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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 271 records · Page 15
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Energy filtering–induced ultrahigh thermoelectric power factors in Ni 3 Ge

Traditional thermoelectric materials rely on low thermal conductivity to enhance their efficiency but suffer from inherently limited power factors. Innovative pathways to optimize electronic transport are thus crucial. Here, we achieve ultrahigh power factors in Ni 3 Ge-based systems through an unconventional thermoelectric materials design principle. When overlapping flat and dispersive bands are engineered to the Fermi level, charge carriers can undergo intense interband scattering, yielding an energy filtering effect similar to what has long been predicted in certain nanostructured materials. Via a multistep DFT-based screening method developed here, we find a family of L1 2 -ordered binary compounds with ultrahigh power factors up to 11 mW m −1 K −2 near room temperature, which are driven by an intrinsic phonon-mediated energy filtering mechanism. Our comprehensive experimental and theoretical study of these intriguing materials paves the way for understanding and designing high-performance scattering-tuned metallic thermoelectrics.

Science & Technology - Other Topics↗
DOE OSTI2020

A Thermal and Nanomechanical Study of Molecular Crystals as Versatile Mocks for Pentaerythritol Tetranitrate

Pentaerythritol tetranitrate (PETN) is a commonly used high explosive (HE) in detonators. Often, surrogate or “mock” materials are used in place of HE for mechanical tests, proofing out equipment, or developing new diagnostics. However, there is no commonly accepted mock for PETN. A good mock should match at least one physical property of the target material, and ideally mimic multiple thermal and mechanical properties. Here, we investigate several molecular crystals to evaluate their efficacy in mocking PETN density, melting point, elastic modulus, hardness, plastic deformation, and fracture behavior. Materials were tested with a combination of calorimetry and nanoindentation. Two materials, 2,4,6-trifluorobenzoic acid (246 TFBA) and mesoerythritol, were downselected for detailed indentation study after the initial round of screening experiments, both were found to mimic PETN mechanical behavior quite well, 246 TFBA closer to PETN in most properties (hardness, modulus, and density) than erythritol, but erythritol having advantages in relative cost and matching the onset of yield. Depending on the desired implementation of the mock, one material may be preferred over the other, but both have potential as generic mocks for PETN. Nanoindentation is demonstrated as a versatile tool to provide rapid screening of these materials’ mechanical properties.

42 ENGINEERING↗
DOE OSTI2022

Screening of bimetallic electrocatalysts for water purification with machine learning

Electrocatalysis provides a potential solution to NO 3 - pollution in wastewater by converting it to innocuous N 2 gas. However, materials with excellent catalytic activity are typically limited to expensive precious metals, hindering their commercial viability. Here, in response to this challenge, we have conducted the most extensive computational search to date for electrocatalysts that can facilitate NO 3 - reduction reaction, starting with 59 390 candidate bimetallic alloys from the Materials Project and Automatic-Flow databases. Using a joint machine learning- and computation-based screening strategy, we evaluated our candidates based on corrosion resistance, catalytic activity, N 2 selectivity, cost, and the ability to synthesize. We found that only 20 materials will satisfy all criteria in our screening strategy, all of which contain varying amounts of Cu. Our proposed list of candidates is consistent with previous materials investigated in the literature, with the exception of Cu–Co and Cu–Ag based compounds that merit further investigation.

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

Gamma Spectroscopy Detector Candidates and Radioisotope Identification Algorithms for Use in National Security Applications

In 2006, the United States Congress mandated for the Department of Homeland Security (DHS) to screen all cargo containers to protect against terrorist acts and ensure the safety and security of the nation. Containers are screened by gamma and neutron detectors to ensure threat material is not smuggled into the country. However, because commerce is radioactive, detecting the presence of radioactive materials is not sufficient in ensuring the safety and security of the nation. Radioactive materials must also be identified in real time, thus distinguishing threat sources (Uranium-235 and Plutonium-230) from non-threat sources (kitty litter, pot ash, medical isotopes). Screening cargo containers can be considered a two-step process (1) alerting to the presence of radioactive material when gamma counts exceed a threshold setting, and (2) once alerted, identifying the type of radioactive material, which is done by collecting a gamma spectra and analyzing it with an analysis tool/algorithm. For this reason, it is important to evaluate not only emerging technology in neutron and gamma detection, but also investigate new advances in algorithm development for radioisotope identification (RIID). New candidates in detection and on-board algorithm analysis might offer opportunities to make the scanning, detection, and identification process more efficient while still ensuring the health and safety of the public. This research will investigate emerging technology in radiation detection focused on gamma spectroscopy capabilities and RIID algorithms for DHS applications.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2022

Universal machine learning framework for defect predictions in zinc blende semiconductors

Our article introduces a universal predictive framework for point defect formation energies and charge transition levels in a wide chemical space of zinc blende semiconductors and possible impurity atoms selected from across the periodic table. This framework was developed by leveraging high-throughput quantum mechanical simulations benchmarked using some experimental data from the literature, as well as machine learning (ML)-based regressions techniques that map unique materials descriptors to computed defect properties and yield optimized and generalizable models. Furthermore, the power and utility of these models is revealed through quick predictions for thousands of new defects and screening of low-energy impurities, which may tune the equilibrium conductivity in the semiconductor. This work presents, to our knowledge, the largest density functional theory (DFT) dataset of defect properties in semiconductors and the largest DFT+ML-based screening of point defects in semiconductors to date.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Identifying High-Performance Metal–Organic Frameworks for Low-Temperature Oxygen Recovery from Helium by Computational Screening

Metal–organic frameworks (MOFs) are an important class of porous crystalline materials for applications ranging from gas adsorption and separation to catalysis. There are thousands of potential MOFs available for separation applications. Here, we developed a computational approach to screen MOFs for the separation of oxygen–helium mixtures at low temperatures (100–200 K), conditions that were motivated by issues associated with propulsion in space-based settings. We used detailed molecular simulations for a small number of MOFs to develop screening methods that were then used to estimate the optimum temperatures for separations using pressure swing adsorption for 2932 MOFs from the CoRE MOF database and the swing capacity and oxygen–helium selectivity at these temperatures. We used the stability of the best-performing structures in the presence of moisture as a means to provide a short list of high-performance materials. In addition to identifying specific materials for oxygen–helium separations, this approach could prove useful for selecting adsorbents for other gas separations.

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

Ultrafast creation of a light-induced semimetallic state in strongly excited 1T-TiSe 2

Screening, a ubiquitous phenomenon associated with the shielding of electric fields by surrounding charges, has been widely adopted as a means to modify a material’s properties. While most studies have relied on static changes of screening through doping or gating thus far, here we demonstrate that screening can also drive the onset of distinct quantum states on the ultrafast timescale. By using time- and angle-resolved photoemission spectroscopy, we show that intense optical excitation can drive 1T-TiSe 2 , a prototypical charge density wave material, almost instantly from a gapped into a semimetallic state. By systematically comparing changes in band structure over time and excitation strength with theoretical calculations, we find that the appearance of this state is likely caused by a dramatic reduction of the screening length. In summary, this work showcases how optical excitation enables the screening-driven design of a nonequilibrium semimetallic phase in TiSe 2 , possibly providing a general pathway into highly screened phases in other strongly correlated materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Designing shape-memory-like microstructures in intercalation materials

During the reversible insertion of ions, lattices in intercalation materials undergo structural transformations. These lattice transformations generate misfit strains and volume changes that, in turn, contribute to the structural decay of intercalation materials and limit their reversible cycling. In this paper, we draw on insights from shape-memory alloys, another class of phase transformation materials, that also undergo large lattice transformations but do so with negligible macroscopic volume changes and internal stresses. We develop a theoretical framework to predict structural transformations in intercalation compounds and identify crystallographic design rules necessary for forming shape-memory-like microstructures in intercalation materials. We use our approach to systematically screen open-source structural databases comprising $n$ > 5000 pairs of intercalation compounds. We identify candidate compounds, such as Li x Mn 2 O 4 (Spinel), Li x Ti 2 (PO 4 ) 3 (NASICON), that approximately satisfy the crystallographic design rules and can be precisely doped to form shape-memory-like microstructures. Throughout, we compare our analytical results with experimental measurements of intercalation compounds. We find a direct correlation between structural transformations, microstructures, and increased capacity retention in these materials. These results, more generally, show that crystallographic designing of intercalation materials could be a novel route to discovering compounds that do not decay with continuous usage.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Erratum to: Soft- and reactive landing of ions onto surfaces: concepts and applications

Soft- and reactive landing of mass-selected ions is gaining attention as a promising approach for the precisely-controlled preparation of materials on surfaces that are not amenable to deposition using conventional methods. A broad range of ionization sources and mass filters are available that make ion soft-landing a versatile tool for surface modification using beams of hyperthermal (<100?eV) ions. The ability to select the mass-to-charge ratio of the ion, its kinetic energy and charge state, along with precise control of the size, shape, and position of the ion beam on the deposition target distinguishes ion soft landing from other surface modification techniques. Soft- and reactive landing have been used to prepare interfaces for practical applications as well as precisely-defined model surfaces for fundamental investigations in chemistry, physics, and materials science. For instance, soft- and reactive landing have been applied to study the surface chemistry of ions isolated in the gas-phase, prepare arrays of proteins for high-throughput biological screening, produce novel carbon-based and polymer materials, enrich the secondary structure of peptides and the chirality of organic molecules, immobilize electrochemically-active proteins and organometallics on electrodes, create thin films of complex molecules, and immobilize catalytically active organometallics as well as ligated metal clusters. In addition, soft landing has enabled investigation of the size-dependent behavior of bare metal clusters in the critical subnanometer size regime where chemical and physical properties do not scale predictably with size. The morphology, aggregation, and immobilization of larger bare metal nanoparticles, which are directly relevant to the design of catalysts as well as improved memory and electronic devices, have also been studied using ion soft landing. This review article begins in section 1 with a brief introduction to the existing applications of ion soft- and reactive landing. Section 2 provides an overview of the ionization sources and mass filters that have been used to date for soft landing of mass-selected ions. A discussion of the competing processes that occur during ion deposition as well as the types of ions and surfaces that have been investigated follows in section 3. Section 4 discusses the physical phenomena that occur during and after ion soft landing, including retention and reduction of ionic charge along with factors that impact the efficiency of ion deposition. The influence of soft landing on the secondary structure and biological activity of complex ions is addressed in section 5. Lastly, an overview of the structure and mobility as well as the catalytic, optical, magnetic, and redox properties of bare ionic clusters and nanoparticles deposited onto surfaces is presented in section 6. © 2015 Wiley Periodicals, Inc. Mass Spec Rev 35:439–479, 2016.

Johnson, Grant E.↗
DOE OSTI2022

Performance of borated scintillator screens for high-resolution neutron imaging

The most commonly used screens for neutron imaging consist of 6 LiF + ZnS. This type of screen yields the highest light output per detected neutron. For high resolution, gadolinium oxysulfide (GOS, Gadox) screens are employed, which have a much higher detection efficiency, but a light output so much lower than LiF + ZnS that measurements are often limited by photon statistics. Historically, screens using boron as a neutron-sensitive material have not been very successful. However, a new preparation method was introduced recently that produces light output higher than Gadox with detection efficiency greater than LiF + ZnS. Measurements of these new borated screens were performed at the NeXT facility at ILL, Grenoble, in comparison to a high resolution Gadox screen.

10B↗
DOE OSTI2022

Detection of multi-reference character imbalances enables a transfer learning approach for virtual high throughput screening with coupled cluster accuracy at DFT cost

Appropriately identifying and treating molecules and materials with significant multi-reference (MR) character is crucial for achieving high data fidelity in virtual high-throughput screening (VHTS). Despite development of numerous MR diagnostics, the extent to which a single value of such a diagnostic indicates the MR effect on a chemical property prediction is not well established. We evaluate MR diagnostics for over 10 000 transition-metal complexes (TMCs) and compare to those for organic molecules. We observe that only some MR diagnostics are transferable from one chemical space to another. By studying the influence of MR character on chemical properties (i.e., MR effect) that involve multiple potential energy surfaces (i.e., adiabatic spin splitting, ΔE H–L , and ionization potential, IP), we show that differences in MR character are more important than the cumulative degree of MR character in predicting the magnitude of an MR effect. Motivated by this observation, we build transfer learning models to predict CCSD(T)-level adiabatic ΔE H–L and IP from lower levels of theory. By combining these models with uncertainty quantification and multi-level modeling, we introduce a multi-pronged strategy that accelerates data acquisition by at least a factor of three while achieving coupled cluster accuracy (i.e., to within 1 kcal mol –1 MAE) for robust VHTS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Quantitative XPS of plutonium: Evaluation of the Pu4f peak shape, relative sensitivity factors and estimated detection limits

We report that high-resolution X-ray photoelectron spectra have been acquired from sputter cleaned and in situ oxidized samples of α-plutonium and a face-centred cubic δ-plutonium–gallium alloy. The differences in the Pu4f peak shape in these two materials have been investigated, and the poorly screened satellite peaks have been quantified. Curve fitting parameters for the Pu4p 3/2 , Pu4d 5/2 , Pu4f and Pu6p 3/2 photoelectron peaks are reported, and relative sensitivity factors have been determined. The Pu4f curve fit model has been applied to data acquired using different spectrometers and alloys. Examples of quantification of the plutonium spectra are provided and minimum detection limits are calculated for common impurities in plutonium metal.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Robustness of electronic screening effects in electron spectroscopies: Example of V 2 O 5

In bulk and low-dimensional extended systems, the screening of excitations by the electron cloud is a key feature governing spectroscopic properties. Widely used computational approaches, especially in the framework of many-body perturbation theory, such as the GW approximation and the resulting approximate Bethe-Salpeter equation, are explicitly formulated in terms of the screened Coulomb interaction. In the present work, we explore the effect of screening in absorption and electron energy loss spectroscopy, concentrating on the effect of local distortions on the screening and elucidating the resulting changes in the various spectra. Using the layered bulk oxide V 2 O 5 as prototype material, we show in which way local distortions affect the screening, and in which way changes in the screening impact electron energy loss and absorption spectra including excitons. We highlight cancellations that make many-body effects in the spectra very robust with respect to structural modifications, while the band structure undergoes significant changes and the nature of the excitations may also be affected. Here, this yields insight concerning the structure-properties relations that are crucial for the use of V 2 O 5 as energy storage material, and more generally, that may be used to optimize the analysis and the calculation of electronic spectra in complex materials.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Post-closure Nuclear Criticality Safety Evaluations for Disposition of Criticality Control Overpacks at the Waste Isolation Pilot Plant

The Waste Isolation Pilot Plant (WIPP) is a geological repository in southern New Mexico that provides for disposal of transuranic (TRU) wastes from atomic energy defense activities. The Sandia National Laboratories (Sandia) Report, Consideration of Nuclear Criticality When Disposing of Transuranic Waste at the Waste Isolation Pilot Plant, addresses nuclear criticality safety based on the projected inventory characteristics for the initial compliance certification application of WIPP in 1996. As the inventory, waste forms, and disposal package designs change, revised or new analyses are necessary to demonstrate acceptability for these configurations within the WIPP safety basis and compliance with 10,000-year post-closure standards of the US Environmental Protection Agency (EPA). Saylor and Scaglione evaluated criticality control overpacks (CCOs) in 2017 based on conservative assumptions for post-closure repository structural conditions with resulting effects on containers and container spacing, The Saylor and Scaglione evaluation of CCOs addressed a single waste configuration that represents the Surplus Plutonium Disposition Program’s dilute and dispose waste form and composition. This initial CCO study demonstrated that 50 grams of boron carbide (B 4 C) per CCO is sufficient to ensure post-closure criticality safety based on a well-mixed waste composition, and Oak Ridge National Laboratory (ORNL) subsequently determined that this amount of B 4 C does not require constraints on moisture or plastic present as moderator. The Saylor and Scaglione analysis conservatively assumes repository room closure that eliminates all space between fissile gram equivalent (FGE) 239 Pu masses. The close-packed array was selected based on limited availability of repository salt creep modeling results at that time. In 2019, Brickner provided additional evaluations for pipe overpack containers (POCs), building on the conservative basis provided by Saylor and Scaglione. Brickner’s 2019 analysis made use of new geomechanical data for post-closure spacing that rely on advances in repository modeling as documented in the work by Reedlunn and Bean. This current CCO evaluation for generic waste materials expands on earlier work performed at ORNL and includes evaluation of CCOs across a much broader range of possible waste compositions and geometries. This evaluation is intended to provide input for the required feature, event and process (FEP) screening to determine if post-closure criticality must be included as an event in the 10,000-year regulatory evaluation. As such, the approach to modeling post-closure criticality presented in this report has been coordinated with the Sandia team responsible for FEP screening. The resulting analysis supports disposition of fissile materials in the CCO containing up to 380 FGE 239 Pu and expands conditions acceptable for disposal of fissile material in CCOs. This evaluation builds on the methodology of Saylor and Scaglione and Brickner, using the most recently available geomechanical data for CCO spacing under salt creep compaction scenarios provided by Reedlunn and Bean. The broad range of fissile material configurations analyzed in this report are intended to account for configurations that may occur during the post-closure disposal time period, and it also includes waste configurations that are not physically possible to support analysis of conditions that influence neutron fluence.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2024

PyFaults: A Python Toolkit for Stacking Fault Screening

PyFaults is an open-source Python library designed to model stacking fault disorder in crystalline materials and qualitatively assess the characteristic selective broadening effects in powder X-ray diffraction (PXRD). Here, the main capabilities of PyFaults are presented, including unit cell and supercell model construction, PXRD pattern calculation, assessment against experimental PXRD, and methods for rapid screening of candidate models within a set of possible stacking vectors and fault occurrence probabilities. This program aims to serve as a computationally inexpensive tool for identifying and screening potential stacking fault models in materials with planar disorder. Three diverse case studies, involving GaN, Li2MnO3 and Li3YCl6, are presented to illustrate the program functionality across a range of structure types and stacking fault modalities.

MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Screening and Discovery of Metal Compound Active Sites for Strong and Selective Adsorption of N 2 in Air

Photocatalytic nitrogen fixation has the potential to provide a greener route for producing nitrogen-based fertilizers under ambient conditions. Computational screening is a promising route to discover new materials for the nitrogen fixation process, but requires identifying “descriptors” that can be efficiently computed. In this work, we argue that selectivity toward the adsorption of molecular nitrogen and oxygen can act as a key descriptor. A catalyst that can selectively adsorb nitrogen and resist poisoning of oxygen and other molecules present in air has the potential to facilitate the nitrogen fixation process under ambient conditions. Here we provide a framework for active site screening based on multifidelity density functional theory (DFT) calculations for a range of metal oxides, oxyborides, and oxyphosphides. The screening methodology consists of initial low-fidelity fixed geometry calculations and a second screening in which more expensive geometry optimizations were performed. The approach identifies promising active sites on several TiO 2 polymorph surfaces and a VBO 4 surface, and the full nitrogen reduction pathway is studied with the BEEF-vdW and HSE06 functionals on two active sites. The findings suggest that metastable TiO 2 polymorphs may play a role in photocatalytic nitrogen fixation, and that VBO 4 may be an interesting material for further studies.

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

Rapid discovery of high hardness multi-principal-element alloys using a generative adversarial network model

Multi-principal element alloys (MPEAs) continue to gain research prominence due to their promising high-temperature microstructural and mechanical properties. Recently, machine learning (ML) and materials informatics have been used extensively for screening MPEAs, however, most of these efforts were focused on constructing classification and regression models for predicting phase stability and mechanical properties of known compositions. These approaches may accelerate the screening process but optimizing new compositions with desirable properties within a practical time frame from an infinitely large design space of MPEA systems remains a grand challenge. To tackle this composition optimization challenge, a generative adversarial network coupled with a neural-network ML model was utilized to design MPEAs by filtering compositions that have high hardness. Even in a high-dimensional space with 18 elements as descriptors, the ML model was able to generate optimized compositions from which one composition was found to have 10% higher hardness (941 HV) than the maximum in the training data (857 HV). Density-functional theory was used to provide thermodynamic and electronic insights to higher hardness of the new MPEA found. The present work can optimize compositions from a wide design space of 18 elements (including W, Ta and Nb) that presents an opportunity to synthesize new compositions for applications ranging from corrosion-resistant alloys to nuclear materials. Here the findings suggest that generative ML can greatly accelerate materials discovery by identifying novel compositions, which can serve as a data-informed tool to guide experiments.

36 MATERIALS SCIENCE↗