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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 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Reactive flash sintering of TiZrN and TiAlN ternary metal nitrides

Here, this study demonstrated the reactive flash sintering (RFS) for two powder mixtures: TiN-ZrN (both conducting) and AlN-TiN (TiN conducting but AlN insulating), targeting ternary metal nitrides (TMN) of Ti 0.5 Zr 0.5 N and Ti 0.5 Al 0.5 N, respectively. A constant volage and pressure (e.g., 8 V DC, ∼15 MPa) at room temperature triggered the flash (current density up to 27 A/mm 2 ) without pre-heating, and the entire RFS process finished in a few minutes. For TiZrN, the flash was instantaneous whereas for TiAlN, there was a long incubation before the flash followed by a quick and dramatic flash. Both conventional ex situ XRD and in situ synchrotron study had been carried out. They showed a uniform Ti 0.57 Zr 0.43 N solution formed in RFS and persisted upon cooling, while (Ti, Al) N solid solution formed at high temperature was not stable and likely went through a very quick phase separation in the cooling process. The final products from RFS had been characterized using SEM/EDS for microstructure. Both TiZrN and TiAlN were dense. Distribution of Ti, Zr, and N was uniform for TiZrN; for TiAlN, Ti and N distribution was uniform, while association of Al with oxygen was observed. TGA-DSC revealed the onset oxidation temperature for TiZrN was comparable to TiN and ZrN, while it was higher by ∼200 °C for TiAlN, likely due to the formation of Al 2 O 3 . In terms of mechanical properties such as hardness or fracture toughness, forming a single-phase solid solution (like TiZrN) does not offer obvious benefits. while large grain size from RFS seemed to be unfavorable. Future optimization of RFS condition and in-depth study by both experiments and simulation are needed to fully understand the composition-processing-structure-property relationships for such TMN from the reactive flash sintering process.

Flash Sintering↗
DOE OSTI2022

Tuning the Product Distribution of Acetylene Dimerization through Bimetallic Metal–Organic Framework-Supported Nanoporous Systems

Metal-organic frameworks (MOFs) are receiving increased attention due to their well-defined structures that allow the determination of structure-property relationships. MOFs have been used as heterogeneous catalyst supports in a variety of fashions including for confinement of metal nanoparticles, which have demonstrated enhanced resistance to aggregation, a common issue in amorphous metal oxide supports. Cu and In catalysts were installed in the Zr-based MOF NU-907, being confined within the nanoporous structure. The Cu catalyst is known to, under various conditions, either selectively hydrogenate acetylene to ethylene or generate C4 products such as butenes and 1,3-butadiene, an important feedstock for rubber and adhesives. The addition of indium to the Cu catalyst is intended to serve as a promoter to produce C4 products by decreasing the surface coverage of copper while still allowing for C-C coupling. When employed for acetylene dimerization, InCu-NU-907 shows slightly decreased C4 production overall but enhanced 1,3-butadiene production compared to all other catalysts studied herein. These catalysts were thoroughly characterized by a range of techniques to confirm structural integrity and porosity and probe the nature of the interactions of indium with the Cu nanoparticle active site.

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

Development of interatomic potential for Al–Tb alloys using a deep neural network learning method

An interatomic potential for the Al–Tb alloy around the composition of Al 90 Tb 10 is developed using the deep neural network (DNN) learning method. The atomic configurations and the corresponding total potential energies and forces on each atom obtained from ab initio molecular dynamics (AIMD) simulations are collected to train a DNN model to construct the interatomic potential for the Al–Tb alloy. Here we show that the obtained DNN model can well reproduce the energies and forces calculated by AIMD simulations. Molecular dynamics (MD) simulations using the DNN interatomic potential also accurately describe the structural properties of the Al 90 Tb 10 liquid, such as partial pair correlation functions (PPCFs) and bond angle distributions, in comparison with the results from AIMD simulations. Furthermore, the developed DNN interatomic potential predicts the formation energies of the crystalline phases of the Al–Tb system with an accuracy comparable to ab initio calculations. The structure factors of the Al 90 Tb 10 metallic liquid and glass obtained by MD simulations using the developed DNN interatomic potential are also in good agreement with the experimental X-ray diffraction data. The development of short-range order (SRO) in the Al 90 Tb 10 liquid and the undercooled liquid is also analyzed and three dominant SROs, i.e., Al-centered distorted icosahedron (DISICO) and Tb-centered ‘3661’ and ‘15551’ clusters, respectively, are identified.

36 MATERIALS SCIENCE↗
DOE OSTI2025

LiPF 6 under pressure: a symmetry-mode-based structural analysis

The lithium salt LiPF 6 is a key component in commercial lithium-ion battery (LIB) electrolytes. However, its structural properties under pressure remain unexplored, despite increasing interest in the effect of pressure on electrolyte behaviour, and a growing interest in high pressure synthesis of new Li-hexafluoride cathode materials. Here, we present the first high pressure neutron diffraction study of solid LiPF 6 up to 7.4 GPa. Refinements reveal that the ambient rhombohedral structure (space group R$\overline{3}$) remains stable throughout, with pressure accommodated primarily by tilting of the corner-shared LiF 6 and PF 6 octahedra and contraction of the LiF 6 units. In conclusion, this study establishes a foundation for understanding the phase behaviour of Li-based hexafluorides under extreme conditions.

25 ENERGY STORAGE↗
DOE OSTI2022

The crystal structure of Fe2S at 90 GPa based on single-crystal X-ray diffraction techniques

The Fe-S system was explored in a laser-heated diamond-anvil cell at 89(2) GPa and 2380(120) K to better understand the phase stability of Fe 2 S. Upon temperature quenching, crystallites of Fe 2 S were identified, and their structure was investigated using single-crystal X-ray diffraction techniques. At these conditions, Fe 2 S adopts the C23 structure (anti-PbCl 2 , Co 2 P) with space group Pnma (Z = 4). This structure consists of columns of corner-sharing, FeS 4 tetrahedra, and columns of edge-sharing FeS 5 square pyramids linked along edges in the b direction. Sulfur is in ninefold coordination with Fe. This work marks the first high-pressure structural solution and refinement of Fe 2 S synthesized in a multigrain Fe+FeS sample at 90 GPa and 2400 K and establishes the stability of C23 Fe 2 S at these conditions. A previous powder diffraction study reports an orthorhombic Fe 2 S phase with a C37, Co 2 Si-like unit cell above 190 GPa. A C23–C37 structural transition is inferred to explain the previously observed unit-cell parameters at higher pressures and temperatures. These results highlight the utility of applying single-crystal X-ray diffraction techniques to high P-T multigrain samples to explore the structural properties of iron-rich phases in Earth and planetary cores.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Modulating Iron Crystals with Lattice Chalcophile‐Siderophile Elements for Selective Dechlorinations Over Hydrogen Evolution

Selective dechlorination of organic chlorides over hydrogen evolution reaction (HER) remains a challenge because of their coincidence. Nanoscale zerovalent iron (nFe 0 ) draws a promising picture of in situ groundwater dechlorination, but its indiscriminate reactivity limits the application. Here, nFe 0 crystals are designed with electron shuttles and improved hydrophobic nature based on elemental chalcophile-siderophile characteristics, where chalcophile-siderophile S served as a bridge to allow impregnating nFe 0 crystals with weakly siderophile and strongly chalcophile Cu. Even impregnations of lattice chalcophile-siderophile elements into the nFe 0 crystals are evidenced at both intraparticle and individual-particle levels. The modulated Fe microenvironment and physicochemical properties broke the reactivity-selectivity-longevity-stability trade-off. Compared to nFe 0 , superhydrophobic Cu─S─nFe 0 with lattice expansion promoted dechlorination by 20-fold but inhibited HER by 150-fold, utilizing ≈80–100% electrons from the Fe 0 reservoir. This work demonstrates the concept of engineering nFe 0 lattice with tunable structure-property relationships, mimicking reductive dehalogenases by selectively interacting with halocarbon functional groups for efficient dehalogenation and sustainable groundwater remediation.

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

Emerging Opportunities in Manufacturing Bulk Soft-Magnetic Alloys for Energy Applications: A Review

Abstract Soft-magnetic alloys exhibit exceptional functional properties that are beneficial for a variety of electromagnetic applications. These alloys are conventionally manufactured into sheet or bar forms using well-established ingot metallurgy practices that involve hot- and cold-working steps. However, recent developments in process metallurgy have unlocked opportunities to directly produce bulk soft-magnetic alloys with improved, and often tailorable, structure–property relationships that are unachievable conventionally. The emergence of unconventional manufacturing routes for soft-magnetic alloys is largely motivated by the need to improve the energy efficiency of electromagnetic devices. In this review, literature that details emerging manufacturing approaches for soft-magnetic alloys is overviewed. This review covers (1) severe plastic deformation, (2) recent advances in melt spinning, (3) powder-based methods, and (4) additive manufacturing. These methods are discussed in comparison with conventional rolling and bar processing. Perspectives and recommended future research directions are also discussed.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Solvent Transport in Disordered and Dynamic Membrane Pores: Implications for Reverse Osmosis and Nanofiltration Membranes

Pressure-driven separations with nanoporous membranes, such as reverse osmosis and nanofiltration, play a vital role in addressing water scarcity and enabling resource recovery. Understanding water or solvent transport in membrane pores is essential for advancing membrane separation technologies. A key question in transport modeling is to establish a relationship between solvent permeability and membrane porous structure properties, such as porosity or pore size. The nano- and subnanometer pores in polymeric membranes such as reverse osmosis and nanofiltration membranes are highly tortuous and dynamically connected, which challenges the conventional methods of transport modeling. This study addresses this challenge by developing a theoretical framework to describe solvent transport through membranes with dynamic and disordered porous structure. Specifically, we propose a lattice model to describe the pore network, while preserving the viscous nature of solvent permeation. We further establish a relationship between solvent permeability and membrane porosity or pore size, which is validated by molecular dynamics simulations and experimental data. By integrating this relationship into the solution-friction model, we define pore connectivity and local friction coefficient to quantify the impact of pore structure on solvent permeability. Our analysis highlights the dominant influence of pore connectivity on the permeability of reverse osmosis and nanofiltration membranes, particularly when the pore size approaches the dimensions of solvent molecules. Overall, this study provides critical insights into water and solvent transport mechanisms in nanoporous membranes, opening the door for strategies to substantially enhance membrane performance.

membranes↗
DOE OSTI2024

Effect of Pressure on Crystal Structure and Phonon Density of States of FeSi

The strongly correlated material FeSi displays several unusual thermal, magnetic, and structural properties under varying pressure-temperature (P-T) conditions. It is a potential thermoelectric alloy and a material with several geochemical implications as a possible constituent at the Earth's core-mantle boundary (CMB). Previous theoretical studies predicted a pressure-induced B20-B2 transition at ambient temperature below 40 GPa; however, experimentally, the structural transition is observed only at high P-T conditions. In this study, we have performed high-pressure powder X-ray diffraction (XRD) up to 90 GPa and Nuclear Resonant Inelastic X-ray Scattering (NRIXS) measurements up to 120 GPa to understand the phase stability and lattice dynamics. Our study provides evidence for a non-hydrostatic stress-induced B20-B2 transition in FeSi at around 36 GPa. We deduced the Fe partial phonon density of states (PDOS) and thermal parameters from NRIXS measurements up to 120 GPa and compared them with density functional theory (DFT) calculations. Furthermore, the computations show pressure-induced metallization and the band gap closing around 12 GPa.

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

TransPolymer: a Transformer-based language model for polymer property predictions

Accurate and efficient prediction of polymer properties is of great significance in polymer design. Conventionally, expensive and time-consuming experiments or simulations are required to evaluate polymer functions. Recently, Transformer models, equipped with self-attention mechanisms, have exhibited superior performance in natural language processing. However, such methods have not been investigated in polymer sciences. Herein, we report TransPolymer, a Transformer-based language model for polymer property prediction. Our proposed polymer tokenizer with chemical awareness enables learning representations from polymer sequences. Rigorous experiments on ten polymer property prediction benchmarks demonstrate the superior performance of TransPolymer. Moreover, we show that TransPolymer benefits from pretraining on large unlabeled dataset via Masked Language Modeling. Experimental results further manifest the important role of self-attention in modeling polymer sequences. We highlight this model as a promising computational tool for promoting rational polymer design and understanding structure-property relationships from a data science view.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Rapid data acquisition and machine learning-assisted composition design of functionally graded alloys via wire arc additive manufacturing

Abstract The lack of high-quality datasets in materials science hinders artificial intelligence (AI)-driven alloy design. To address this challenge, wire arc additive manufacturing (WAAM) was employed to fabricate graded alloys, generating extensive data for machine learning (ML)-assisted property prediction. ML models were developed using high-throughput experiments, computational models, and genetic algorithm to optimize feature selection, successfully predicting hardness and porosity. The ML model demonstrated its efficacy by designing a gradient alloy with enhanced properties. However, scaling up revealed uncertainties in tensile property and porosity due to differences in size and thermal conditions between the designed alloy build and the gradient print used to construct the ML model. This underscores the need for uncertainty quantification and process optimization in WAAM-driven alloy design. Our work advances AI-integrated additive manufacturing, offering a rapid approach to exploring process–structure–property relationships and accelerating materials development.

Wang, Xin↗
DOE OSTI2025

Advanced Interactive 3D Visualization Tool for Customizable Analyses of Tomography Datasets in Material Science

Current methods for visualizing and analyzing 3D tomography datasets in materials science often lack the interactivity and depth required for detailed structural insights. This limitation restricts a researchers' ability to accurately interpret complex data, which is critical for advancing material innovations and understanding structural properties. To address this issue, we have developed a novel, web-based interactive 3D visualization and analysis tool from the Trame framework that offers customizable features to enhance data interpretability. The tool allows users to adjust parameters such as visible range, slice planes, data rotation, and layering, providing a more detailed and dynamic view of complex structures. Its user-friendly web interface increases the accessibility and ease of use for both novice and experienced researchers, to visualize large volumetric datasets. The tool supports a diverse range of data formats, making it versatile for various research applications. Unique capabilities include real-time data manipulation, automated feature detection, context-sensitive feedback, and real-time volume calculations and distributions per sliced region or layer, alongside the ability to quickly generate high-quality screenshots and videos for presentations and reports. These advancements offer a comprehensive solution for enhanced 3D data exploration, significantly improving the analysis process and communication of results in materials science.

36 - MATERIALS SCIENCE↗
DOE OSTI2022

Feedstock design for quality biomaterials

Feedstock design is crucial for lignocellulosic biomass use. Current strategies for feedstock design cannot be readily applied to improve the quality of biomass-based materials, limiting the sustainability and economics of lignocellulosic biorefineries. Recent studies have advanced the understanding of biomass structure–property relationships and discovered several characteristics, such as molecular weight, uniformity, linkage profile, and functional groups, that are critical for manufacturing diverse quality biomaterials. Further, these discoveries call for fundamentally different strategies for feedstock development. Such strategies need to rediscover the roles of monolignol biosynthesis enzymes and leverage lignin polymerization enzymes to achieve precise control of lignin molecular structure. These innovations could transform biomass into feedstock for high-quality biomaterials, addressing essential environmental challenges and empowering the bioeconomy.

09 BIOMASS FUELS↗
DOE OSTI2025

Ferroelectric phase transition in group-IV monochalcogenides from an equivariant machine learned force field

Group-IV monochalcogenides are a class of layered ferroelectric semiconductors that have demonstrated spontaneous intrinsic polarization above room temperature. Here, in this study, we use the multi-atomic cluster expansion (MACE) machine learning architecture to train and test a force field capable of modeling the structural properties and second-order ferroelectric-to-paraelectric phase transition in a Group-IV monochalcogenide, GeSe. The model captures the double-well potential energy surface associated with the onset of macroscopic polarization in bulk GeSe within 12.5 meV/atom, as well as near-equilibrium properties like the phonon dispersion. The development of this quantitatively accurate force field enables long-time molecular dynamics simulations, which predict the critical temperature of the ferroelectric-to-paraelectric phase transition in bulk GeSe to be T c = 600 K. This study demonstrates the capabilities of equivariant force-fields to accurately describe phenomena associated with structural symmetry breaking.

ferroelectricity↗
DOE OSTI2021

Convergence of micro-geochemistry and micro-geomechanics towards understanding proppant shale rock interaction: A Caney shale case study in southern Oklahoma, USA

As a direct outcome of economic development coupled with an increase in population, global energy demand will continue to rise in the coming decades. Although renewable energy sources are increasingly investigated for optimal production, the immediate needs require focus on energy sources that are currently available and reliable, with a minimal environmental impact; the efficient exploration and production of unconventional hydrocarbon resources is bridging the energy needs and energy aspirations, during the current energy transition period. The main challenges are related to the accurate quantification of the critical rock properties that influence production, their heterogeneity and the multiscale driven physico-chemical nature of rock–fluid interactions. A key feature of shale reservoirs is their low permeability due to dominating nanoporosity of the clay-rich matrix. As a means of producing these reservoirs in a cost-effective manner, a prerequisite is creation of hydraulic fracture networks capable of the highest level of continued conductivity. Fracturing fluid chemical design, formation brine geochemical composition, and rock mineralogy all contribute to swelling-induced conductivity damage. The Caney Shale is an organic-rich, often calcareous mudrock. Many studies have examined the impact that clay has on different kinds of shale productivity but there is currently no data reported on the Caney Shale in relation to horizontal drilling; all reported data on the Caney Shale is on vertical wells which are shallow, compared to an emerging play that is at double the depth. Here, in this work we develop geochemical–geomechanical integration of rock properties at micro-and nanoscales that can provide insights into the potential proppant embedment and its mitigation. The novel methodology amalgamates the following: computed X-ray tomography, scanning electron microscopy, energy dispersive spectroscopy, micro-indentation, and Raman spectroscopy techniques. Here, our results show that due to the multiscale heterogeneity in the Caney Shale, these geochemical and structural properties translate into a variation in mechanical properties that will impact interaction between the proppant and the host shale rock.

03 NATURAL GAS↗
DOE OSTI2021

Nucleation and growth of blue phase liquid crystals on chemically-patterned surfaces: a surface anchoring assisted blue phase correlation length

In condensed matter, the correlation length is an essential parameter which describes the distance over which a material maintains its structural properties. In liquid crystals, for the orientational order parameter this characteristic length is of the order of nanometers, while in solid crystals it can extend to macroscopic length scales. Here, we report the measurement of the correlation length, or persistence length, of the crystallographic orientation of blue phases (BPs)—chiral liquid crystals with long-range 3D-crystalline structures and submicron-sized lattice-parameters. These materials exhibit phase transformations that have been identified as the liquid analog of crystal-crystal martensitic transformations. In this work, we use liquid epitaxial growth to achieve spontaneous BP-crystal nucleation and subsequent growth. Specifically, we design patterned substrates made of a binary array of regions with different liquid crystal anchoring, which facilitate a uniform nucleation and growth of BP-crystals with (100)-lattice orientation and a simple cubic symmetry. Furthermore, our results indicate that this simple cubic BP, the so-called blue phase II (BPII), forms first on the patterned surface, thereby propagating the growth of domains in directions parallel or perpendicular to the patterned regions. These results are used to understand the emergence of a surface anchoring assisted BPII-correlation length, taken as the distance over which the BP preserves its lattice orientation, as a function of time and pattern characteristics. We found that BPII single crystals can be achieved on patterned regions whose lateral dimensions are equal to or larger than 10 μm, consistent with our measurements of the BPII-correlation length. This newly acquired understanding of the role of surfaces on the formation of BPs is then used to design processes that permit formation of macroscopically large mono-domain, single-crystal BPs by relying on significantly reduced patterned areas (only part of the area is patterned), a feature that could benefit the applications of this intriguing class of materials.

correlation length↗
DOE OSTI2021

Quasiparticle electronic structure of phthalocyanine:TMD interfaces from first-principles GW

Interfaces formed between monolayer transition metal dichalcogenides and (metallo)phthalocyanine molecules are promising in energy applications and provide a platform for studying mixed-dimensional molecule-semiconductor heterostructures in general. An accurate characterization of the frontier energy level alignment at these interfaces is key in the fundamental understanding of the charge transfer dynamics between the two photon absorbers. Here, we employ the first-principles substrate screening GW approach to quantitatively characterize the quasiparticle electronic structure of a series of interfaces: metal-free phthalocyanine (H 2 Pc) adsorbed on monolayer MX 2 (M = Mo, W; X = S, Se) and zinc phthalocyanine (ZnPc) adsorbed on MoX 2 (X = S, Se). Furthermore, we reveal the dielectric screening effect of the commonly used α-quartz (SiO 2 ) substrate on the H 2 Pc:MoS 2 interface using the dielectric embedding GW approach. Our calculations furnish a systematic set of GW results for these interfaces, providing the structure–property relationship across a series of similar systems and benchmarks for future experimental and theoretical studies.

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

Exploring solvation structure and transport behavior for rational design of advanced electrolytes for next generation of lithium batteries

The efficacy of electrolytes significantly affects battery performance, leading to the development of several strategies to enhance them. Despite this, the understanding of solvation structure remains inadequate. It is imperative to understand the structure–property–performance relationship of electrolytes using diverse techniques. Here, this review explores the recent advancements in electrolyte design strategies for high capacity, high-voltage, wide-temperature, fast-charging, and safe applications. To begin, the current state-of-the-art electrolyte design directions are comprehensively reviewed. Subsequently, advanced techniques and computational methods used to understand the solvation structure are discussed. Additionally, the importance of high-throughput screening and advanced computation of electrolytes with the help of machine learning is emphasized. Finally, future horizons for studying electrolytes are proposed, aimed at improving battery performance and promoting their application in various fields by enhancing the microscopic understanding of electrolytes.

25 ENERGY STORAGE↗