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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Pore‐Scale Modeling of Reactive Transport with Coupled Mineral Dissolution and Precipitation

Abstract We present a new pore‐scale model for multicomponent advective‐diffusive transport with coupled mineral dissolution and precipitation. Both dissolution and precipitation are captured simultaneously by introducing a phase transformation vector field representing the direction and magnitude of the overall phase change. An effective viscosity model is adopted in simulating fluid flow during mineral dissolution‐precipitation that can accurately capture the velocity field without introducing any empirical parameters. The proposed approach is validated against analytical solutions and interface tracking simulations in simplified structures. After validation, the proposed approach is employed in modeling realistic rocks where mineral dissolution and precipitation are dominant at different locations. We have identified three regimes for mineral dissolution‐precipitation coupling: (a) compact dissolution‐precipitation where dissolution is dominant near the inlet and precipitation is dominant near the outlet, (b) wormhole dissolution with clustered precipitation where dissolution generates wormholes in the main flow paths and precipitation clogs the secondary flow paths, and (c) dissolution dominant where all solid grains are gradually dissolved. In the three regimes, the proposed approach provides reliable porosity‐permeability relationships that cannot be described well by traditional macroscale models. We find that the permeability can increase while the overall porosity decreases when the main flow paths are expanded by dissolution and adjacent pore spaces are clogged by precipitation.

58 GEOSCIENCES↗
DOE OSTI2016

Efficiency limits for photoelectrochemical water-splitting

Theoretical limiting efficiencies have a critical role in determining technological viability and expectations for device prototypes, as evidenced by the photovoltaics community’s focus on detailed balance. However, due to their multicomponent nature, photoelectrochemical devices do not have an equivalent analogue to detailed balance, and reported theoretical efficiency limits vary depending on the assumptions made. Here we introduce a unified framework for photoelectrochemical device performance through which all previous limiting efficiencies can be understood and contextualized. Ideal and experimentally realistic limiting efficiencies are presented, and then generalized using five representative parameters—semiconductor absorption fraction, external radiative efficiency, series resistance, shunt resistance and catalytic exchange current density—to account for imperfect light absorption, charge transport and catalysis. Finally, we discuss the origin of deviations between the limits discussed herein and reported water-splitting efficiencies. This analysis provides insight into the primary factors that determine device performance and a powerful handle to improve device efficiency.

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

Direct laser writing of volumetric gradient index lenses and waveguides

Abstract Direct laser writing (DLW) has been shown to render 3D polymeric optical components, including lenses, beam expanders, and mirrors, with submicrometer precision. However, these printed structures are limited to the refractive index and dispersive properties of the photopolymer. Here, we present the subsurface controllable refractive index via beam exposure (SCRIBE) method, a lithographic approach that enables the tuning of the refractive index over a range of greater than 0.3 by performing DLW inside photoresist-filled nanoporous silicon and silica scaffolds. Adjusting the laser exposure during printing enables 3D submicron control of the polymer infilling and thus the refractive index and chromatic dispersion. Combining SCRIBE’s unprecedented index range and 3D writing accuracy has realized the world’s smallest (15 µm diameter) spherical Luneburg lens operating at visible wavelengths. SCRIBE’s ability to tune the chromatic dispersion alongside the refractive index was leveraged to render achromatic doublets in a single printing step, eliminating the need for multiple photoresins and writing sequences. SCRIBE also has the potential to form multicomponent optics by cascading optical elements within a scaffold. As a demonstration, stacked focusing structures that generate photonic nanojets were fabricated inside porous silicon. Finally, an all-pass ring resonator was coupled to a subsurface 3D waveguide. The measured quality factor of 4600 at 1550 nm suggests the possibility of compact photonic systems with optical interconnects that traverse multiple planes. SCRIBE is uniquely suited for constructing such photonic integrated circuits due to its ability to integrate multiple optical components, including lenses and waveguides, without additional printed supports.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Topologically guided tuning of Zr-MOF pore structures for highly selective separation of C6 alkane isomers

Effective separation of alkane isomers is a critical step in the petroleum refining process to produce gasoline with high octane rating. Current technology relies on energy intensive distillation method. Adsorptive separation using porous solids offers an alternative route that is more cost effective, lower energy and higher efficiency. Guided by topology-directed design and synthesis, we have developed a series of zirconium-based metal-organic frameworks with optimized pore structure for efficient separation of C6 alkane isomers, Zr-bptc adsorbs a large amount of n-hexane while excluding branched isomers. The n-hexane uptake is ~70% higher than that of a benchmark adsorbent, zeolite-5A. A derivative structure, Zr-abtc, is capable of discriminating all three C6 isomers with a high separation factor for 3-methylpentane over 2,3-dimethylbutane. This property is important for producing gasoline with further improved quality.

36 MATERIALS SCIENCE↗
DOE OSTI2018

High-throughput discovery of organic cages and catenanes using computational screening fused with robotic synthesis

Supramolecular synthesis is a powerful strategy for assembling complex molecules, but to do this by targeted design is challenging. This is because multicomponent assembly reactions have the potential to form a wide variety of products. High-throughput screening can explore a broad synthetic space, but this is inefficient and inelegant when applied blindly. Here we fuse computation with robotic synthesis to create a hybrid discovery workflow for discovering new organic cage molecules, and by extension, other supramolecular systems. A total of 78 precursor combinations were investigated by computation and experiment, leading to 33 cages that were formed cleanly in one-pot syntheses. Comparison of calculations with experimental outcomes across this broad library shows that computation has the power to focus experiments, for example by identifying linkers that are less likely to be reliable for cage formation. Screening also led to the unplanned discovery of a new cage topology—doubly bridged, triply interlocked cage catenanes.

Science & Technology - Other Topics↗
DOE OSTI2020

Imaging the kinetics of anisotropic dissolution of bimetallic core–shell nanocubes using graphene liquid cells

Chemical design of multicomponent nanocrystals requires atomic-level understanding of reaction kinetics. Here, we apply single-particle imaging coupled with atomistic simulation to study reaction pathways and rates of Pd@Au and Cu@Au core-shell nanocubes undergoing oxidative dissolution. Quantitative analysis of etching kinetics using in situ transmission electron microscopy (TEM) imaging reveals that the dissolution mechanism changes from predominantly edge-selective to layer-by-layer removal of Au atoms as the reaction progresses. Dissolution of the Au shell slows down when both metals are exposed, which we attribute to galvanic corrosion protection. Morphological transformations are determined by intrinsic anisotropy due to coordination-number-dependent atom removal rates and extrinsic anisotropy induced by the graphene window. Our work demonstrates that bimetallic coreshell nanocrystals are excellent probes for the local physicochemical conditions inside TEM liquid cells. Furthermore, single-particle TEM imaging and atomistic simulation of reaction trajectories can inform future design strategies for compositionally and architecturally sophisticated nanocrystals.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2021

Strength can be controlled by edge dislocations in refractory high-entropy alloys

Energy efficiency is motivating the search for new high-temperature (high-T) metals. Some new body-centered-cubic (BCC) random multicomponent “high-entropy alloys (HEAs)” based on refractory elements (Cr-Mo-Nb-Ta-V-W-Hf-Ti-Zr) possess exceptional strengths at high temperatures but the physical origins of this outstanding behavior are not known. Here we show, using integrated in-situ neutron-diffraction (ND), high-resolution transmission electron microscopy (HRTEM), and recent theory, that the high strength and strength retention of a NbTaTiV alloy and a high-strength/low-density CrMoNbV alloy are attributable to edge dislocations. This finding is surprising because plastic flows in BCC elemental metals and dilute alloys are generally controlled by screw dislocations. We use the insight and theory to perform a computationally-guided search over 10 7 BCC HEAs and identify over 10 6 possible ultra-strong high-T alloy compositions for future exploration.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Selective cobalt and nickel electrodeposition for lithium-ion battery recycling through integrated electrolyte and interface control

Abstract Molecularly-selective metal separations are key to sustainable recycling of Li-ion battery electrodes. However, metals with close reduction potentials present a fundamental challenge for selective electrodeposition, especially for critical elements such as cobalt and nickel. Here, we demonstrate the synergistic combination of electrolyte control and interfacial design to achieve molecular selectivity for cobalt and nickel during potential-dependent electrodeposition. Concentrated chloride allows for the speciation control via distinct formation of anionic cobalt chloride complex (CoCl 4 2- ), while maintaining nickel in the cationic form ([Ni(H 2 O) 5 Cl] + ). Furthermore, functionalizing electrodes with a positively charged polyelectrolyte (i.e., poly(diallyldimethylammonium) chloride) changes the mobility of CoCl 4 2- by electrostatic stabilization, which tunes cobalt selectivity depending on the polyelectrolyte loading. This strategy is applied for the multicomponent metal recovery from commercially-sourced lithium nickel manganese cobalt oxide electrodes. We report a final purity of 96.4 ± 3.1% and 94.1 ± 2.3% for cobalt and nickel, respectively. Based on a technoeconomic analysis, we identify the limiting costs arising from the background electrolyte, and provide a promising outlook of selective electrodeposition as an efficient separation approach for battery recycling.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Toward the design of ultrahigh-entropy alloys via mining six million texts

Abstract It has long been a norm that researchers extract knowledge from literature to design materials. However, the avalanche of publications makes the norm challenging to follow. Text mining (TM) is efficient in extracting information from corpora. Still, it cannot discover materials not present in the corpora, hindering its broader applications in exploring novel materials, such as high-entropy alloys (HEAs). Here we introduce a concept of “context similarity" for selecting chemical elements for HEAs, based on TM models that analyze the abstracts of 6.4 million papers. The method captures the similarity of chemical elements in the context used by scientists. It overcomes the limitations of TM and identifies the Cantor and Senkov HEAs. We demonstrate its screening capability for six- and seven-component lightweight HEAs by finding nearly 500 promising alloys out of 2.6 million candidates. The method thus brings an approach to the development of ultrahigh-entropy alloys and multicomponent materials.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A blueprint for biomolecular condensation driven by bacterial microcompartment encapsulation peptides

Bacterial microcompartments are protein organelles with diverse metabolic capabilities. Their functional diversity is determined by an enzymatic core that is sequestered within a structurally conserved protein shell architecture. Segregation of protein cargo into the bacterial microcompartment is enabled by encapsulation peptides, which are short helical domains fused to core proteins through a disordered linker. Here, we investigate how encapsulation peptides drive multicomponent cargo assembly into biomolecular condensates. In vitro experiments supported by molecular dynamics simulations demonstrate the importance of both conserved hydrophobic packing and electrostatic interactions in stabilizing trimeric encapsulation peptide bundles. Topological rearrangements of encapsulation peptide domains can drive programmable liquid- or gel-like partitioning in vitro and in vivo. This partitioning is found to be encapsulation peptide-specific, modular, and can co-assemble at least three fluorescent reporters. In summary, we describe the molecular features necessary to drive biomolecular condensation using a widespread peptide tag. This work can serve as a blueprint for implementing encapsulation peptide biotechnology across diverse applications.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Thermally driven surface phase separation in intermetallic alloys

Intermetallic compounds are widely recognized for their high-temperature phase stability and resistance to composition and structural changes. However, we reveal athermally activated bulk-to-surface mass exchange mechanism that drives surface phase separation, resulting in the formation of surface precipitates with distinct composition andstructure from the bulk matrix. Using the archetypal β-NiAl system, we show that asymmetries in vacancy formation energies between Ni and Al atoms induce preferential Ni segregation to the surface, forming Ni-rich γ'-Ni 3 Al precipitates. By integrating in-situ electron microscopy, synchrotron X-ray absorption spectroscopy and first-principles computational modeling, we establish a direct mechanistic connection between bulk thermal defect dynamics, surface compositional evolution, and phase segregation behavior. This bulk-surface coupling mechanism can be a driver of surface phase separation in multicomponent alloys under thermal stress. In conclusion, these results refine the thermodynamic boundaries of intermetallic stability and provide insights into managingthe performance and durability of intermetallic alloys for demanding high-temperature applications.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Modulating coordinate site occupancy in high-entropy spinel electrocatalysts

High entropy spinel oxides provide a versatile platform for electrocatalysis because multiple metal cations can be incorporated into a single crystalline lattice, enabling tunable electronic structures. However, controlling how these cations distribute between tetrahedral and octahedral coordination sites remains a major challenge, limiting rational catalyst design. Here, we modulate cation coordination site occupancy between tetrahedral and octahedral sites in a Co–Fe–Cr–Mn–Ni framework by introducing a sixth cation (Zn, Ga, Mg, or Al) with distinct site preference energies. Using density functional theory, synchrotron X-ray absorption spectroscopy, and magnetic circular dichroism, we demonstrate that Zn preferentially occupies tetrahedral sites, driving increased octahedral occupancy of cobalt. This redistribution increases the population of octahedrally coordinated cobalt in mixed oxidation states, enhances electrical conductivity, and improves oxygen evolution reaction activity. Our findings establish coordination site occupancy as a critical design parameter, providing a strategic pathway for tailoring multicomponent spinel electrocatalysts with optimized performance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Application of pan-sharpening algorithm for correlative multimodal imaging using AFM-IR

The coupling of atomic force microscopy with infrared spectroscopy (AFM-IR) offers the unique capability to characterize the local chemical and physical makeup of a broad variety of materials with nanoscale resolution. However, in order to fully utilize the measurement capability of AFM-IR, a three-dimensional dataset (2D map with a spectroscopic dimension) needs to be acquired, which is prohibitively time-consuming at the same spatial resolution of a regular AFM scan. In this paper, we provide a new approach to process spectral AFM-IR data based on a multicomponent pan-sharpening algorithm. This approach requires only a low spatial resolution spectral and a limited number of high spatial resolution single wavenumber chemical maps to generate a high spatial resolution hyperspectral image, greatly reducing data acquisition time. As a result, we are able to generate high-resolution maps of component distribution, produce chemical maps at any wavenumber available in the spectral range, and perform correlative analysis of the physical and chemical properties of the samples. We highlight our approach via imaging of plant cell walls as a model system and showcase the interplay between mechanical stiffness of the sample and its chemical composition. We believe our pan-sharpening approach can be more generally applied to different material classes to enable deeper understanding of that structure-property relationship at the nanoscale.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Statistics of the NiCoCr medium-entropy alloy: Novel aspects of an old puzzle

Abstract We study the K -state phenomenon in the NiCoCr medium-entropy alloy using first-principles techniques jointly with the efficient Wang–Landau Monte Carlo and simulated annealing algorithms. Our theoretical results successfully explain the existence of the peak around 940 K in the experimental specific heat curve that characterizes the K -state phenomenon and give a fine picture of its atomic origin. The peak is caused by the maximum change of the local configurations characterized by the short-range-order (SRO) parameters at that temperature. The maximum change in SRO parameters is dominated by the nearest-neighbor interactions of atoms but substantially tuned by the many-body interactions. One surprising aspect revealed by the reciprocal-space SRO parameters is that the Ni–Co pair distribution is not random even above the ordering transition temperature, dramatically different from Ni–Cr and Co–Cr, indicating the system cannot be treated as a pseudo binary alloy. This prototypical example shows the complicated nature of multicomponent alloys, different from binary alloys. Our methods can be directly used to study the important K -state phenomenon observed in a number of other composition-concentrated alloys regardless of their number of components.

Pei, Zongrui (ORCID:0000000307484629)↗
DOE OSTI2020

Tunable chemical complexity to control atomic diffusion in alloys

Abstract In this paper we report a new fundamental understanding of chemically-biased diffusion in Ni–Fe random alloys that is tuned/controlled by the intrinsic quantifiable chemical complexity. Development of radiation-tolerant alloys has been a long-standing challenge. Here we show how intrinsic chemical complexity can be utilized to guide the atomic diffusion and suppress radiation damage. The influence of chemical complexity is shown by the example of interstitial atom (IA) diffusion that is the most important defect in radiation effects. We use μs-scale molecular dynamics to reveal sluggish diffusion and percolation of IAs in concentrated Ni–Fe alloys. We develop a mean field diffusion model to take into account the effect of migrating defect energy properties on diffusion percolation, which is verified by a new kinetic Monte Carlo approach addressing detailed processes. We demonstrate that the local variations in the ground state energy of IA configurations in alloys, reflecting the chemical difference between alloying components, drives the percolation effects for atomic diffusion. Percolation, chemically-biased and sluggish diffusion are phenomena that are directly related to the chemical complexity intrinsically to multicomponent alloys.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Machine-learning informed prediction of high-entropy solid solution formation: Beyond the Hume-Rothery rules

The empirical rules for the prediction of solid solution formation proposed so far in the literature usually have very compromised predictability. Some rules with seemingly good predictability were, however, tested using small data sets. Based on an unprecedented large dataset containing 1252 multicomponent alloys, machine-learning methods showed that the formation of solid solutions can be very accurately predicted (93%). The machine-learning results help identify the most important features, such as molar volume, bulk modulus, and melting temperature. As such a new thermodynamics-based rule was developed to predict solid–solution alloys. The new rule is nonetheless slightly less accurate (73%) but has roots in the physical nature of the problem. The new rule is employed to predict solid solutions existing in the three blocks, each of which consists of 9 elements. The predictions encompass face-centered cubic (FCC), body-centered cubic (BCC), and hexagonal closest packed (HCP) structures in a high throughput manner. The validity of the prediction is further confirmed by CALculations of PHAse Diagram (CALPHAD) calculations with high consistency (94%). Since the new thermodynamics-based rule employs only elemental properties, applicability in screening for solid solution high-entropy alloys is straightforward and efficient.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Approaches for handling high-dimensional cluster expansions of ionic systems

Disordered multicomponent systems attract great interest due to their engineering design flexibility and subsequent rich space of properties. However, detailed characterization of the structure and atomic correlations remains challenging and hinders full navigation of these complex spaces. A lattice cluster expansion is one tool to obtain configurational and energetic resolution. While in theory a cluster expansion can be applied to any system of any dimensionality, the method has primarily been used in binary systems or ternary alloys. Here we apply cluster expansions in high-component ionic systems, setting up the largest cluster expansion ever attempted to our knowledge. In doing so, we address and discuss challenges specific to high-component ionic systems, namely charge state assignments, structural relaxations, and rank-deficient systems. We introduce practical procedures to make the fitting and analysis of complex systems tractable, providing guidance for future computational studies of disordered ionic systems.

25 ENERGY STORAGE↗
DOE OSTI2023

CEGANN: Crystal Edge Graph Attention Neural Network for multiscale classification of materials environment

Abstract We introduce Crystal Edge Graph Attention Neural Network (CEGANN) workflow that uses graph attention-based architecture to learn unique feature representations and perform classification of materials across multiple scales (from atomic to mesoscale) and diverse classes ranging from metals, oxides, non-metals to hierarchical materials such as zeolites and semi-ordered mesophases. CEGANN can classify based on a global, structure-level representation such as space group and dimensionality (e.g., bulk, 2D, clusters, etc.). Using representative materials such as polycrystals and zeolites, we demonstrate its transferability in performing local atom-level classification tasks, such as grain boundary identification and other heterointerfaces. CEGANN classifies in (thermal) noisy dynamical environments as demonstrated for representative zeolite nucleation and growth from an amorphous mixture. Finally, we use CEGANN to classify multicomponent systems with thermal noise and compositional diversity. Overall, our approach is material agnostic and allows for multiscale feature classification ranging from atomic-scale crystals to heterointerfaces to microscale grain boundaries.

36 MATERIALS SCIENCE↗