cases. The approach is further tested by analyzing SAXS data from different amyloid β fibril assemblies, with results that are in agreement with previously proposed models from cryo-EM measurements. The limitations of the proposed method, together with an analysis of the robustness of the method and the combination with different experimental sources, are also discussed.

Biochemistry & Molecular Biology↗
DOE OSTI2020

Implementation and assessment of an extended hydrogenic molecular model in UEDGE

Abstract Two sets of pure deuterium plasmas are simulated using UEDGE, one including atoms and molecules and the other including atoms only as separate fluid species. Simulation results in one and two dimensions are reported to assess the role of molecules in tokamak plasmas. It is shown that thermal coupling of the molecules to the plasma can be relevant for the onset of detachment under highly collisional conditions in simple geometries, but play a modest role at low collisionality. Ion‐molecule equipartition presents an additional ion energy loss channel, dissipating ion energy that would otherwise heat the electrons by equipartition. The resulting reduced electron temperature increases the ion‐electron recombination by an order of magnitude as the plasma density is increased, yielding earlier and deeper detachment.

Holm, A.↗
DOE OSTI2022

Bridging microscopy with molecular dynamics and quantum simulations: an atomAI based pipeline

Recent advances in (scanning) transmission electron microscopy have enabled a routine generation of large volumes of high-veracity structural data on 2D and 3D materials, naturally offering the challenge of using these as starting inputs for atomistic simulations. In this fashion, the theory will address experimentally emerging structures, as opposed to the full range of theoretically possible atomic configurations. However, this challenge is highly nontrivial due to the extreme disparity between intrinsic timescales accessible to modern simulations and microscopy, as well as latencies of microscopy and simulations per se. Addressing this issue requires as a first step bridging the instrumental data flow and physics-based simulation environment, to enable the selection of regions of interest and exploring them using physical simulations. Here we report the development of the machine learning workflow that directly bridges the instrument data stream into Python-based molecular dynamics and density functional theory environments using pre-trained neural networks to convert imaging data to physical descriptors. Additionally, the pathways to ensure structural stability and compensate for the observational biases universally present in the data are identified in the workflow. This approach is used for a graphene system to reconstruct optimized geometry and simulate temperature-dependent dynamics including adsorption of Cr as an ad-atom and graphene healing effects. However, it is universal and can be used for other material systems.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Clarifying the definition of ‘transonic’ screw dislocations

We report a number of recent Molecular Dynamics (MD) simulations have demonstrated that screw dislocations in face centred cubic (fcc) metals can achieve stable steady state motion above the lowest shear wave speed (v shear ) which is parallel to their direction of motion (often referred to as transonic motion). This is in direct contrast to classical continuum analyses which predict a divergence in the elastic energy of the host material at a crystal geometry dependent ‘critical’ velocity v crit . Within this work, we first demonstrate through analytic analyses that the elastic energy of the host material diverges at a dislocation velocity (v crit ) which is greater than v shear , i.e. v crit >v shear . We argue that it is this latter derived velocity (v crit ) which separates ‘subsonic’ and ‘supersonic’ regimes of dislocation motion in the analytic solution. In addition to our analyses, we also present a comprehensive suite of MD simulation results of steady state screw dislocation motion for a range of stresses and several cubic metals at both cryogenic and room temperatures. At room temperature, both our independent MD simulations and the earlier works find stable screw dislocation motion only below our derived v crit . Nonetheless, in real-world polycrystalline materials v crit cannot be interpreted as a hard limit for subsonic dislocation motion. In fact, at very low temperatures our MD simulations of Cu at 10 Kelvin confirm a recent claim in the literature that true ‘supersonic’ screw dislocations with dislocation velocities v>v crit are possible at very low temperatures.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2024

Mild-Temperature Supercritical Water Confined in Hydrophobic Metal–Organic Frameworks

Fluids under extreme confinement show characteristics significantly different from those of their bulk counterpart. This work focuses on water confined within the complex cavities of highly hydrophobic metal–organic frameworks (MOFs) at high pressures. A combination of high-pressure intrusion–extrusion experiments with molecular dynamic simulations and synchrotron data reveals that supercritical transition for MOF-confined water takes place at a much lower temperature than in bulk water, ~250 K below the reference values. This large shifting of the critical temperature (T c ) is attributed to the very large density of confined water vapor in the peculiar geometry and chemistry of the cavities of Cu 2 tebpz (tebpz = 3,3',5,5'-tetraethyl-4,4'-bipyrazolate) hydrophobic MOF. This is the first time the shift of Tc is investigated for water confined within highly hydrophobic nanoporous materials, which explains why such a large reduction of the critical temperature was never reported before, neither experimentally nor computationally.

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

Enhancement of terahertz fields in LiTaO 3 waveguides using a conical pulse front

The development of methods for the generation of strong ultrafast electromagnetic pulses in the terahertz (THz) spectral range has led to a surge of progress in nonlinear THz spectroscopy and THz control of molecular and collective responses. For spectroscopy in the 1-THz range, the submillimeter wavelengths and associated large spot sizes, large optical elements, and short distances between final focusing elements and samples can lead to cumbersome experimental setups that are incompatible with some sample environments. Here, we introduce a novel terahertz ring excitation (TREx) optical pumping geometry to generate superposing, focusing fields in planar THz waveguides made out of the electro-optic material lithium tantalate. High THz fields, >175 kV/cm, are generated and measured optically with no free-space THz propagation. The field level achieved by pumping with a sequence of concentric rings of excitation light exceeds by about 20× the result of a single cylindrically focused line of pump light that has been used routinely in previous work. The technique opens new prospects for compact waveguide-based linear and nonlinear THz spectroscopy and signal processing.

47 OTHER INSTRUMENTATION↗
DOE OSTI2023

Phase-Field Modeling of Materials Interfaces and Nanostructures

Nanostructured materials offer unique properties for a wide range of energy applications. Among various known processing methods, liquid metal dealloying (LMD)—the selective dissolution of a base alloy element into a metallic melt—has emerged as a powerful technique to produce a new class of nano-/meso-scale open porous and bicontinuous composite structures with ultra-high interfacial area. LMD has recently been complemented by the advent of vapor phase dealloying (VPD), a novel technique that exploits the selective evaporation of one element from a parent alloy containing elements with very different vapor pressures, thereby enabling to fabricate open nanoporous structures of various elements from less-noble metals to inorganic elements regardless of their chemical activity without requiring high LMD temperatures and chemical etching. Together LMD and VPD have greatly expanded the scope of dealloying techniques, limited by traditional electrochemical means to noble metals, and boosted the design of new functional and structural materials that combine a wide variety of elements. Topologically-connected open porous structures with ultra-high surface area allow mass transport within the structure while preserving structural integrity, enabling them to serve as catalysts, fuel cells, supercapacitors, or high-capacity battery materials. Bicontinuous composite structures in turn can display high strength and high ductility or superior radiation-damage resistance due to the ultra-high interface area between interpenetrating solid phases. This research program makes use of state-of-the-art computational methods to understand at a basic level the self-organizing dealloying process with main focus on dealloying kinetics and interfacial pattern formation at the dealloying front controlling initial structure size, topology, and phase compositions. Phase-field simulation studies of LMD focus on solid solutions, line compounds, and intermetallic systems that can form ternary composites by nucleation and growth of a new phase. Studies of VPD employ phase-field modeling and a hybrid method combining a kinetic Monte Carlo (KMC) model of evaporation and surface diffusion with molecular dynamics for vapor-phase transport inside nanopores. Simulations explore mechanisms of interface- and diffusion-controlled dealloying kinetics, both observed in VPD but not fundamentally understood. In addition, our newly developed multi-physics phase-field approach of large-volume-change phase transformations is being used to model novel 3D anode geometries based on dealloyed nanoporous structures including novel sandwiched graphene/Si/silica for highrate Li ion battery. Those studies are aimed at elucidating geometric design principles that improve mechanical stability. We expect this research to enhance the capability to tailor nano-/mesoscale structures for a wide range of energy-related materials applications and to yield further advances in computational methodologies that benefit a broad materials research community.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2023

Exploring the Structural, Dynamic, and Functional Properties of Metal–Organic Frameworks through Molecular Modeling

This review spotlights the role of atomic-level modeling in research on metal-organic frameworks (MOFs), especially the key methodologies of density functional theory (DFT), Monte Carlo (MC) simulations, and molecular dynamics (MD) simulations. The discussion focuses on how periodic and cluster-based DFT calculations can provide novel insights into MOF properties, with a focus on predicting structural transformations, understanding thermodynamic properties and catalysis, and providing information or properties that are fed into classical simulations such as force field parameters or partial charges. Classical simulation methods, highlighting force field selection, databases of MOFs for high-throughput screening, and the synergistic nature of MC and MD simulations, are described. By predicting equilibrium thermodynamic and dynamic properties, these methods offer a wide perspective on MOF behavior and mechanisms. Additionally, the incorporation of machine learning (ML) techniques into quantum and classical simulations is discussed. These methods can enhance accuracy, expedite simulation setup, reduce computational costs, as well as predict key parameters, optimize geometries, and estimate MOF stability. By charting the growth and promise of computational research in the MOF field, the aim is to provide insights and recommendations to facilitate the incorporation of computational modeling more broadly into MOF research.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Deep Minima in the Triply Differential Cross Section for Ionization of Atomic Hydrogen by Electron and Positron Impact

We investigate ionization of atomic hydrogen by electron- and positron-impact. We apply the Coulomb–Born (CB1) approximation, various modified CB1 approximations, the three body distorted wave (3DW) approximation, and the time-dependent close-coupling (TDCC) method to electron-impact ionization of hydrogen. For electron-impact ionization of hydrogen for an incident energy of approximately 76.45 eV, we obtain a deep minimum in the CB1 triply differential cross section (TDCS). However, the TDCC for 74.45 eV and the 3DW for 74.46 eV gave a dip in the TDCS. For positron-hydrogen ionization (breakup) we apply the CB1 approximation and a modified CB1 approximation. We obtain a deep minimum in the TDCS and a zero in the CB1 transition matrix element for an incident energy of 100 eV with a gun angle of 56.13 °. Corresponding to a zero in the CB1 transition matrix element, there is a vortex in the velocity field associated with this element. For both electron- and positron-impact ionization of hydrogen the velocity field rotates in the same direction, which is anticlockwise. All calculations are performed for a doubly symmetric geometry; the electron-impact ionization is in-plane and the positron-impact ionization is out-of-plane.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2021

Machine Learning Self-Diffusion Prediction for Lennard-Jones Fluids in Pores

Predicting the diffusion coefficient of fluids under nanoconfinement is important for many applications including the extraction of shale gas from kerogen and product turnover in porous catalysts. Due to the large number of important variables, including pore shape and size, fluid temperature and density, and the fluid–wall interaction strength, simulating diffusion coefficients using molecular dynamics (MD) in a systematic study could prove to be prohibitively expensive. Here, we use machine learning models trained on a subset of MD data to predict the self-diffusion coefficients of Lennard-Jones fluids in pores. Our MD data set contains 2280 simulations of ideal slit pore, cylindrical pore, and hexagonal pore geometries. We use the forward feature selection method to determine the most useful features (i.e., descriptors) for developing an artificial neutral network (ANN) model with an emphasis on easily acquired features. Our model shows good predictive ability with a coefficient of determination (i.e., R 2 ) of ~0.99 and a mean squared error of ~2.9 × 10 –5 . Finally, we propose an alteration to our feature set that will allow the ANN model to be applied to nonideal pore geometries.

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

Confinement effects on the solvation structure of solvated alkaline metal cations in a single-digit 1T-MoS 2 nanochannel: A first-principles study

Confinement plays an important role in determining ion transport in porous materials, which, in turn, may influence the performance of many energy storage and desalination devices. In this work, we combined density functional theory (DFT) with an implicit solvation model and ab initio molecular dynamics (AIMD) to investigate the effects of nanoconfinement on several solvated alkaline metal cations in a single-digit 1T-MoS 2 nanochannel. Our DFT calculations with a solvation model indicated that cations with stronger hydration energy introduce a higher number of co-intercalated water molecules into the channel, consistent with early experimental observation obtained for MXene (2D transition metal carbide) channels. The predicted optimal water numbers for the cations were then used for AIMD simulations that explicitly include the effects of the solvent. When compared with the cations in bulk solution, our simulations showed that the hydration structure and coordination number (CN) of the solvated cations confined in the MoS 2 channel can be significantly altered. Further, we found that larger cations with weaker hydration energy (K + , Rb + , and Cs + ) exhibited a distinctive CN decrease under confinement, while smaller cations (Li + and Na + ) retained a similar hydration shell as in the bulk solution. More specifically, the hydration shell of large cations (K + , Rb + , and Cs + ) in MoS 2 showed similar features of the coordination angle to the bulk, which suggests the partially broken hydration shell with no geometry change under confinement. Our simulations provided insights into the change of the hydration structure of alkaline metal cations under confinement, which may have important implications on their transport in the 1T-MoS 2 channel.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ammonia Synthesis under Ambient Conditions: Insights into Water–Nitrogen–Magnetite Interfaces

New routes for transforming nitrogen into ammonia at ambient conditions would be a milestone toward an energy efficient and economically attractive production route in comparison to the traditional Haber-Bosch process. Recently, the synthesis of ammonia from water and nitrogen at room temperature and atmospheric pressure has been reported to be catalyzed by Fe 3 O 4 at the air-water interface. By integrating ambient pressure X-ray photoelectron spectroscopy and ab initio molecular dynamics and free energy calculations, we investigate the underlying thermodynamic mechanisms governing ammonia and hydrazine formation at the water-Fe 3 O 4 -nanoparticle interface. Here, we find that, unlike pure Fe 3 O 4 where N 2 can only interact with a limited number of Fe sites, hydroxylated species introduce large and diverse adsorption geometries where N 2 can bind through either Fe sites or Fe-OH groups, each of which are capable of independently facilitating proton-coupled electron transfer.

Chandy, Sruthy K. [University of California, Berke↗
DOE OSTI2023

Strain enhances the activity of molecular electrocatalysts via carbon nanotube supports

Abstract Support-induced strain engineering is useful for modulating the properties of two-dimensional materials. However, controlling strain of planar molecules is technically challenging due to their sub-2 nm lateral size. Additionally, the effect of strain on molecular properties remains poorly understood. Here we show that carbon nanotubes (CNTs) are ideal substrates for inducing optimum properties through molecular curvature. In a tandem-flow electrolyser with monodispersed cobalt phthalocyanine (CoPc) on single-walled CNTs (CoPc/SWCNTs) for CO 2 reduction, we achieve a methanol partial current density of >90 mA cm −2 with >60% selectivity, surpassing wide multiwalled CNTs at 16.6%. We report vibronic and X-ray spectroscopies to unravel the distinct local geometries and electronic structures induced by the strong molecule–support interactions. Grand canonical density functional theory confirms that curved CoPc/SWCNTs improve *CO binding to enable subsequent reduction, whereas wide multiwalled CNTs favour CO desorption. Our results show the important role of SWCNTs beyond catalyst dispersion and electron conduction.

Chemistry↗