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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 181 records · Page 10

DFT-Based Calculation of Dissolution Activation Energy and Kinetics of Ni–Cr Alloys

A density functional theory investigation of the process of dissolution of Ni and Cr adatoms from model Ni–Cr(111) alloy surfaces is presented, both in vacuum and with explicit water molecules. The goal is to understand how the electronic structures solved using DFT can provide insights as to changes in valence, energy and coordination of the adatoms in the process of dissolving from the alloy surface. It is found that nearby Cr solute atoms increase the dissolution activation energy of Ni. Cr adatoms have a similar dissolution activation energy as Ni adatoms, except for one particular surface configuration that has a much smaller dissolution activation energy, which might promote selective dissolution of Cr in Ni–Cr alloys. To interpret the first-principles modeling results for the potential energy trajectory along the dissolution reaction coordinate, we provide a thermodynamic breakdown of the various terms that contribute to the dissolution activation and total reaction energy. The role of addition of Cr is explored on the dissolution kinetics of a Ni–Cr alloys of varying composition by constructing a model for the corrosion current density with DFT-based activation energies. Lastly, dissolution resistance index is proposed as a quantifiable descriptor of the dissolution resistance of corrosion resistant alloys.

36 MATERIALS SCIENCE↗

Babinet's principle and saturation of the resonance frequency of scaled-down complementary metasurfaces

It has been previously demonstrated that the resonant frequency of the Split Ring Resonator (SRR) reaches a saturation value when its size is continuously scaled down, and at the same time, the resonance is attenuated. Now, it is demonstrated that similar saturation and attenuation phenomena happens to its complementary screen, which is commonly called a Complementary SRR (CSRR). It was found that the saturation frequency of the CSRR is near the saturation frequency of the SRR with equal geometrical dimensions. It is possible to control their saturation levels by tuning the geometry of the resonators. This work is relevant for the design of quasi-optical metamaterials based on one or both kinds of structures (i.e., SRRs and CSRRs) at once, especially when they both are required to resonate at the same frequency.

Ortiz, Julián D. (ORCID:0000000248056148)↗

Hunting Sub-GeV Dark Matter with Diamonds and Magnetic Microcalorimeters Principle

This project is to study feasibility of a new concept for the sub-GeV dark matter (DM) detection using diamond crystals and magnetic microcalorimeters (MMCs). Diamond crystals are made of low mass carbon constituent that maximize kinetic energy of nuclear recoils from sub-GeV DM scattering due to their relatively similar masses. MMC is employed as a sensitive phonon sensor to measure athermal phonons that are produced by DM scattering in diamond crystals with 100 ns timing resolution and ~10 eV energy resolution. MMC’s fast timing resolution allows high precision phonon pulse shape analysis to separate out unwanted background or noise signals in the low energy region at E < 1 keV. Especially, the low energy excess (LEE) problems have been reported in the dark matter detection community and the proposed diamond-MMC detector might be able to provide important information to understand the origins of LEE signals in DM detectors. In this project a diamond-MMC detector has been built for proof-of-concept experiments and athermal phonon collection efficiencies have been investigated for single crystal and poly-crystal chemical-vapour-diamonds (SC and PC CVDs) as well as reference sapphire crystals are tested in the same geometries to quatify athermal phonon propagation and their collection to the MMC phonon sensors. Athermal phonon collection efficiencies and their lifetime in the diamond and sapphire crystals are successfully measured in the same geometry and experimental setup for comparison. SC CVD crystals exhibit poorer performance than the sapphire reference crystals in athermal phonon collection, while the PC CVD crystal exhibit better result than the sapphire. PC CVD with MMC phonon sensors would be feasible for sub GeV DM detection.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

Interfacial contributions to spin-orbit torque and magnetoresistance in ferromagnet/heavy-metal bilayers

The thickness dependence of spin-orbit torque and magnetoresistance in ferromagnet/heavy-metal bilayers is studied here using the first-principles nonequilibrium Green's function formalism combined with the Anderson disorder model. A systematic expansion in orthogonal vector spherical harmonics is used for the angular dependence of the torque. The dampinglike torque in Co/Pt and Co/Au bilayers can be described as a sum of the spin-Hall contribution, which increases with thickness in agreement with the spin-diffusion model, and a comparable interfacial contribution. The magnetoconductance in the plane perpendicular to the current in Co/Pt bilayers is of the order of a conductance quantum per interfacial atom, exceeding the prediction of the spin-Hall model by more than an order of magnitude. This indicates that the “spin-Hall magnetoresistance,” similarly to the dampinglike torque, has a large interfacial contribution unrelated to the spin-Hall effect.

36 MATERIALS SCIENCE↗

Understanding the influence of solvents on the Pt-catalyzed hydrodeoxygenation of guaiacol

Bio-oil derived from fast pyrolysis of lignocellulosic biomass needs to be deoxygenated to become a substitute for petroleum fuels. Here, we study the hydrodeoxygenation mechanism of guaiacol, a bio-oil model compound derived from the lignin fraction of biomass, on Pt(1 1 1) terrace sites in the presence of water, diethyl ether, 1-butanol, and n-hexane as solvent. Using first-principles periodic density functional theory (DFT) calculations and mean-field microkinetic reactor modeling, a detailed reaction mechanism is investigated targeting various products such as catechol, phenol, anisole, benzene, cyclohexanone, and cyclohexanol. Solvent phase DFT outcomes are mostly similar to that of the vapor phase; however, microkinetic modeling results suggest that rate controlling species and transition states differ somewhat in the various reaction environments. Catechol was found to be the major aromatic product across all reaction environments. Over Pt(1 1 1), unsaturated monooxygenate production from catechol is unlikely.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Near-continuum, hypersonic oxygen flow over a double cone simulated by direct simulation Monte Carlo informed from quantum chemistry

A large-scale, fully resolved direct simulation Monte Carlo (DSMC) computation of a non-equilibrium, reactive flow of pure oxygen over a double cone is presented. Under the simulated near-continuum conditions, the computational demands are shown to be significant because of the wide range of length scales that must be resolved. Therefore, robust grid adaption capabilities and efficient parallelization of the Stochastic PArallel Rarefied-gas Time-accurate Analyzer (SPARTA) code that is utilized in this work are essential. The thermochemical and transport collision models were selected for efficiency and simplicity. First-principles data, obtained from the highly accurate direct molecular simulation method, were used to inform the collision models’ parameters. Importantly, because SPARTA implements molecular collision models using collision-specific energies, the resulting macroscopic relaxation rates were evaluated a posteriori via zero-dimensional heat bath simulations. The comparisons of surface properties, namely heat flux and pressure, show very close agreement with previous computational fluid dynamics (CFD) results. Differences with the measurements were found to be similar to the CFD simulations. The unresolved discrepancy with the measurements could be due to inconsistent free stream conditions with the actual experimental data or missing physical phenomena altogether, for example atomic and molecular oxygen electronically excited states, three-dimensional effects, or more complex gas–surface interactions. As shown in this work, the advantages of obtaining a DSMC particle solution for these flows reside in the method's ability to be directly informed from first principles and to seamlessly describe internal energy non-equilibrium for all modes. With the advent of exascale computing and beyond, particle methods will be an increasingly important tool to verify the validity of physical assumptions in reduced-order models via fully resolved, experimental-scale simulations, down to the level of molecular-level distributions.

Mechanics↗

ProZES: the methodology and software tool for assessment of assigned share of radiation in probability of cancer occurrence

ProZES is a software tool for estimating the probability that a given cancer was caused by preceding exposure to ionising radiation. ProZES calculates this probability, the assigned share, for solid cancers and hematopoietic malignant diseases, in cases of exposures to low-LET radiation, and for lung cancer in cases of exposure to radon. User-specified inputs include birth year, sex, type of diagnosed cancer, age at diagnosis, radiation exposure history and characteristics, and smoking behaviour for lung cancer. Cancer risk models are an essential part of ProZES. Linking disease and exposure to radiation involves several methodological aspects, and assessment of uncertainties received particular attention. ProZES systematically uses the principle of multi-model inference. Models of radiation risk were either newly developed or critically re-evaluated for ProZES, including dedicated models for frequent types of cancer and, for less common diseases, models for groups of functionally similar cancer sites. The low-LET models originate mostly from the study of atomic bomb survivors in Hiroshima and Nagasaki. Risks predicted by these models are adjusted to be applicable to the population of Germany and to different time periods. Adjustment factors for low dose rates and for a reduced risk during the minimum latency time between exposure and cancer are also applied. The development of the methodology and software was initiated and supported by the German Federal Ministry for the Environment, Nature Conservation and Nuclear Safety (BMU) taking up advice by the German Commission on Radiological Protection (SSK, Strahlenschutzkommission). These provide the scientific basis to support decision making on compensation claims regarding malignancies following occupational exposure to radiation in Germany.

97 MATHEMATICS AND COMPUTING↗

Leveraging Polymorphism in YbCuBi to Map Transport and Elastic Properties

AMX Zintl compounds with the hexagonal ZrBeSi structure have gained significant attention for their remarkable vacancy tolerance and low thermal conductivity. Their 2D honeycomb sublattice, composed of M–X covalent bonds, is believed to contribute to high anharmonicity and unusual thermal transport properties. In this study, we explore the temperature-dependent polymorphism of YbCuBi as a model system to investigate the relationship between the structure and elastic and thermal transport properties in AMX Zintls. YbCuBi undergoes a structural transition from the “flat” Cu–Bi layers in the ZrBeSi structure to corrugated layers in the LiGaGe structure below 410 K, resulting in a distortion of its centrosymmetric structure. To probe the effects of this crystallographic transition, we employ inelastic neutron scattering and temperature-dependent resonant ultrasound spectroscopy. These experimental findings, coupled with first-principles calculations and thermal conductivity measurements, allow us to elucidate a direct relationship between corrugation of the honeycomb lattice and the observed changes in elastic and thermal transport properties. These insights can be extended to other Zintl phases with similar structure types, providing a platform for the rational design of functional materials with tailored thermal properties.

Chemical structure↗

An accurate measurement of the spectral resolution of the JWST Near Infrared Spectrograph

The spectral resolution (R ≡ λ/Δλ) of spectroscopic data is crucial information for accurate kinematic measurements. In this letter we present a robust measurement of the spectral resolution of the JWST Near Infrared Spectrograph (NIRSpec) in fixed slit (FS) and integral field spectroscopy (IFS) modes. Due to the similarity of the utilized slit dimension in the FS mode to that of the shutters in the multi-object spectroscopy (MOS) mode, our resolution measurements in the FS mode can also be used for the MOS mode in principle. We modeled H and He lines of the planetary nebula SMP LMC 58 using a Gaussian line spread function (LSF) to estimate the wavelength-dependent resolution for multiple disperser and filter combinations. We corrected for the intrinsic width of the planetary nebula’s H and He lines due to its expansion velocity by measuring it from a higher-resolution X-shooter spectrum. We find that NIRSpec’s in-flight spectral resolutions exceed the pre-launch estimates provided in the JWST User Documentation by 11–53% in the FS mode and by 1–24% in the IFS mode across the covered wavelengths. We recover the expected trend that the resolution increases with the wavelength within a configuration. The robust and accurate LSFs presented in this letter will enable high-accuracy kinematic measurements using NIRSpec for applications in cosmology and galaxy evolution.

methods: data analysis↗

Observation of van der Waals phonons in the single-layer cuprate ( Bi , Pb ) 2 ( Sr , La ) 2 CuO 6 + δ

Interlayer van der Waals (vdW) coupling is generic in two-dimensional materials such as graphene and transition-metal dichalcogenides, which can induce very low-energy phonon modes. Using high-resolution inelastic hard x-ray scattering, we uncover the ultralow energy phonon mode along the Cu-O bond direction in the high- T c cuprate (Bi,Pb) 2 (Sr,La) 2 ⁢CuO 6+$\delta$ (Bi2201). The energy and full-width half-maximum (FWHM) of these modes are independent of temperature, while their intensity decreases with doping in accordance with an increasing c -axis lattice parameter. Here, we compare the experimental results to first-principles density functional theory simulations and identify the observed mode as a van der Waals phonon, which arises from the shear motion of the adjacent Bi-O layers. This shows that Bi-based cuprate has vibrational properties similar to graphene and transition-metal dichalcogenides, which can be exploited to engineer novel heterostructures.

36 MATERIALS SCIENCE↗

Final Report (October 2024): University of Tennessee, Knoxville (UTK) contribution to: FusMatML: Machine Learning Atomistic Modeling for Fusion Materials Collaborative Project led by Dr. Aidan Thompson, Sandia National Laboratory

The rapid growth of the field of Machine Learning Inter-Atomic Potentials (MLIAP) has lead to a profusion of methods, all of which have some similarity to each other, but each also restricted to particular design choices, often arrived at in a rather ad hoc fashion. Beyond anecdotal evidence, and some benchmarking studies on specific problems, little progress has been made in developing design principles for MLIAPs. The goal of this project is to use machine learning, data science, and uncertainty quantification methods to optimize the design choices for MLIAP.

Density functional theory, Helium and Hydrogen↗

Direct and indirect spin current generation and spin-orbit torques in ferromagnet/nonmagnet/ferromagnet trilayers

Spin-orbit torques in ferromagnet/nonmagnet/ferromagnet trilayers are studied using a combination of symmetry analysis, circuit theory, semiclassical simulations, and first-principles calculations using the nonequilibrium Green's function method with supercell disorder averaging. Here, we focus on unconventional processes involving the interplay between the two ferromagnetic layers, which are classified into direct and indirect mechanisms. The direct mechanism involves spin current generation by one ferromagnetic layer and its subsequent absorption by the other. In the indirect mechanism, the in-plane spin-polarized current from one ferromagnetic layer “leaks” into the other layer, where it is converted into an out-of-plane spin current and reabsorbed by the original layer. The direct mechanism results in a predominantly dampinglike torque, which damps the magnetization towards a certain direction 𝐬 𝑑 . The indirect mechanism results in a predominantly fieldlike torque with respect to a generally different direction 𝐬 𝑓 . Similarly to the current-in-plane giant magnetoresistance, the indirect mechanism is only active if the thickness of the nonmagnetic spacer is smaller than or comparable to the mean free path. Numerical calculations for a semiclassical model based on the Boltzmann equation confirm the presence of both direct and indirect mechanisms of spin current generation. First-principles calculations reveal sizable unconventional spin-orbit torques in Co/Cu/Co, Py/Cu/Py, and Co/Pt/Co trilayers and provide strong evidence of indirect spin current generation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

Effect of disorder on thermodynamic instability of binary Rare-earth – Nickel – Palladium compounds

In this work, we have investigated the thermodynamic stability of disordered rare-earth phases SmX 2 and Sm 10 X 21 (X=Ni, Pd) using machine-learning based analytical descriptor and first-principles density functional theory methods. The absence of Laves phase compounds in R-Pd binary systems is a longstanding problem of rare earth science: even though Ni and Pd belong to the same group of the periodic table and have similar electronic structure, the Pd compound crystallizes in a monoclinic (C2/m) phase with 10:21 stoichiometry, i.e., Sm 10 Pd 21 , while the Ni compound adopts a cubic Laves phase (MgCu 2 ) structure. To understand this contrasting phase stability, we performed thermodynamic convex hull analysis of Sm x Ni 1-x and Sm x Pd 1-x binary systems, which is experimentally validated using powder X-ray diffraction (PXRD) analyzes of polycrystalline Sm(Ni x Pd 1-x ) 2 samples with x=0, 0.5, and 1. A detailed electronic-structure (band-structure, charge density, and Fermi-surface) analysis of the differences between SmNi 2 /SmPd 2 and Sm 10 Ni 21 /Sm 10 Pd 21 compounds provides the quantum mechanical origin of the unfavorable mixing of Pd with Ni in cubic Laves phase. We show that the stability of Sm-Pd in 10:21 stoichiometry arises from improved intra-/inter-layer 5d-4d bonding compared to the 1:2 stoichiometry. Our work emphasizes the importance of ab-initio methods and computationally inexpensive analytical descriptors for the detailed analysis of thermodynamic and electronic properties of hard-to-prepare rare-earth compounds.

36 MATERIALS SCIENCE↗

Revealing the Chemical Bonding in Adatom Arrays via Machine Learning of Hyperspectral Scanning Tunneling Spectroscopy Data

The adatom arrays on surfaces offer an ideal playground to explore the mechanisms of chemical bonding via changes in the local electronic tunneling spectra. While this information is readily available in hyperspectral scanning tunneling spectroscopy data, its analysis has been considerably impeded by a lack of suitable analytical tools. Here we develop a machine learning based workflow combining supervised feature identification in the spatial domain and unsupervised clustering in the energy domain to reveal the details of structure-dependent changes of the electronic structure in adatom arrays on the Co 3 Sn 2 S 2 cleaved surface. This approach, in combination with first-principles calculations, provides insight for using artificial neural networks to detect adatoms and classifies each based on their local neighborhood comprised of other adatoms. These structurally classified adatoms are further spectrally deconvolved. The unexpected inhomogeneity of electronic structures among adatoms in similar configurations is unveiled using this method, suggesting there is not a single atomic species of adatoms, but rather multiple types of adatoms on the Co 3 Sn 2 S 2 surface. This is further supported by a slight contrast difference in the images (or slight size variation) of the topography of the adatoms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

Long-read sequencing transcriptome quantification with lr-kallisto

RNA abundance quantification has become routine and affordable thanks to high-throughput “short-read” technologies that provide accurate molecule counts at the gene level. Similarly accurate and affordable quantification of definitive full-length, transcript isoforms has remained a stubborn challenge, despite its obvious biological significance across a wide range of problems. “Long-read” sequencing platforms now produce data-types that can, in principle, drive routine definitive isoform quantification. However some particulars of contemporary long-read datatypes, together with isoform complexity and genetic variation, present bioinformatic challenges. We show here, using ONT data, that fast and accurate quantification of long-read data is possible and that it is improved by exome capture. To perform quantifications we developed lr-kallisto, which adapts the kallisto bulk and single-cell RNA-seq quantification methods for long-read technologies.

Loving, Rebekah K. (ORCID:0000000187250376)↗

New principles of self‐organization created through the interplay of DNA condensates, microtubules, and motors

Bioinspired design—which holds great promise for a new generation of materials that are robust to defects, scalable under green manufacture, environmentally responsive, and programmably reconfigurable—requires mastery over molecular self-organization. Yet, from its specific mechanisms to most general architectures, the principles governing self-organization remain poorly understood and not even fully enumerated. For living systems, one obvious architectural principle is the modular reuse of a few simple molecular components in myriad combinations to achieve more complex phenomena. For example, the mechanical tasks of a cell are driven by the nonequilibrium dynamics of cytoskeletal filaments and molecular motors—the same filaments and motors, reprogrammed by a variety of modulators, perform tasks ranging from cell movement to division. Similarly, many compartmentalization tasks are performed by liquid-like condensates of simple components, which act as membraneless organelles to localize particular molecules in space and time (e.g. for gene regulation or RNA processing). In a few cases, condensates combine and interact with the cytoskeleton to create still more complex phenomena, e.g. the nucleation of microtubule asters from the centrosome (a protein condensate) to form the mitotic spindle during cell division. Very little is known about the fundamental mechanisms of such filament-plus-condensate phenomena. Despite few examples, the landscape of behaviors that can be achieved through the combination of condensates, filaments, and motors appears vast. However, exploration has been hindered by a lack of systems that have sufficiently programmable and dynamically tunable interactions between component condensates, filaments, and motors. We proposed to combine programmable DNA condensates, filamentous microtubules, and light-controlled motors into self-organizing systems whose principles go beyond those that have been observed in nature. In one limit, our systems will use microtubules and motors to create the molecular analog of a network of roads, which will organize droplets of DNA condensates capable of carrying molecular cargo. DNA condensates coupled to motors will flow from one microtubule aster hub to another, with their direction and timing controlled by DNA circuits. In another limit, microtubules will swim through bulk DNA condensates and exhibit strong interactions with boundaries between different types of condensates. Microtubule swimmers will reflect, get trapped, or refract at boundaries, under a mechanical analog of the classical optical index of refraction. DNA condensates having different mechanical indexes of refraction will be used to construct the analog of optical lenses, so that microtubule swimmers can be manipulated like light—collimated, diffracted, focused, and sorted based on properties analogous to wavelength. These two limits define two new architectures, within which multiple new mechanistic principles for self-organization will be discovered and explored. To explore these architectures, the motor-based coupling between DNA condensates and filaments will be controlled in time and space through the use of opto-proteins that create reversible links between DNA condensates and motors upon illumination. For each principle of interest, patterns of light will create virtual experiments by defining patterns of activity where DNA condensates walk along filaments, or filaments swim through condensates, and patterns of inactivity which will serve either as controls, or as boundary conditions vital to create the desired phenomena. This research serves the goals of Basic Energy Sciences Biomolecular Material Program by elucidating the principles by which the emergent, nonequilibrium behavior of collections of DNA condensates, motors, and microtubules can be programmed by environmental light patterns to create complex motion and materials transport. Because DNA condensates can be readily coupled to virtually any high performance nanomaterial, from carbon nanotubes, to metal nanoparticles, to light harvesting systems, this work provides a path to the construction, self-maintenance and reconfiguration of materials relevant to the Department of Energy.

60 APPLIED LIFE SCIENCES↗

Role of chalcogen vacancies and hydrogen in the optical and electrical properties of bulk transition-metal dichalcogenides

Abstract Like in any other semiconductor, point defects in transition-metal dichalcogenides (TMDs) are expected to strongly impact their electronic and optical properties. However, identifying defects in these layered two-dimensional materials has been quite challenging with controversial conclusions despite the extensive literature in the past decade. Using first-principles calculations, we revisit the role of chalcogen vacancies and hydrogen impurity in bulk TMDs, reporting formation energies and thermodynamic and optical transition levels. We show that the S vacancy can explain recently observed cathodoluminescence spectra of MoS 2 flakes and predict similar optical levels in the other TMDs. In the case of the H impurity, we find it more stable sitting on an interstitial site in the Mo plane, acting as a shallow donor, and possibly explaining the often observed n-type conductivity in some TMDs. We also predict the frequencies of the local vibration modes for the H impurity, aiding its identification through Raman or infrared spectroscopy.

36 MATERIALS SCIENCE↗

Machine learning force field model for kinetic Monte Carlo simulations of itinerant Ising magnets

Here, we present a scalable machine learning (ML) framework for large-scale kinetic Monte Carlo (kMC) simulations of itinerant electron Ising systems. As the effective interactions between Ising spins in such itinerant magnets are mediated by conducting electrons, the calculation of energy change due to a local spin update requires solving an electronic structure problem. Such repeated electronic structure calculations could be overwhelmingly prohibitive for large systems. Assuming the locality principle, a convolutional neural network (CNN) model is developed to directly predict the effective local field and the corresponding energy change associated with a given spin update based on Ising configuration in a finite neighborhood. As the kernel size of the CNN is fixed at a constant, the model can be directly scalable to kMC simulations of large lattices. Our approach is reminiscent of the ML force field models widely used in first-principles molecular dynamics simulations. Applying our ML framework to a square-lattice double-exchange Ising model, we uncover unusual coarsening of ferromagnetic domains at low temperatures. Our work highlights the potential of ML methods for large-scale modeling of similar itinerant systems with discrete dynamical variables.

machine learning↗