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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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DOE OSTI2021

Instrument for in situ hard x-ray nanobeam characterization during epitaxial crystallization and materials transformations

Solid-phase epitaxy (SPE) and other three-dimensional epitaxial crystallization processes pose challenging structural and chemical characterization problems. The concentration of defects, the spatial distribution of elastic strain, and the chemical state of ions each vary with nanoscale characteristic length scales and depend sensitively on the gas environment and elastic boundary conditions during growth. The lateral or three-dimensional propagation of crystalline interfaces in SPE has nanoscale or submicrometer characteristic distances during typical crystallization times. Here, an in situ synchrotron hard x-ray instrument allows these features to be studied during deposition and crystallization using diffraction, resonant scattering, nanobeam and coherent diffraction imaging, and reflectivity. The instrument incorporates a compact deposition system allowing the use of short-working-distance x-ray focusing optics. Layers are deposited using radio-frequency magnetron sputtering and evaporation sources. The deposition system provides control of the gas atmosphere and sample temperature. The sample is positioned using a stable mechanical design to minimize vibration and drift and employs precise translation stages to enable nanobeam experiments. Results of in situ x-ray characterization of the amorphous thin film deposition process for a SrTiO 3 /BaTiO 3 multilayer illustrate implementation of this instrument.

47 OTHER INSTRUMENTATION↗

DOE OSTI2025

Probing Operando Electrochemical Strain Generation in α-NaFeO 2 Composite Cathodes during Cycling of Na-Ion Batteries

The transition metal oxide (TMO) cathodes in Na-ion batteries suffer from low-capacity retention. Chemo-mechanical instabilities lead to the deterioration of the electrochemical performance of TMO cathodes in Li-ion batteries. However, there is not much known about the chemo-mechanical instabilities in the TMO cathodes for Na-ion batteries. Understanding the governing forces behind the interplay between the electrochemical performance and mechanical stability in TMO cathodes is critical for the development of Na-ion batteries. Here, we synchronize the digital image correlation (DIC) technique with electrochemical analysis to capture the real-time deformation behavior of the α-NaFeO 2 cathodes during cycling. When the charge cutoff voltage is 3.6 V, the cathode experiences reversible deformations (except for the first cycle). There is negative strain (shrinkage) generation during Na extraction and positive strain (expansion) generation during the subsequent Na insertion. A detailed analysis of the potential-dependent strain rate evolution points out complicated phase transformations and nonequilibrium conditions in the α-NaFeO 2 cathodes during cycling. When the charge cutoff voltage was increased to 4.2 V, there was a rapid capacity loss and large plastic deformations in the α-NaFeO 2 cathodes. We provide an in-depth discussion about the possible mechanisms behind the chemo-mechanical instabilities in the α-NaFeO 2 . In conclusion, the correlation is critical to develop material-based strategies to mitigate instability mechanisms in TMO cathodes for Na-ion batteries.

Wable, Minal [University of Maryland Baltimore Cou↗

DOE OSTI2026

A non-intrusive framework using acoustic signals and deep learning for boiling diagnostics in visual-limited environments

Accurate monitoring of boiling heat transfer is critical for safeguarding high-power systems operating in environments where conventional optical diagnostics are hindered by radiation fields or restricted visual accessibility. This study presents a non-intrusive framework that integrates hydroacoustic sensing with deep learning to infer near-wall boiling characteristics and enable predictive thermal assessment without visual access. In a prototypical subcooled flow-boiling facility representative of the Isotope Production Facility (IPF) at Los Alamos, hydrophones capture boiling-induced acoustic emissions that are transformed into background-removed Short-Time Fourier Transform (STFT) spectrograms. A convolutional neural network (CNN) then regresses heat flux, wall superheat, and key bubble parameters directly from these spectrograms. The CNN achieved predictive accuracy under nominal conditions and demonstrated robustness and generalization under acoustic noise for Signal-to-Noise Ratios (SNRs) down to approximately 0 dB. When integrated into an ANSYS CFX wall-boiling model, the acoustically inferred parameters reproduced boiling curve and critical heat flux (CHF) values consistent with image-based benchmarks. Furthermore, the model retained reliable performance under moderate variations in bulk temperature, flow rate, and hydrophone placement, confirming its generalizability across practical boundary conditions. These results demonstrate the feasibility of hydroacoustic-based deep learning as a viable path toward real-time, radiation-tolerant boiling diagnostics and predictive thermal safety assessment in inaccessible systems such as the IPF.

42 ENGINEERING↗

DOE OSTI2020

Structural transition and recovery of Ge implanted β -Ga 2 O 3

Ion implantation-induced effects were studied in Ge implanted β-Ga 2 O 3 with the fluence and energy of 3 × 10 13 cm -2 /60 keV, 5 × 10 13 cm -2 /100 keV, and 7 × 10 13 cm -2 /200 keV using analytical electron microscopy via scanning/transmission electron microscopy, electron energy loss spectroscopy, and precession electron diffraction via TopSpin. Imaging shows an isolated band of damage after Ge implantation, which extends ~130 nm from the sample surface and corresponds to the projected range of the ions. Electron diffraction demonstrates that the entirety of the damage band is the κ phase, indicating an implantation-induced phase transition from β to κ-Ga 2 O 3 . Post-implantation annealing at 1150 °C for 60 s under the O 2 atmosphere led to a back transformation of κ to β; however, an ~17 nm damage zone remained at the sample surface. Despite the back transformation from κ to β with annealing, O K-edge spectra show changes in the fine structure between the pristine, implanted, and implanted-annealed samples, and topspin strain analysis shows a change in strain between the two samples. These data indicate differences in the electronic/chemical structure, where the change of the oxygen environment extended beyond the implantation zone (~130 nm) due to the diffusion of Ge into the bulk material, which, in turn, causes a tensile strain of 0.5%. This work provides a foundation for understanding of the effects of ion implantation on defect/phase evolution in β-Ga 2 O 3 and the related recovery mechanism, opening a window toward building a reliable device for targeted applications.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Probing Dopant Redistribution, Phase Propagation, and Local Chemical Changes in the Synthesis of Layered Oxide Battery Cathodes

Achieving the targeted control of layered oxide properties calls for more fundamental studies to mechanistically probe their evolution during their synthesis. Herein, dopant distribution, phase propagation, and local chemical changes as well as their interplay in multielement-doped LiNiO 2 materials are investigated using spectroscopic, imaging, and scattering techniques. It is shown that dopants undergo dynamic redistribution in the Ni(OH) 2 host lattice at the early stage of calcination (below 300 °C). Such redistribution behavior exhibits strong dopant-dependent characteristics, allowing for targeted surface and bulk doping control. The Ni oxidation process exhibits depth-dependent characteristics and the most rapid Ni oxidation takes place between 300 and 700 °C. Using Ni oxidation state as the proxy for the phase transformation, the buildup of heterogenous phase propagation in the early stage of calcination is shown, especially along the radial direction of secondary particles. Furthermore, the radial heterogenous phase distribution gradually decreases upon completing the calcination. However, a high degree of mosaic-like heterogeneity may still be present in the final product, departing from the perfect layered oxide. Overall, the present study offers fundamental insights into manipulating multiscale materials properties during calcination for obtaining stable, high-energy layered oxide cathodes.

36 MATERIALS SCIENCE↗

DOE OSTI2020

Lipids status and copper in a single astrocyte of the rat model for amyotrophic lateral sclerosis: Correlative synchrotron-based X-ray and infrared imaging

Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disease, causing death of motor neurons controlling voluntary muscles. The pathological mechanisms of the disease are only partially understood. The hSOD1-G93A ALS rat model is characterized by an overexpression of human mutated SOD1, causing increased vulnerability by forming intracellular protein aggregates, inducing excitotoxicity, affecting oxidative balance and disturbing axonal transport. In this study we followed the bio-macromolecular organic composition and compartmentalization together with trace metal distribution in situ in single astrocytes from the ALS rat model and compared them to the control astrocytes from nontransgenic littermates by simultaneous use of two synchrotron radiation-based methods: Fourier transform infrared microspectroscopy (SRFTIR) and hard X-ray fluorescence microscopy (XRF). Here we show that ALS cells contained more Cu, which colocalized with total lipids, increased carbonyl groups and oxidized lipids, thus implying direct involvement of Cu in oxidative stress of lipidic components without direct connection to protein aggregation in situ.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2022

Transient superconductivity at nano- and meso-scales

High-T c superconductivity is surpassed by few, if any, other unsolved problems in contemporary physics in terms of its richness, complexity, impact on other fields, and potential technological importance in energy applications. Recent discoveries reveal that the highest transition temperatures emerge under extraordinary experimental conditions such as the strong perturbation of a single atomic layer material by the underlying substrate, application of ~200 GPa pressure or ~MV/cm static electric fields, or irradiation by intense femtosecond optical fields. Research in superconductivity under extreme experimental conditions is challenging and requires the development of novel experimental methods and new theoretical tools suitable to tackle the problem. Our team has approached the challenges of transient superconductivity under intense optical fields by rethinking the types of experimental observables that are best suited to elucidate the physics of light-induced effects. This exercise has identified the need for: i) ultra-fast spatio-temporal probes enabling access to plasmonic properties and also nano-scale inhomogeneities that are ubiquitous in unconventional superconductors; ii) meso-scale structures and hybrid meta-surfaces imperative for strong enhancement of optical fields and iii) theoretical approaches suitable to explain and predict the response of superconductors under ultra-fast photo-excitation. Co-PIs have already made major advances with developing capabilities i)-iii). Therefore, our team is poised to make significant progress in transient superconductivity by exploring novel spatio-temporal effects that currently remain unattainable. Co-investigators will search for light-induced superconductivity in the high-T c cuprates to build upon spectacular recent results still lacking independent confirmations. Novel data acquisition methods combined with nano-plasmonic imaging will allow our team to test the hypothesis of photo-induced superconducting pairing. Spatio-temporal experiments will provide insights into the interplay between superconductivity and competing orders in the cuprates. Co-PIs will also investigate strong light-matter interaction and superconductivity in FeSe monolayers. Finally, we will study spatio-temporal electrodynamics of planar Josephson junctions. This latter direction will allow our team to thoroughly characterize one of the most fundamental examples of inhomogeneity in all of unconventional superconductivity. The proposed experiments in combination with theoretical analysis will provide information that is difficult to obtain using alternative methods. Basov and Averitt will carry out pump-probe spectroscopy and nano-imaging experiments. Averitt and Hone will design and fabricate state-of-the-art meta-surfaces. Theoretical and computational studies of ultrafast transient phenomena will be carried out by Millis and Fogler. Modeling of time-resolved nano-optical effects will be done by Fogler. The bulk of the requested budget will be used to support graduate students at Columbia & UCSD that will be co-supervised by co-PIs. The proposed program is transformative, since it will enable an entire suite of experiments previously either impossible or technically implausible. It will deliver critically important insights not only into mechanisms of unconventional superconductivity but also to many other correlated quantum materials.

36 MATERIALS SCIENCE↗

DOE OSTI2024

Reaction dynamics of S( 3 P) with 1,3-butadiene and isoprene: crossed-beam scattering, low-temperature flow experiments, and high-level electronic structure calculations

Sulfur atoms serve as key players in diverse chemical processes, from astrochemistry at very low temperature to combustion at high temperature. Building upon our prior findings, showing cyclization to thiophenes following the reaction of ground-state sulfur atoms with dienes, we here extend this investigation to include many additional reaction products, guided by detailed theoretical predictions. The outcomes highlight the complex formation of products during intersystem crossing (ISC) to the singlet surfaces. Here, we employed crossed-beam velocity map imaging and high-level ab initio methods to explore the reaction of S( 3 P) with 1,3-butadiene and isoprene under single-collision conditions and in low-temperature flows. For the butadiene reaction, our experimental results show the formation of thiophene via H 2 loss, a 2H-thiophenyl radical through H loss, and thioketene through ethene loss at a slightly higher collision energy compared to previous observations. Complementary Chirped-Pulse Fourier-Transform mmWave spectroscopy (CP-FTmmW) measurements in a uniform flow confirmed the formation of thioketene in the reaction at 20 K. For the isoprene reaction, we observed analogous products along with the 2H-thiophenyl radical arising from methyl loss and C 3 H 4 S (loss of ethene or H 2 + acetylene). CP-FTmmW detected the formation of thioformaldehyde via loss of 1,3-butadiene, again in the 20 K flow. Coupled-cluster calculations on the pathways found by the automated kinetic workflow code KinBot support these findings and indicate ISC to the singlet surface, leading to the generation of various long-lived intermediates, including 5-membered heterocycles.

Lang, Jinxin↗

DOE OSTI2025

Direct observation of oxygen vacancy ordering evolution in cerium oxide at varying concentrations via high-resolution transmission electron microscopy

Under illumination by a 300 keV electron beam, oxygen vacancy ordering structures are induced within cerium oxide grains. Here, our high-resolution transmission electron microscopy (HRTEM) study, supported by image simulation, reveals the evolution of these structures as vacancy concentration increases. The observed fluorite-type superlattice structures are identified as CeO 1.825 , CeO 1.75 , Ce 2 O 3 , displaying a gradient in oxygen vacancy concentration moving away from the grain surface. Correspondingly, the structural sequence transitions from Ce 2 O 3 to CeO 1.75 and then to CeO 1.825 . Without the constraints of surrounding grains, fluorite-type Ce 2 O 3 nanocrystals show a preference for transformation into an A-type trigonal structure. Notably, at temperatures up to 200°C, only the perfect fluorite structure is observed. Structural models were validated through both [110] and [001] projections. Our findings further confirm lattice expansion associated with local oxygen vacancy enrichment, which can be compensated by the formation of stacking faults, where a {111} oxygen plane is lost at defect sites.

Cerium oxide↗

DOE OSTI2023

Novel angular velocity estimation technique for plasma filaments

Magnetic field aligned filaments such as blobs and edge localized mode filaments carry significant amounts of heat and particles to the plasma facing components and they decrease their lifetime. The dynamics of these filaments determine at least a part of the heat and particle loads. These dynamics can be characterized by their translation and rotation. In this paper, we present an analysis method novel for fusion plasmas, which can estimate the angular velocity of the filaments on frame-by-frame time resolution. After pre-processing, the frames are two-dimensional (2D) Fourier-transformed, then the resulting 2D Fourier magnitude spectra are transformed to log-polar coordinates, and finally the 2D cross-correlation coefficient function (CCCF) is calculated between the consecutive frames. The displacement of the CCCF’s peak along the angular coordinate estimates the angle of rotation of the most intense structure in the frame. Further, the proposed angular velocity estimation method is tested and validated for its accuracy and robustness by applying it to rotating Gaussian-structures. The method is also applied to gas-puff imaging measurements of filaments in National Spherical Torus Experiment plasmas.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2020

Apparatus, method and system for imaging and utilization of SEM charged particles

A scanning electron microscope (SEM) system includes an SEM objective that emits an electron beam toward a sample, causing emission of charged particles including secondary electrons, Auger electrons, backscattered electrons, anions and cations. The SEM system includes electron optics elements that are configured to establish electric fields around the sample that accelerate charged particles toward a detector. A two-dimensional distribution of locations of incidence of the charged particles on the detector is indicative of energies of the charged particles and their emission angles from the sample. A three-dimensional spatial distribution of charged particles emitted from the sample is recovered by performing an Abel transform over the distribution on the detector. The energies and emission angles of the charged particles are then determined from the three-dimensional spatial distribution.

42 ENGINEERING↗

DOE OSTI2022

Stabilization of Dinuclear Rhodium and Iridium Clusters on Layered Titanate and Niobate Supports

Atomically dispersed organometallic clusters can provide well-defined nuclearity of active sites for both fundamental studies as well as new regimes of activity and selectivity in chemical transformations. More recently, dinuclear clusters adsorbed onto solid surfaces have shown novel catalytic properties resulting from the synergistic effect of two metal centers to anchor different reactant species. Difficulty in synthesizing, stabilizing, and characterizing isolated atoms and clusters without agglomeration challenges allocating catalytic performance to atomic structure. Here, we explore the stability of dinuclear rhodium and iridium clusters adsorbed onto layered titanate and niobate supports using molecular precursors. Both systems maintain their nuclearity when Statistical analysis of HAADF-STEM images revealed that rhodium and iridium dimers had mean cluster-to-cluster distances very similar to what is expected from a random distribution of atoms over a large area, indicating that they are dispersed without aggregation. The stability of dinuclear rhodium clusters supported on titanate nanosheets was also investigated by X-ray absorption fine structure (EXAFS), DRIFTS, and first-principles calculations. Both X-ray absorption spectroscopy and HAADF-STEM simulations, guided by density functional theory (DFT)-optimized structure models, suggested that rhodium dimers adsorb onto the nanosheets in an end-on binding mode that is stable up to 100 degrees C under reducing conditions. Finally, this study highlights that crystalline nanosheets derived from layered metal oxides can be used as model supports to selectively stabilize dinuclear clusters, which could have implications for heterogeneous catalysis.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2026

Generative modeling enables molecular structure retrieval from Coulomb explosion imaging

Capturing the structural changes that molecules undergo during chemical reactions in real space and time is a long-standing dream and an essential prerequisite for understanding and ultimately controlling femtochemistry. A key approach to tackle this challenging task is Coulomb explosion imaging, which benefited decisively from recently emerging high-repetition-rate X-ray free-electron laser sources. With this technique, information on the molecular structure is inferred from the momentum distributions of the ions produced by the rapid Coulomb explosion of molecules. Retrieving molecular structures from these distributions poses a highly non-linear inverse problem that remains unsolved for molecules consisting of more than a few atoms. Here, we address this challenge using a diffusion-based Transformer neural network. We show that the network reconstructs unknown molecular geometries from ion-momentum distributions with a mean absolute error below one Bohr radius, which is half the length of a typical chemical bond.

Artificial Intelligence (cs.AI)↗

DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

This presentation explores the transformative potential of autonomous electron microscopy and artificial intelligence (AI) in accelerating materials science discovery, particularly for energy applications and materials operating in extreme environments. We discuss pioneering self-driving laboratories at NREL designed to intelligently probe material synthesis and degradation across multiple scales, aiming to rapidly bridge the gap between atomic-level understanding and the development of high-performance, reliable materials. Utilizing advanced machine learning techniques, such as few-shot learning and multimodal analysis integrating imaging and spectroscopy, we demonstrate methods to extract actionable descriptors for material behavior, quantify complex microstructural evolution, and statistically link synthesis parameters to defect populations. This AI-driven approach promises to accelerate the creation of predictive materials tailored for specific missions, enabling faster development cycles and enhanced material assurance.

36 MATERIALS SCIENCE↗

DOE OSTI2025

Seeing is Believing: Autonomous Microscopy and the Data Revolution in Materials Science

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

DEEP CELLULAR RECURRENT NEURAL ARCHITECTURE FOR EFFICIENT MULTIDIMENSIONAL TIME-SERIES DATA PROCESSING

Efficient processing of time series data is a fundamental yet challenging problem in pattern recognition. Though recent developments in machine learning and deep learning have enabled remarkable improvements in processing large scale datasets in many application domains, most are designed and regulated to handle inputs that are static in time. Many real-world data, such as in biomedical, surveillance and security, financial, manufacturing and engineering applications, are rarely static in time, and demand models able to recognize patterns in both space and time. Current machine learning (ML) and deep learning (DL) models adapted for time series processing tend to grow in complexity and size to accommodate the additional dimensionality of time. Specifically, the biologically inspired learning based models known as artificial neural networks that have shown extraordinary success in pattern recognition, tend to grow prohibitively large and cumbersome in the presence of large scale multi-dimensional time series biomedical data such as EEG. Consequently, this work aims to develop representative ML and DL models for robust and efficient large scale time series processing. First, we design a novel ML pipeline with efficient feature engineering to process a large scale multi-channel scalp EEG dataset for automated detection of epileptic seizures. With the use of a sophisticated yet computationally efficient time-frequency analysis technique known as harmonic wavelet packet transform and an efficient self-similarity computation based on fractal dimension, we achieve state-of-the-art performance for automated seizure detection in EEG data. Subsequently, we investigate the development of a novel efficient deep recurrent learning model for large scale time series processing. For this, we first study the functionality and training of a biologically inspired neural network architecture known as cellular simultaneous recurrent neural network (CSRN). We obtain a generalization of this network for multiple topological image processing tasks and investigate the learning efficacy of the complex cellular architecture using several state-of-the?art training methods. Finally, we develop a novel deep cellular recurrent neural network (CDRNN) architecture based on the biologically inspired distributed processing used in CSRN for processing time series data. The proposed DCRNN leverages the cellular recurrent architecture to promote extensive weight sharing and efficient, individualized, synchronous processing of multi-source time series data. Experiments on a large scale multi-channel scalp EEG, and a machine fault detection dataset show that the proposed DCRNN offers state-of-the-art recognition performance while using substantially fewer trainable recurrent units.

Vidyaratne, Lasitha S.↗

DOE OSTI2025

Understanding Fracture Aperture and Permeability Evolution due to Carbonate Mineralization Utilizing 3D Printing

Caprock formations are a crucial part of subsurface-engineered systems. Composed largely of shale, caprocks act as natural barriers that prevent the upward migration of fluids, thereby ensuring the containment of stored substances in subsurface formations. Fractures in these formations are potential leakage pathways for stored fluids. Mineral precipitation reactions in these fractures, particularly calcite, can significantly restrict the fluid permeability, reducing leakage potential. However, predictive capabilities of mineral precipitation in fractures and associated permeability evolution are limited due to a lack of fundamental understanding of such reactions in natural samples, complicated by mineral heterogeneity and the complexity of the fracture structure. In this study, 3D-printed fracture samples are used to understand the impact of carbonate mineralization on fracture aperture and permeability evolution. Samples were printed using a digital light processing (DLP) 3D printer and commercial liquid resin. Calcite precipitation was first tested on printed 2D films before conducting plug flow column experiments aimed to understand fracture permeability changes due to mineral precipitation. Contact angle measurement and Fourier transform infrared (FTIR) spectroscopy on printed 2D films show evidence of a substantial amount of surface energy for calcite nucleation and precipitation. Surface topography analysis of printed fractured surfaces reveals comparable values, highlighting the high replicability of the printed samples. During the column experiments, the permeability reduces exponentially due to a decrease in fracture aperture. Reductions in fracture aperture estimated from effluent concentration and 3D X-ray computed tomography (CT) show comparable results. Moreover, 3D X-ray CT images suggest the impact of local flow velocities on precipitation. The insights gained from this research contribute to a deeper understanding of the permeability evolution due to carbonate mineralization in caprock formations.

3D printing↗

DOE OSTI2026

Geophysical Impacts and Spectroscopic Identification of a Hydrous Iron Sulfate on Icy Worlds

Over geologic time-scales, large volumes of exogenic sulfur ions from Io's plasma torus have been supplied to the surface of Europa and Ganymede, which, combined with recent interpretations of orbiter images, dynamical modeling, and surface-subsurface exchange, suggests further sulfur transport into the interior of the icy worlds. These observations motivate mixed-phase spectral modeling for interpreting orbiter spectroscopy data and determination of hydration states of candidate surface materials including hydrous sulfates. In this work, we present a combined experimental and theoretical study of the low temperature and high pressure vibrational spectral signature of the iron-sulfate monohydrate endmember, szomolnokite (FeSO 4 ·H 2 O). By employing synchrotron Fourier-transform infrared spectroscopy (FTIR) in the diamond anvil cell up to 23 GPa and down to 20 K, we explore the extreme range of pressure-temperature domains relevant to icy environments throughout our solar system and beyond. Combined with our density-functional theory quantum-mechanics molecular dynamics results, we demonstrate that experimentally observed infrared features in the O-H stretching region commonly associated with nH 2 O (n > 1) hydration states can be attributed to a pure monohydrate without the need for pressure-induced exsolved ice, other coexisting hydrous iron sulfates, or strong overtone and combination modes. We further discuss the possibility of lateral variations in density and shear properties on icy worlds associated with temperature variations and the high-pressure phases of kieserite group monohydrated sulfates.

Geosciences↗