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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 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

A Bulk versus Nanoscale Hydrogen Storage Paradox Revealed by Material-System Co-Design

Metal hydrides are serious contenders for materials-based hydrogen storage to overcome constraints associated with compressed or liquefied H 2 . Their ultimate performance is usually evaluated using intrinsic material properties without considering a systems design perspective. An illustrative case with startling implications is (LiNH 2 +2LiH). Using models that simulate the storage system and associated fuel cell of a light-duty vehicle (LDV), the performance of the bulk hydrides is compared with a nanoscaled version in porous carbon (PC), (LiNH 2 +2LiH)@(6-nm PC). Using experimental material properties, the simulations show that (LiNH 2 +2LiH)@(6-nm PC) counterintuitively has higher usable gravimetric and volumetric capacities than the bulk counterpart on a system basis despite having lower capacities on a materials-only basis. Nanoscaling increases the thermal conductivity and lowers the desorption enthalpy, which consequently increases heat management efficiency. In a simulated drive cycle for fuel cell-powered LDV, the fuel cell is inoperable using bulk (LiNH 2 +2LiH) as the storage material but completes the drive cycle using the nanoscale material. Further, these results challenge the notion that nanoscaling incurs mass and volume penalties. Instead, the synergistic nanoporous host-hydride interaction can favorably modulate chemical and heat transfer properties. Moreover, a co-design approach considering application-specific tradeoffs is essential to accurately assess a material's potential for real-world hydrogen storage.

08 HYDROGEN↗
DOE OSTI2025

Deterministic fabrication of highly reproducible monochromatic quantum emitters in hexagonal boron nitride

Quantum emitters in hexagonal boron nitride are important room temperature single-photon sources. However, conventional fabrication methods yield quantum emitters with dispersed and inconsistent spectral profiles, limiting their potential for practical quantum applications, which demand reproducible high quality single-photon sources. Here, we report the deterministic creation of highly reproducible monochromatic quantum emitters by applying carbon-ion implantation on freestanding hexagonal boron nitride flakes, while a carbon mask with suitable thickness was adapted to optimize the implantation results. Quantum emitters fabricated using this approach exhibited thermally limited monochromaticity, with an emission center wavelength of 590.7 ± 2.7 nm, a narrow full width at half maximum of 7.1 ± 1.7 nm, an emission rate of 1 MHz without optical engineering, and exceptional stability under ambient conditions. Density functional theory calculations and scanning transmission electron microscopy suggest that these emitters are comprised of boron centered carbon tetramers. This method provides a reliable single-photon source for optical quantum computing and potential future industry-scale applications.

Hua, Muchuan [Argonne National Laboratory (ANL), A↗
DOE OSTI2025

Probing Surface Plasmon Dynamics in Periodic Nanostructures through Ultrafast Electron Microscopy

Surface plasmon polaritons (SPPs) can be manipulated to localize and guide light in subwavelength distances, enabling them to find applications in a wide range of areas, from sensing to quantum computing. Among several methods of SPP excitation, periodic arrays of nano- and microstructures are of particular interest, as they enable engineering SPP properties through structural parameters. Here, in this study, using the photon-induced near-field electron microscopy (PINEM) technique, we investigated the mode formation, coupling, interference, and decay of SPPs in square and hexagonal arrays of circular nanoholes under both visible and near-infrared excitation. Polarization-resolved analysis revealed the key factors governing SPP localization and interference patterns, showing that the periodicity and symmetry of the array primarily determine the SPP interference patterns and their orientation, while pump polarization mainly modulates their intensity. Time-resolved PINEM measurements demonstrated the spatial dependence of the SPP temporal characteristics. In addition, cathodoluminescence (CL) spectroscopy was employed to examine the intrinsic plasmonic characteristics of the structure. Finite difference time domain (FDTD) simulations showed strong agreement with both PINEM and CL measurements on the spatial and spectral behavior of SPPs. Understanding the spatiotemporal dynamics of SPPs on nanostructures beyond the diffraction limit is crucial for optimizing plasmonic structures for advanced photonic and quantum technologies.

Plasmonics↗
DOE OSTI2025

Machine learning enabled discovery of superhard and ultrahard carbon polymorphs

The demand for multifunctional materials has motivated the move from near-equilibrium materials to metastable i.e. out-of-equilibrium phases that can meet several desired target properties. The search for such metastable phases with exotic properties is non-trivial and often serendipitous. Inverse design approaches based on evolutionary search have been powerful tools, but such traditional searches have focused on identifying primarily stable and metastable materials with the lowest enthalpy. The inverse design of materials, with a focus on a desired property such as, for example, hardness is a challenging task because of the expensive computational cost involved in sampling multiple structures. The recent advances in machine learning have brought new powerful AI techniques to the forefront which can potentially revolutionize the inverse design and discovery of materials, especially metastable phases capable of meeting multifunctionality. Here, in this work, we develop and apply an automated reinforcement learning workflow for inverse design that integrates first principles physics and atomistic simulations with machine learning (ML), and high-performance computing to allow rapid exploration of the superhard and ultrahard metastable phases of Carbon. We demonstrate an automatic machine learning based inverse design workflow to map new undiscovered metastable states ranging from near equilibrium to those far-from-equilibrium that satisfy multiple property objectives, specifically bulk moduli, shear moduli and hardness. We create a comprehensive library of carbon stable and metastable phases with varying hardness and subsequently shortlist 10 top performing candidate carbon structures, including two newly reported phases, based on their hardness and characterize their temperature dependent mechanical properties. A neural network model is built using featurization of allotropes of carbon to predict the quasi-harmonic Gibbs free energies. The Gibbs free energies of the top performing phases are analyzed to get an estimate of the experimental synthesizability of these superhard and ultrahard carbon phases. In general, we show using machine learning based inverse design approaches how hitherto inaccessible metastable states can be identified and potentially synthesized to meet the demand for multifunctional materials.

Balasubramanian, Karthik [Univ. of Illinois, Chica↗
DOE OSTI2025

Development and assessment of hierarchical multi-reward reinforcement learning based potential for silicene with state-of-the-art models

We develop a new interatomic force field for Silicene, a 2D material with a buckled hexagonal lattice structure with high polymorphism. We introduce new parameterizations of a Tersoff model using a hierarchical multi-reward reinforcement learning (RL) methodology coupled with a continuous Monte Carlo Tree Search optimization. Our model significantly outperforms existing methods by enhancing the accuracy of predictions for the structural and thermodynamic properties of seven silicene polymorphs-including structure, energy, equation of state, elasticity, and phonon dispersion-when compared to established models. We further make a comprehensive comparison of the various models in predicting the mechanical and thermal properties of silicene. We trace the origin of the improved performance to the description of the angular dependence in the bond-order term, suggesting that modifying the angular terms in short-range models is essential to capture the structural diversity in low dimensional systems.

2D materials↗
DOE OSTI2023

Application of a deep learning semantic segmentation model to helium bubbles and voids in nuclear materials

Imaging nanoscale radiation-induced defects using the transmission electron microscope (TEM) is a key factor in the successful implementation of materials for nuclear energy structural applications. Analyzing each defect in a TEM micrograph is currently a manual task. To identify the defects in a single image can take anywhere from 15 min to an hour and a project can require the analysis of anywhere from tens to ≥ 100 images. Here, we use artificial intelligence (AI) models to automate this task. For simplification, we evaluated images with only a single type of defect; helium bubbles. Additionally, we performed semantic segmentation of these helium bubble defects in electron microscopy images of irradiated FeCrAl alloys using a deep learning DefectSegNet model. This model, which was previously used to classify crystal defects, is inspired by the classic DenseNet and U-Net image segmentation models. It claims high spatial resolution, but has poor performance at object boundaries. Our paper improves the DefectSegNet model’s application by adding two new features. First, the DefectSegNet model is applied not only to perform calculation pixel-wise but also object (or feature) wise. Because object-wise metrics are directly relevant to our final goal of detecting bubbles, whereas pixel-wise classification is only an intermediate step, it is an important part of our study. Second, a distance map loss (DML) function has been added to increase its performance at object boundaries. It is crucial to accurately represent defects boundaries, especially bubbles, in order to track the bubble-induced swelling caused by irradiation. The boundary-focused DML function is also compared to other loss functions like Cross-entropy, Weighted Binary Cross Entropy (WBCE), Dice and Intersection over Union (IOU). Finally, by incorporating new features, we found a marked improvement on segmentation quality and better shape preservation at the boundaries and areas of the bubbles.

42 ENGINEERING↗
DOE OSTI2025

Self‐Lubricating Tribo‐Catalytic Activity of 2D High Entropy Alloy Nanoflakes

High Entropy Alloys (HEAs) have garnered attention due to their remarkable tribological attributes. Predominantly, failure mechanisms in HEAs emanate from stress‐induced dislocations, culminating in crack propagation and film delamination. In this study, we report on the synthesis of 2D HEA of (MoWNbTaV) 0.2 S 2 which facilitates shear‐induced energy dissipation at sliding interfaces. The ball‐on‐disk tribological investigations demonstrate unprecedentedly low average coefficients of friction (0.076) and wear rates (10 −9 mm 3 (N∙m) −1 ) under high contact pressures (0.936 GPa) within ambient conditions. Employing multi‐scale characterizations alongside molecular dynamic simulations, we elucidate that the presence of the HEA triggers tribocatalytic activity under high contact pressures emerging as a pivotal factor in extending lubricant lifespan during tribological tests. The resilient lubriciousness coupled with the facile spray coating methodology of (MoWNbTaV) 0.2 S 2 in ambient environments paves the way for the development of a new class of solid lubricants based on 2D HEA.

2D solid lubricant↗
DOE OSTI2025

Low-nickel cathode chemistry for sustainable and high-energy lithium-ion batteries

The transition to sustainable energy storage demands lithium-ion batteries with high energy density and reduced reliance on critical metals such as nickel (Ni), yet current strategies to increase capacity have largely depended on raising Ni content, leading to escalating supply risks, rising costs and sustainability concerns. More critically, Ni-rich cathodes suffer from rapid electrochemical degradation driven by structural instability, creating an insurmountable trade-off between capacity and cycle life. Here, in this study, we introduce a low-Ni chemistry cathode, Li(Li 0.05 Ni 0.57 Mn 0.31 Co 0.07 )O 2 , with a radial phase integration design that overcomes these limitations, enabling a remarkable Ni usage reduction (Ni < 0.6) while demonstrating high capacity (215 mAh g −1 ) and markedly improved cyclability (~97% retention over 400 cycles) compared to conventional high-Ni cathodes (Ni = 0.8). Advanced X-ray and electron microscopy analyses reveal that the designed cathode exhibits a highly reversible oxygen anionic redox, benefiting from a structurally stable surface and minimizing irreversible phase transitions. Moreover, the integrated structure substantially mitigates lattice strain and improves mechanical stability even under harsh conditions. In conclusion, this advance offers a general design principle for developing next-generation cathodes that combine resource efficiency with long-term electrochemical reliability.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Extreme Temperature Cryptography Based On Nitrogen-Incorporated Ultrananocrystalline Diamond

Physical entropy sources that remain stable under extreme temperatures are essential for cryptography in emerging technological frontiers in deep space exploration, geothermal energy harvesting, and nuclear energy. However, conventional semiconductor platforms fail to generate stable and reliable cryptographic keys above 200 degrees C due to performance degradation. Here, we report a diamond-based cryptographic primitive that exploits the defect-rich sp 2 -bonded grain boundary network in nitrogen-incorporated ultrananocrystalline diamond (n-UNCD) film as a robust entropy source to generate cryptographic keys that remain operationally stable even after enduring extreme temperatures of 700 degrees C for 54 h while also surviving thermal cycling between room temperature and 700 degrees C for 48 h. The strength of the generated keys is assessed through several cryptographic metrics such as bit uniformity, entropy, hamming distances, and correlation coefficients, all of which are found to be near their respective ideal values. Moreover, the generated keys pass the NIST SP 800 and SP 800-90B tests and are also resilient to supply bias variations and a regression-based machine learning attack model based on the Fourier series. The robustness of the keys is attributed to the better thermal stability and chemical inertness of the n-UNCD film. This is supported by high-resolution energy-dispersive X-ray spectroscopy (EDS), which shows no significant lateral diffusion of metal atoms into the n-UNCD layer, and by Raman spectroscopy, which reveals no significant changes in the bonding configuration of the n-UNCD structure. Our findings highlight the remarkable potential of n-UNCD film for extreme environment cryptography by expanding the operational limits of conventional hardware security platforms.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Bound and Continuum Intersubband Transitions in Colloidal Quantum Wells

Quantum well intersubband transitions are critical for quantum cascade lasers and infrared photodetectors. Control of band offsets allows bound-to-bound intersubband transitions, with confinement of both initial and final states, and bound-to-continuum transitions, in which only the initial state is energetically confined within the potential well. Both types of transitions are also achieved in colloidal CdSe wells by changing the heterostructure shell. Bare wells have narrow intersubband transitions spanning the near-infrared spectrum following effective mass predictions. Atomically precise core/shells enable a readily adjusted potential well for electrons. For CdSe/ZnS, bound-to-bound transitions are narrow and redshift with shell thickness. By contrast, broad bound-to-continuum absorptions are found in CdSe/CdS. Due to small conduction band offsets, higher conduction band states of the well are more delocalized into the CdS shell. In conclusion, these measurements provide unique data to understand the electronic structure of colloidal quantum wells and chart a path to atomically precise optoelectronic materials for the mid-infrared.

colloidal atomic layer deposition↗
DOE OSTI2025

Ohmic contacts to nitrogen-incorporated n-type ultrananocrystalline diamond film grown on intrinsic single crystal diamond substrate

Nitrogen-incorporated ultrananocrystalline diamond (n-UNCD) films offer tremendous potential for diverse electronic applications. However, the absence of a reliable Ohmic contact at room temperature limits their practical integration and broader applicability. Here, in this study, we investigate the room temperature specific contact resistivity (ρ c ) of Ti/Pt/Au metal stack deposited on n-UNCD films grown on an intrinsic single crystal diamond substrate using a microwave plasma chemical vapor deposition system. We employ a circular transfer length model (c-TLM) and find the room temperature ρ c to be ∼4.67 × 10 −5 Ω cm 2 , which is among the lowest reported value for n-UNCD films. High temperature vacuum annealing conducted at 700 and 800 °C results in an initial improvement, followed by a minor degradation in ρ c values, respectively. The electrical contacts remain highly Ohmic for all measurements. Furthermore, cross-sectional transmission electron microscopy analysis suggests formation of conductive titanium carbide layer with no significant inter metallic diffusion. Overall, the electrical contacts demonstrate robust thermal stability, both of which are critical for attaining high-performance nanocrystalline diamond-based electronic devices.

Low resistance contacts↗
DOE OSTI2025

Photosynthetic Biohybrid System for Enhanced Abiotic N 2 -to-NH 3 Conversion under Ambient Conditions

Photosynthetic biohybrid systems (PBSs) offer an eco-friendly approach to transforming solar energy into value-added products by integrating biological entities with inorganic semiconductors. However, the chemical conversion capacity of most PBSs has inherent limitations, as whole-cell bacteria and isolated enzymes require fine-tuning of environmental conditions. Here, in this study, we report a new PBS developed by introducing free-standing ceria nanoparticles into the purple membrane (PM) of Halobacterium salinarum archaea, which can unidirectionally transfer charge carriers in response to incident photons, even after separation from living archaea at various conditions. Our microscopy, spectroscopy, and synchrotron X-ray scattering analyses confirm that the electrostatic assembly between ceria and PM creates seamless interfacial contact, thereby enhancing the photocatalytic capacity of ceria. Although the conversion of dinitrogen (N 2 ) to ammonia (NH 3 ) is thermodynamically challenging due to the triple bond in N 2 and a series of charge-transfer reactions, our PM–ceria (PMC) hybrid nanoparticle efficiently produces NH 3 by reducing N 2 using solar energy even under atmospheric pressure and room temperature while simultaneously converting glycerol into value-added derivatives. Additionally, our PMC nanoparticle involves neither toxic/precious metals nor bioengineering processes to achieve enhanced photocatalytic N 2 -to-NH 3 conversion. This study sheds light on the new aspect of PBSs by employing PM to potentially resolve the global energy and environmental challenges posed by the conventional Haber–Bosch process.

Jang, Jinhyeong [Argonne National Laboratory (ANL)↗
DOE OSTI2022

Topological control of liquid-metal-dealloyed structures

Abstract The past few years have witnessed the rapid development of liquid metal dealloying to fabricate nano-/meso-scale porous and composite structures with ultra-high interfacial area for diverse materials applications. However, this method currently has two important limitations. First, it produces bicontinuous structures with high-genus topologies for a limited range of alloy compositions. Second, structures have a large ligament size due to substantial coarsening during dealloying at high temperature. Here we demonstrate computationally and experimentally that those limitations can be overcome by adding to the metallic melt an element that promotes high-genus topologies by limiting the leakage of the immiscible element during dealloying. We further interpret this finding by showing that bulk diffusive transport of the immiscible element in the liquid melt strongly influences the evolution of the solid fraction and topology of the structure during dealloying. The results shed light on fundamental differences in liquid metal and electrochemical dealloying and establish a new approach to produce liquid-metal-dealloyed structures with desired size and topologies.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Prediction of the Cu oxidation state from EELS and XAS spectra using supervised machine learning

Abstract Electron energy loss spectroscopy (EELS) and X-ray absorption spectroscopy (XAS) provide detailed information about bonding, distributions and locations of atoms, and their coordination numbers and oxidation states. However, analysis of XAS/EELS data often relies on matching an unknown experimental sample to a series of simulated or experimental standard samples. This limits analysis throughput and the ability to extract quantitative information from a sample. In this work, we have trained a random forest model capable of predicting the oxidation state of copper based on its L-edge spectrum. Our model attains an R 2 score of 0.85 and a root mean square error of 0.24 on simulated data. It has also successfully predicted experimental L-edge EELS spectra taken in this work and XAS spectra extracted from the literature. We further demonstrate the utility of this model by predicting simulated and experimental spectra of mixed valence samples generated by this work. This model can be integrated into a real-time EELS/XAS analysis pipeline on mixtures of copper-containing materials of unknown composition and oxidation state. By expanding the training data, this methodology can be extended to data-driven spectral analysis of a broad range of materials.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Precisely patterned nanofibres made from extendable protein multiplexes

Molecular systems with coincident cyclic and superhelical symmetry axes have considerable advantages for materials design as they can be readily lengthened or shortened by changing the length of the constituent monomers. Among proteins, alpha-helical coiled coils have such symmetric, extendable architectures, but are limited by the relatively fixed geometry and flexibility of the helical protomers. Here we describe a systematic approach to generating modular and rigid repeat protein oligomers with coincident C 2 to C 8 and superhelical symmetry axes that can be readily extended by repeat propagation. From these building blocks, we demonstrate that a wide range of unbounded fibres can be systematically designed by introducing hydrophilic surface patches that force staggering of the monomers; the geometry of such fibres can be precisely tuned by varying the number of repeat units in the monomer and the placement of the hydrophilic patches.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Self-similar mesocrystals form via interface-driven nucleation and assembly

Crystallization by particle attachment (CPA) is a frequently occurring mechanism of colloidal crystallization that results in hierarchical morphologies. CPA has been exploited to create nanomaterials with unusual properties and is implicated in the development of complex mineral textures. Oriented attachment–a form of CPA in which particles align along specific crystallographic directions–produces mesocrystals that diffract as single crystals do, although the constituent particles are still discernible. The conventional view of CPA is that nucleation provides a supply of particles that aggregate via Brownian motion biased by attractive interparticle potentials. However, mesocrystals often exhibit regular morphologies and uniform sizes. Although many crystal systems form mesocrystals and individual attachment events have been directly visualized10, how random attachment events lead to well defined, self-similar morphologies remains unknown, as does the role of surface-bound ligands, which are ubiquitous in nanoparticle systems. Attempts to understand mesocrystal formation are further complicated in many systems by the presence of precursor nanoparticles with a phase distinct from that of the bulk. Some studies propose that such particles convert before attachment, whereas others attribute conversion to the attachment process itself and yet others conclude that transformation occurs after the mesocrystals exceed a characteristic size. Here we investigate mesocrystal formation by iron oxides, which are important colloidal phases in natural environments and classic examples of systems forming ubiquitous precursor phases and undergoing CPA accompanied by phase transformations. Combining in situ transmission electron microscopy (TEM) at 80 degrees Celsius with ‘freeze-and-look’ TEM, we tracked the formation of haematite (Hm) mesocrystals in the presence of oxalate (Ox), which is abundant in soils, where iron oxides are common. In this work, we find that isolated Hm particles rarely appear, but once formed, interfacial gradients at the Ox-covered surfaces drive Hm particles to nucleate repeatedly about two nanometres from the surfaces, to which they then attach, thereby generating mesocrystals. Comparison to natural and synthetic systems suggests that interface-driven pathways are widespread.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Microporous water with high gas solubilities

Liquids with permanent microporosity can absorb larger quantities of gas molecules than conventional solvents , providing new opportunities for liquid-phase gas storage, transport, and reactivity. Current approaches to designing porous liquids rely on sterically bulky solvent molecules or surface ligands and thus are not amenable to many important solvents, including water. Here, we report a generalizable thermodynamic strategy to preserve permanent microporosity and impart high gas solubilities to liquid water. Specifically, we show how the external and internal surface chemistry of microporous zeolite and metal–organic framework nanocrystals can be tailored to promote the formation of stable dispersions in water while maintaining dry networks of micropores that are accessible to gas molecules. As a result of their permanent microporosity, these aqueous fluids can concentrate gases, including oxygen (O 2 ) and carbon dioxide (CO 2 ), to much higher densities than are found in typical aqueous environments. Furthermore, when these fluids are oxygenated, record-high capacities of O 2 can be delivered to hypoxic red blood cells, highlighting one potential application of this new class of microporous liquids for physiological gas transport.

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

Machine-learning for designing nanoarchitectured materials by dealloying

Machine learning-augmented materials design is an emerging method for rapidly developing new materials. It is especially useful for designing new nanoarchitectured materials, whose design parameter space is often large and complex. Metal-agent dealloying, a materials design method for fabricating nanoporous or nanocomposite from a wide range of elements, has attracted significant interest. Here, a machine learning approach is introduced to explore metal-agent dealloying, leading to the prediction of 132 plausible ternary dealloying systems. A machine learning-augmented framework is tested, including predicting dealloying systems and characterizing combinatorial thin films via automated and autonomous machine learning-driven synchrotron techniques. This work demonstrates the potential to utilize machine learning-augmented methods for creating nanoarchitectured thin films.

36 MATERIALS SCIENCE↗