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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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Machine-learning molecular dynamics simulations pave the way to completely treat the anharmonicity of phonons. Low-energy anharmonic modes in transition-metal dichalcogenides drive the thermal and transport properties.
Synergizing wire arc-directed energy deposition (WA-DED) additive manufacturing (AM) with particle-strengthened creep strength-enhanced ferritic (CSEF) steels enables fabrication and repair of critical power-plant components. Investigations focused on fusion-welded particle-strengthened CSEF steels, such as Grade 91 steel, have linked microstructurally heterogeneous regions—forming due to heat affected zones (HAZ)—with premature failure during elevated temperature service. Fusion-based AM, including WA-DED, likewise generates microstructurally and plastically heterogeneous regions due to spatiotemporally varying thermokinetics during deposition. However, works investigating such microstructural heterogeneities, their implications for mechanical behavior, and strategies to mitigate their formation remain scarce. This work identifies microstructurally and plastically heterogeneous regions within the WA-DED-processed Grade 91 steel. Spatial microhardness variations in the as-fabricated specimen correlate with the variation in the attributes of grain, martensitic microstructure, and precipitates across the fusion zone and HAZ. Digital image correlation-enabled tensile tests performed at 500 °C revealed pronounced deformation localization and a wave-like strain distribution, with wavelength close to the melt pool depth, indicating susceptibility of the as-fabricated components to premature creep failure. Such heterogeneity in microstructural and mechanical behavior was attributed to recurring solid-state phase transformations. Subsequently, an interlayer temperature control strategy was implemented, wherein maintaining interlayer temperature above the martensitic start temperature mitigated the heterogeneous microstructural and plastic response in the as-fabricated condition. Findings open pathways to achieving deformation-localization- and creep-resistant microstructures in WA-DED fabricated particle-strengthened CSEF steel components, reducing reliance on post-welding heat treatments—conventionally required to enhance creep resistance—and enabling on-demand, short lead-time fabrication of next-generation power-plant components.
Soft Li-ion batteries, based on conventional organic electrolytes, face performance degradation challenges due to moisture penetration and safety concerns due to possible leakage of toxic fluorine compounds and flammable solvents under mechanical damage. We design a water-scarce hydrogel electrolyte with fluorine-free lithium salt to achieve wide electrochemical stability window (up to 3.11 volts) in ambient air without hermetic packaging while balancing high stretchability (1348%), ion conductivity (41 millisiemens per centimeter), and self-healing capabilities for mechanically and chemically safe stretchable Li-ion batteries. Molecular synergy between hydrophilicity and lithiophilicity of zwitterionic polymer backbone is revealed by molecular dynamics simulations. The battery exhibits capacity retention under harsh mechanical stresses—enduring stretching, twisting, folding, and multiple through-punctures by a needle—while self-healing from repeated through cuts by a razor blade. Stable ambient operation for 1 month over 500 charge-discharge cycles (average coulomb efficiency, 95%) is achieved. A prototype self-healing electronic system with embedded soft batteries demonstrates practical application as a durable embodied energy source.
Semicrystalline polymers, e.g., polyethylene terephthalate (PET), release micro- (100 nm to 1 mm) and nanoplastics (up to 100 nm) [MNPL] when they are degraded under quiescent conditions. However, the exact molecular mechanisms leading to material fragmentation into MNPL are unknown. Here, we monitor the evolution of chain molecular weight and the MNPL production kinetics during hydrolysis of PET pellets. We find that only ~0.6% of the amorphous phase ester bonds are hydrolyzed at the onset of MNPL release. Then, by combining a random scission model with measured amorphous spacings, we estimate that only ~15% of the stress transmitters in the amorphous phase, namely, bridges and entangled loops, have failed by this point. Thus, spontaneous fragmentation of the semicrystalline nanostructure occurs despite significant intercrystalline connectivity. We resolve this apparent contradiction by proposing that fragmentation can only occur when progressive tie-chain scission causes the material to undergo the ductile/brittle transition. Mechanistically, we speculate that the internal stresses responsible for material fragmentation are caused by processing (i.e., residual stresses) and/or sample densification induced by chemi-crystallization. We discuss additional factors that may affect our analysis and thus require further investigation, such as skin-core effects, preferential degradation of stress transmitters and/or recrystallization processes.
Achieving core-edge compatible divertor detachment is a critical requirement for stable operation in future fusion devices. This study compares nitrogen and neon seeding in KSTAR H-mode plasmas with carbon walls, combining experiments and SOLPS-ITER modelling to evaluate their radiative dissipation and core-edge compatibility. Experimentally, N seeding achieved stronger divertor detachment, with larger reductions in target particle and heat fluxes, a higher divertor radiation fraction, and a lower core radiation fraction than Ne. In contrast, Ne seeding triggered a significant rise in core radiation followed by H–L back transitions, limiting the maximum total radiated power fraction to roughly half that of N. SOLPS-ITER simulations reproduced the experimental trends and revealed that the better core-edge compatibility of N arises from its higher divertor retention in addition to its higher cooling factor. The relative positions of the stagnation points of impurity poloidal velocity and ionization sources did not explain the different divertor compression. Instead, in the present modelling, the higher impurity parallel particle flux, resulting from the higher parallel impurity velocity, explains the stronger nitrogen impurity compression in the divertor region. The parallel temperature distribution with N was more favourable for achieving higher impurity parallel velocity than with Ne, because the impurity velocity is governed by modifications of the main ion flow due to friction and thermal forces, both of which strongly depend on the temperature. Ultimately, this behaviour is attributed to the strongly divertor-localized radiation of N. These results demonstrate that N is more effective than Ne in achieving radiative divertor detachment while maintaining low core contamination in KSTAR, consistent with observations in other present tokamaks.
A molecular understanding of the solvation and dynamics of ions under static electric fields is crucial for modelling a wide range of natural and technological processes. Yet, traditional simulation methods suffer from a trade-off that has to be made between accuracy and statistical convergence. To bridge this gap, herein we extend our recently introduced Perturbed Neural Network Potential Molecular Dynamics (PNNP MD) approach to investigate the solvation structures and ionic transport mechanisms of electrified alkali cationic solutions. We obtain ionic conductivities for Li+, Na+ and Cs+ from the field dependence of the ionic current density in good agreement with experiment. Surprisingly, the migration mechanism is found to be strikingly different for the three ions despite their similar ionic conductivities. While Li+ conducts predominantly through vehicular migration of a stable 4-fold coordinated ion at all field strengths, Cs+ conducts strictly through a structural diffusion mechanism, where 9-12 transient first shell water coordination bonds are broken and renewed. Notably, aqueous Na+ emerges as a “Goldilocks” ion: its ion-water interactions are strong enough to maintain distinct 5-6 fold coordination shells at zero field (unlike Cs+) yet labile enough to be strongly perturbed by electric fields (unlike Li+). As a consequence, we observe an electric field-induced transition from vehicular to structural ionic transport for Na+ that is accompanied by a marked increase in the ionic current density. Our results imply that the conductance mechanism of ions with moderate ion-solvent interactions can be effectively tuned by external electric fields.
Tailoring the molecular properties that govern energetic material sensitivity is essential to improve safety and help develop new energetic materials. Despite this need, understanding the complex chemistry and physics of explosive initiation and propagation is still a challenge. Recent work by our group has reinforced the view that explosive sensitivity under sub-shock conditions is connected to the strength of the weakest covalent bond in the molecule, that is, its “trigger linkage.” These correlations have been observed with different classes of energetic molecules and indicate that “trigger linkage” bond breaking, and heat of explosion are good indicators for the sensitivity trends. Herein we report the synthesis of aliphatic energetic materials with ethane, propane and neopentane backbones. Experimental and computational studies show that the trigger linkage model, based on results from quantum molecular dynamics simulations, correctly predicts trends observed in the impact sensitivity of the molecules. However, while the model predicts the impact sensitivities of the ethane series, the neopentane series has higher impact sensitivities than predicted, which is presumably influenced by crystal packing effects.
Lipomyces is a genus of oleaginous yeasts with potential for contributing to reliable biomanufacturing supply chains. However, progress in advanced strain designs and engineering efforts are still constrained by a lack of understanding of the underlying molecular drivers of Lipomyces phenotypes. To address this gap, we collected a suite of multi-omic data to dissect how carbon source availability reshapes the metabolic network, lipid allocation, and regulatory architecture of Lipomyces starkeyi. We observed that glucose promotes biosynthetic and proliferative processes supported by abundant energy and carbon intermediates, xylose enhances redox-balancing mechanisms centered on the pentose phosphate pathway, and glycerol activates respiratory metabolism, ß-oxidation, and the glyoxylate cycle. Lipid species distributions remained consistent in both nitrogen replete and depleted conditions across the carbon sources, indicating robust production mechanisms. Regulatory protein identification and network analysis revealed glycerol-driven respiratory growth favors regulatory programs integrating stress tolerance, redox balance, and lipid-associated metabolism, whereas xylose growth activates compensatory transcriptional responses aimed at maintaining mitochondrial function. Nitrogen limitation modulates the strength of these responses but does not fundamentally alter their direction, reinforcing carbon source as the dominant driver of regulatory architecture. Taken together, this data enhances the understanding of Lipomyces molecular rearrangements and provides a foundation for further development of predictive phenotypic tools in this genus.
Controlling nanoscale tip‐induced material removal is crucial for achieving atomic‐level precision in tomographic sensing with atomic force microscopy (AFM). While advances have enabled volumetric probing of conductive features with nanometer accuracy in solid‐state devices, materials, and photovoltaics, limitations in spatial resolution and volumetric sensitivity persist. This work identifies and addresses in‐plane and vertical tip‐sample junction leakage as sources of parasitic contrast in tomographic AFM, hindering real‐space 3D reconstructions. Novel strategies are proposed to overcome these limitations. First, the contrast mechanisms analyzing nanosized conductive features are explored when confining current collection purely to in‐plane transport, thus allowing reconstruction with a reduction in the overestimation of the lateral dimensions. Furthermore, an adaptive tip‐sample biasing scheme is demonstrated for the mitigation of a class of artefacts induced by the high electric field inside the thin oxide when volumetrically reduced. This significantly enhances vertical sensitivity by approaching the intrinsic limits set by quantum tunneling processes, allowing detailed depth analysis in thin dielectrics. The effectiveness of these methods is showcased in tomographic reconstructions of conductive filaments in valence change memory, highlighting the potential for application in nanoelectronics devices and bulk materials and unlocking new limits for tomographic AFM.
We develop a constructive approach to generate quantum neural networks capable of representing the exact thermal states of all many-body qubit Hamiltonians. The Trotter expansion of the imaginary time propagator is implemented through an exact block encoding by means of a unitary, restricted Boltzmann machine architecture. Marginalization over the hidden-layer neurons (auxiliary qubits) creates the nonunitary action on the visible layer. Then, we introduce a unitary deep Boltzmann machine architecture in which the hidden-layer qubits are allowed to couple laterally to other hidden qubits. We prove that this wave-function is closed under the action of the imaginary time propagator and, more generally, can represent the action of a universal set of quantum gate operations. We provide analytic expressions for the coefficients for both architectures, thus enabling exact network representations of thermal states without stochastic optimization of the network parameters. In the limit of large imaginary time, the yields the ground state of the system. The number of qubits grows linearly with the number of interactions and total imaginary time for a fixed interaction order. Both networks can be readily implemented on quantum hardware via midcircuit measurements of auxiliary qubits. If only one auxiliary qubit is measured and reset, the circuit depth scales linearly with imaginary time and number of interactions, while the width is constant. Alternatively, one can employ a number of auxiliary qubits linearly proportional to the number of interactions, and circuit depth grows linearly with imaginary time only. Every midcircuit measurement has a postselection success probability, and the overall success probability is equal to the product of the probabilities of the midcircuit measurements.
Hydrogen plasma is gaining significant interest as a promising pathway for direct iron ore reduction and for lowering process temperatures but the reduction mechanism remains poorly understood. In this work we analyzed the plasma and thermal reduction of thin-film hematite (Fe 2 O 3 ) at temperatures below 340 °C using X-ray diffraction and scanning electron microscopy with energy-dispersive X-ray spectroscopy. Plasma reduced the incubation period by an order of magnitude and increased the reduction rate by a factor of 2.6 compared to thermal reduction. Plasma-produced H-atoms facilitate the formation of numerous iron nucleation sites, bypassing the energetically unfavorable dissociative adsorption of H 2 on iron oxide. These iron nuclei can autocatalyze the reduction of the surrounding hematite via a hydrogen spillover mechanism. Our results demonstrate that plasma-derived H-atoms primarily impact the initial nucleation-limited stage. These new insights provide a mechanistic framework that can aid the implementation and optimization of hydrogen plasma-assisted iron oxide reduction at reduced temperatures.
Abstract Magnetic materials play a crucial role in the transition to more sustainable forms of energy and electric vehicles. There is an anticipated shortage in magnetic materials in the future, and as a result there is an urgent need to discover and design new magnetic materials. Computational magnetic material design using density functional theory is daunting because of the challenge in identifying magnetic ground states from a combinatorially large set of possibilities. Machine learning offers a path forward by enabling efficient surrogate models that can more readily enumerate these states, but there is a dearth of training data available, and what is available tends to be imbalanced with too much non-magnetic data. In this work we show that the discrete and previously tackled data imbalance that exists at the level of the magnetic ordering leads to an imbalanced continuous distribution with many zeros when the data is unraveled at the atomic magnetic moment level, which subsequently leads to models with low accuracy for magnetic properties. We mitigate this by using a two-part model framework. Our scheme is able to classify atoms into magnetic and non-magnetic with an F1 score and Matthew’s correlation coefficient (MCC) of ~91% and then to provide an implicit embedding representation that maps directly onto the magnitude of the magnetic moment with a mean absolute error of 0.1 μ B . Beyond screening for new magnetic materials, we demonstrate an additional practical use case of our scheme: the provision of good initial guesses for magnetic moments in first-principles electronic relaxations. Such initialization is shown to lead to faster convergence to configurations that lie closer to the ground state.
Multilayer plastics (MLPs) are widely used in modern packaging because they combine multiple functions such as oxygen and moisture barriers, mechanical strength, puncture resistance, and heat sealability into lightweight and cost-effective packaging solutions. These attributes are essential for food, beverage, pharmaceutical, and consumer goods packaging. However, conventional commercial MLPs typically consist of five to twelve layers made from chemically incompatible materials, including polyolefins, polyethylene terephthalate, nylon, ethylene-vinyl alcohol (EVOH), adhesives, and tie layers. This complexity makes MLPs extremely difficult to recycle. As a result, the vast majority of multilayer plastics (MLPs) are disposed of through landfilling or incineration. This disposal pathway perpetuates demand for virgin material production, thereby driving high industrial energy consumption, increasing greenhouse gas (GHG) emissions, and contributing to the long-term accumulation of plastic waste. The scope of work included: (1) Design and fabrication of all-polyester multilayer structures using commercially relevant processing methods; (2) Experimental validation of barrier, mechanical, and sealing performance; (3) Demonstration of both mechanical and chemical recycling pathways; and (4) Comprehensive techno-economic analysis (TEA) and life-cycle assessment (LCA) to quantify cost, energy, and environmental impacts.
Neutrons can present a significant challenge for neutrino experiments in which energy reconstruction is critical. With the ability to escape detection completely and with a weak correlation between their kinetic energy and any eventual energy deposition, it is difficult to fully account for neutrons produced in neutrino interactions. This in turn leads to significant model dependence when evaluating neutron-related systematic uncertainties. The NOvA experiment is a long-baseline neutrino oscillation experiment with a high-statistics sample of antineutrino data collected by its near detector. We report an excess relative to data of simulated neutron candidates with low energy depositions when using standard Geant4 physics lists. The simulation excess is traced to an overabundance of secondary photons produced from interactions of neutrons with kinetic energy greater than \SI{20}{\mega\eV}. Improved agreement with data is obtained by applying the data-driven neutron-on-carbon \menate model for neutrons between \SI{20}{\mega\eV} and ${\sim}$\SI{100}{\mega\eV}. With \menate, the residual oversimulation is more uniform across the calorimetric neutron energy spectrum, suggesting possible overproduction of primary neutrons by the GENIE neutrino interaction generator. These results motivate the adoption of \menate-supplemented Geant4 simulation as the nominal simulation in the production of future \nova simulation.
Abstract Lithium‐sulfur (Li‐S) batteries, with their high energy density, nontoxicity, and the natural abundance of sulfur, hold immense potential as the next‐generation energy storage technology. To maximize the actual energy density of the Li‐S batteries for practical applications, it is crucial to escalate the areal capacity of the sulfur cathode by fabricating an electrode with high sulfur loading. Herein, ultra‐high sulfur loading (up to 12 mg cm −2 ) cathodes are fabricated through an industrially viable and sustainable solvent‐free dry‐processing method that utilizes a polytetrafluoroethylene binder fibrillation. Due to its low porosity cathode architecture formed by the binder fibrillation process, the dry‐processed electrodes exhibit a relatively lower initial capacity compared to the slurry‐processed electrode. However, its mechanical stability is well maintained throughout the cycling without the formation of electrode cracking, demonstrating significantly superior cycling stability. Additionally, through the optimization of the dry‐processing, a single‐layer pouch cell with a loading of 9 mg cm −2 and a novel multi‐layer pouch cell that uses an aluminum mesh as its current collector with a total loading of 14 mg cm −2 are introduced. To address the reduced initial capacity of dry‐processed electrodes, strategies such as incorporating electrocatalysts or employing prelithiated active materials are suggested.
Interfacial thermal and acoustic phenomena have an important role in quantum science and technology, including in spintronic and spincaloritronic materials and devices. Simultaneous measurements of the low-temperature thermal and acoustic properties of a metal/insulator heterostructure reveal distinct dynamics in the characteristic phonon frequency ranges of acoustic and thermal transport. The measurements probed a heterostructure consisting of a thin film of Pt on the ferrimagnetic insulator gadolinium iron garnet (Gd 3 Fe 5 O 12 , GdIG) grown epitaxially on a gadolinium gallium garnet substrate. Ultrafast structural dynamics within the Pt layer were tracked using time-resolved ultrafast x-ray diffraction and analyzed to probe interfacial acoustic and thermal properties. The rapid heating of the Pt layer by a 400 nm wavelength femtosecond-duration optical pulse produced transient structural changes that provided the stimulus for these measurements. Rapid heating produced a broadband acoustic pulse that was partially reflected by the Pt/GdIG interface. Temporal frequencies up to 740 GHz, corresponding to angular frequencies of several THz, were detected in a wavelet analysis of the acoustic oscillations of the strain in the Pt layer. The structural results were analyzed to determine (i) the acoustic damping coefficient and phonon mean free path in Pt at frequencies of hundreds of GHz and (ii) the Grüneisen anharmonicity parameter. The thermal conductance of the Pt/GdIG interface was tracked using the slower, tens-of-picosecond-scale, dynamics of the initial cooling of the heated Pt layer. Analysis using a model based on the Boltzmann transport equation shows that the phonon transmission is lower at the phonon frequencies relevant to thermal transport than for subterahertz regime acoustics.
Accurate modeling of particle transport within fusion blankets is essential for predicting performance metrics such as heat deposition and the tritium breeding ratio (TBR). However, high-fidelity coupling of thermal fluids from computational fluid dynamics (CFD) to neutronics simulations often incurs significant computational costs due to the complexity of surface intersection calculations in Monte Carlo codes. This paper presents an accelerated multiphysics coupling method for neutronics that utilizes hierarchical agglomerative clustering to map complex material property distributions to a neutronics model. Implemented within the fusion reactor design and assessment (FREDA) framework, the method leverages existing Python packages to automate the creation of clustered geometries for OpenMC. The approach is demonstrated on a sector model of an ARC-class tokamak with an immersion molten salt blanket, and an simple geometry with varying isotopic concentrations. Results show that the clustering method significantly reduces computational burden without compromising fidelity, providing a foundation for agile iteration of neutronics simulations involving multiple coupled material properties.
Nylons, a major class of synthetic polyamides, are widely used due to their excellent mechanical strength, thermal stability, and chemical resistance. Conventional nylon production relies on the polymerization of lactams or stoichiometric nylon salts. However, applying these approaches to unconventional precursors such as bio-derived glutaric acid produces polymers with low molecular weight and limited applications. To address these challenges, we demonstrated that chemically-synthesized nylon diads enable the production of higher-molecular-weight polyamides compared with traditional salts. We then identified a biosynthetic approach using amide synthetases to convert unprotected bifunctional substrates into nylon-relevant diads. Using a cofactor regeneration system, enzymatic diad synthesis was scaled to produce sufficient material for laboratory-scale characterization and solid-state polymerization. Amide synthetases demonstrated broad substrate scope, catalyzing the regioselective assembly of diverse nylon-relevant diacids, diamines, and ω-amino acids. This strategy offers a novel route to synthesize challenging nylon monomers and advances production of bioderived nylons.