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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 793 records · Page 44
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Superconductivity in the Parent Infinite-Layer Nickelate NdNiO 2

We report evidence for superconductivity with onset temperatures up to 11 K in thin films of the infinite-layer nickelate parent compound NdNiO 2 . A combination of oxide molecular beam epitaxy and atomic hydrogen reduction yields samples with high crystallinity and low residual resistivities, a substantial fraction of which exhibit superconducting transitions. We survey a large series of samples with a variety of techniques, including electrical transport, scanning transmission electron microscopy, x-ray absorption spectroscopy, and resonant inelastic x-ray scattering, to investigate the possible origins of superconductivity. We propose that superconductivity could be intrinsic to the undoped infinite-layer nickelates but suppressed by disorder due to a possibly sign-changing order parameter, a finding which would necessitate a reconsideration of the nickelate phase diagram. Another possible hypothesis is that the parent materials can be hole doped from randomly dispersed apical oxygen atoms, which would suggest an alternative pathway for achieving superconductivity. Published by the American Physical Society 2025

36 MATERIALS SCIENCE↗
DOE OSTI2021

Design and Characterization of ALD-Based Overcoats for Supported Metal Nanoparticle Catalysts

Supported metal nanoparticle (SMN) catalysts are enormously crucial for many catalytic applications. However, catalyst deactivation, caused by sintering and coke formation, is a ubiquitous problem that significantly undermines catalytic processing economics. The application of material overcoating onto supported metal nanoparticles by atomic layer deposition (ALD) offers the solution to inhibit catalyst deactivation. Herein, we discuss examples in which ALD has been used to stabilize SMN catalysts in gaseous and aqueous-phase reactions. We highlight the ALD tools and methods in coating high-surface-area catalysts. Besides, various techniques are introduced to understand better how to characterize the overcoating layer and the catalyst itself. Finally, the outlook for new techniques to characterize the ALD overcoated catalytic materials is discussed.

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

Operando XPS in reactive plasmas: The importance of the wall reactions

In this article, advancements in differential pumping and electron optics over the past few decades have enabled x-ray photoelectron spectroscopy (XPS) measurements at (near-)ambient pressures, bridging the pressure gap for characterizing realistic sample chemistries. Recently, we have demonstrated the capabilities of an ambient pressure XPS setup for in situ plasma environment measurements, allowing plasma-surface interactions to be studied in operando rather than using the traditional before-and-after analysis approach. This new “plasma-XPS” technique facilitates the identification of reaction intermediates critical for understanding plasma-assisted surface processes relevant to semiconductor nanomanufacturing, such as physical vapor deposition, etching, atomic layer deposition, and many other plasma applications. In this paper, we apply the plasma-XPS approach to monitor real-time surface chemical changes on a model Ag(111) single crystal exposed to oxidizing and reducing plasmas. We correlate surface-sensitive data with concurrent gas-phase XPS measurements and residual gas mass-spectrum analysis of species generated during plasma exposure, highlighting the significant role of plasma-induced chamber wall reactions. Ultimately, we demonstrate that plasma-XPS provides comprehensive insights into both surface and gas-phase chemistry, establishing it as a versatile and dynamic characterization tool with broad applications in microelectronics research. Finally, we outline potential enhancements and future metrology directions to advance plasma-XPS investigations further.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Strain Anisotropy Driven Spontaneous Formation of Nanoscrolls from 2D Janus Layers

2D Janus transition metal dichalcogenides (TMDs) have attracted attention due to their emergent properties arising from broken mirror symmetry and self‐driven polarization fields. While it has been proposed that their vdW superlattices hold the key to achieving superior properties in piezoelectricity and photovoltaic, available synthesis has ultimately limited their realization. Here, in this study, the first packed vdW nanoscrolls made from Janus TMDs through a simple one‐drop solution technique are reported. The results, including ab initio simulations, show that the Bohr radius difference between the top sulfur and the bottom selenium atoms within Janus ${M^S_{Se}}$ (M = Mo, W) results in a permanent compressive surface strain that acts as a nanoscroll formation catalyst after small liquid interaction. Unlike classical 2D layers, the surface strain in Janus TMDs can be engineered from compressive to tensile by placing larger Bohr radius atoms on top (${M_S^{Se}}$) to yield inverted C scrolls. Detailed microscopy studies offer the first insights into their morphology and readily formed Moiré lattices. In contrast, spectroscopy and FETs studies establish their excitonic and device properties and highlight significant differences compared to 2D flat Janus TMDs. These results introduce the first polar Janus TMD nanoscrolls and introduce inherent strain‐driven scrolling dynamics as a catalyst to create superlattices.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Thermal stability and protective properties of phenylphosphonic acid on Cu(111)

Phenylphosphonic acid (PPA) adsorbed on Cu(111) has been studied by synchrotron radiation-based techniques in combination with density functional theory calculations. Here, the dehydrogenated phenylphosphonic acid molecule (PP) strongly bound in a tridentate geometry through oxygen atoms to Cu(111) is shown to be the dominant surface species in the temperature range 150–300 °C. The stable PP adlayer substantially protects the Cu(111) surface from oxidation during exposure to ambient conditions. Thermal treatment of the PPA adlayer at 375 °C initiates molecular decomposition through several channels: P–O bond scission forming C 6 H 5 PO 2 ; C–P bond scission forming phenyl and PO 3 ; and C–H bond scission forming C 6 H 4 PO 3 . All three reaction steps have activation barriers of 1.7–1.8 eV. Small products such as O, H, and phenyl can immediately react further and desorb, while the remaining phosphonate species undergo condensation. After annealing at a higher temperature, the phosphonate group is further reduced from oxygen-rich to phosphorus-rich species, which form the majority of remaining adsorbates after 500 °C treatment.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Probing lattice defects in crystalline battery cathode using hard X-ray nanoprobe with data-driven modeling

Lattice defects, e.g., dislocations and grain boundaries, critically impact the properties of crystalline battery cathode materials. A longstanding challenge is to probe the meso–scale heterogeneity and evolution of lattice defects with sensitivity to atomic-scale details. Herein, we tackle this issue with a unique combination of X-ray nanoprobe diffractive imaging and advanced machine learning techniques. The domains with different lattice defect configuration within a single-crystalline LiCoO2 cathode particle are faithfully revealed using our approach. We further visualize the rearrangement of grain boundaries and local crystallinity upon mild thermal annealing. Furthermore, these results pave a direct way to the understanding of crystalline battery materials’ response under external stimuli with high fidelity, which provides valuable empirical guidance to defect-engineering strategies for improving the cathode materials against aggressive battery operation.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Toward a Machine Learning Approach to Interpreting X-ray Spectra of Trace Impurities by Converting XANES to EXAFS

The fact that the photoabsorption spectrum of a material contains information about the atomic structure, commonly understood in terms of multiple scattering theory, is the basis of the popular extended X-ray absorption spectroscopy (EXAFS) technique. How much of the same structural information is present in other complementary spectroscopic signals is not obvious. Here we use a machine learning approach to demonstrate that within theoretical models that accurately predict the EXAFS signal, the extended near-edge region does indeed contain the EXAFS-accessible structural information. We do this by exhibiting deep operator neural networks (DeepONets) that have learned the relationship between the extended and near edge portions of the X-ray absorption spectrum to predict the former from the latter. We find that we can accurately predict the EXAFS spectrum between 6 and 14 Å –1 from the first 6 Å –1 (≈100 eV) of the absorption spectrum of Cu 2 + substitutional defects in the Fe 3+ mineral hematite (α-Fe 2 O 3 ). This surprising finding implies that theoretical analyses of X-ray absorption spectra could be implemented that extract the same conclusions as high-quality EXAFS studies from spectra collected over a much smaller range of photon energies. This relaxes a host of experimental limitations related to the X-ray source and measurement sample, including collection time, minimum dopant concentration, source brilliance, and energy range. We describe the theoretical data sets and DeepONet construction and show that the resulting DeepONets produce EXAFS that recovers linear combination fits to experimental data with accuracy approaching the original ab initio calculations. We discuss the implications of our findings for minor constituent characterization and for understanding the information content of spectroscopic data more broadly, including how this approach might be applied to measured experimental spectra. In conclusion, to encourage similar efforts, the simulated X-ray spectra, machine learning, and fitting code are publicly available.

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

Generation of Tin-Vacancy Centers in Diamond via Shallow Ion Implantation and Subsequent Diamond Overgrowth

Group IV color centers in diamond have garnered great interest for their potential as optically active solid-state spin qubits. The future utilization of such emitters requires the development of precise site-controlled emitter generation techniques that are compatible with high-quality nanophotonic devices. This task is more challenging for color centers with large group IV impurity atoms, which are otherwise promising because of their predicted long spin coherence times without a dilution refrigerator. For example, when applied to the negatively charged tin-vacancy (SnV – ) center, conventional site-controlled color center generation methods either damage the diamond surface or yield bulk spectra with unexplained features. Here we demonstrate a novel method to generate site-controlled SnV – centers with clean bulk spectra. We shallowly implant Sn ions through a thin implantation mask and subsequently grow a layer of diamond via chemical vapor deposition. This method can be extended to other color centers and integrated with quantum nanophotonic device fabrication.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Spin splitting of dopant edge state in magnetic zigzag graphene nanoribbons

Spin-ordered electronic states in hydrogen-terminated zigzag nanographene give rise to magnetic quantum phenomena that have sparked renewed interest in carbon-based spintronics. Zigzag graphene nanoribbons (ZGNRs)-quasi one-dimensional semiconducting strips of graphene bounded by parallel zigzag edges-host intrinsic electronic edge states that are ferromagnetically ordered along the edges of the ribbon and antiferromagnetically coupled across its width. Despite recent advances in the bottom-up synthesis of GNRs featuring symmetry protected topological phases and even metallic zero mode bands, the unique magnetic edge structure of ZGNRs has long been obscured from direct observation by a strong hybridization of the zigzag edge states with the surface states of the underlying support. Here, we present a general technique to thermodynamically stabilize and electronically decouple the highly reactive spin-polarized edge states by introducing a superlattice of substitutional N-atom dopants along the edges of a ZGNR. First-principles GW calculations and scanning tunnelling spectroscopy reveal a giant spin splitting of low-lying nitrogen lone-pair flat bands by an exchange field (~850 tesla) induced by the ferromagnetically ordered edge states of ZGNRs. Finally, our findings directly corroborate the nature of the predicted emergent magnetic order in ZGNRs and provide a robust platform for their exploration and functional integration into nanoscale sensing and logic devices.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Machine learning inversion of interatomic force constants from single-crystal inelastic neutron scattering

Atomic vibrations govern many macroscopic properties of materials, but experiments to comprehensively probe them remain challenging. Inelastic neutron scattering (INS) is a powerful technique to map phonon dispersions in crystals, especially when leveraging modern time-of-flight (ToF) spectrometers with large detectors. However, efficiently and robustly extracting interatomic force constants (FCs) parameterizing phonon dynamics from experimental spectra remains a bottleneck due to the complexity and high dimensionality of ToF INS datasets. Here, we present a machine learning approach for the direct inversion of FCs from single-crystal INS measurements. The framework leverages synthetic training data generated using universal machine-learned force fields and an efficient physics-based forward model. We benchmark two neural architectures–one emphasizing structured latent representation learning and the other direct, supervised spectral regression–across simulated datasets for two materials under idealized and noisy conditions. The latent-representation model is subsequently applied to experimental single-crystal INS data on germanium. The model is shown to reproduce FCs derived from both first-principles simulations and from iterative optimization, and furthermore achieves reliable inference even from sparse, single-orientation measurements representing short data acquisitions. Analysis of the learned latent space reveals semantically continuous and physically interpretable encodings that support strong cross-domain generalization. By bridging theoretical and experimental domains, we establish a path toward rapid inversion of experimental spectra and data-driven interpretation of temperature-dependent lattice dynamics.

42 ENGINEERING↗
DOE OSTI2023

Structure of V-defects in long wavelength GaN-based light emitting diodes

The V-defect is a naturally occurring inverted hexagonal pyramid structure that has been studied in GaN and InGaN growth since the 1990s. Strategic use of V-defects in pre-quantum well superlattices or equivalent preparation layers has enabled record breaking efficiencies for green, yellow, and red InGaN light emitting diodes (LEDs) utilizing lateral injection of holes through the semi-polar sidewalls of the V-defects. In this article, we use advanced characterization techniques such as scattering contrast transmission electron microscopy, high angle annular dark field scanning transmission electron microscopy, x-ray fluorescence maps, and atom probe tomography to study the active region compositions, V-defect formation, and V-defect structure in green and red LEDs grown on (0001) patterned sapphire and (111) Si substrates. We identify two distinct types of V-defects. The “large” V-defects are those that form in the pre-well superlattice and promote hole injection, usually nucleating on mixed (Burgers vector b = ±a±c⁠) character threading dislocations. In addition, “small” V-defects often form in the multi-quantum well region and are believed to be deleterious to high-efficiency LEDs by providing non-radiative pathways. The small V-defects are often associated with basal plane stacking faults or stacking fault boxes. Furthermore, we show through scattering contrast transmission electron microscopy that during V-defect filling, the threading dislocation, which runs up the center of the V-defect, will “bend” onto one of the six {10$\overline{1}$1} semi-polar planes. In conclusion, this result is essential to understanding non-radiative recombination in V-defect engineered LEDs.

42 ENGINEERING↗
DOE OSTI2020

Spatial decomposition of magnetic anisotropy in magnets: Application to doped Fe 16 N 2

We propose a scheme of decomposition of the total relativistic energy in solids to intra- and interatomic contributions. The method is based on a site variation of such fundamental constant as the speed of light. As a practical illustration of the method, we tested such decomposition in the case of a spin-orbit interaction variation for the decomposition of the magnetic anisotropy energy (MAE) in CoPt. We further studied the α" - Fe 16 N 2 magnet doped by Bi, Sb, Co, and Pt atoms. It was found that the addition of Pt atoms can enhance the MAE by as much as five times while Bi and Sb substitutions double the total MAE. Using the proposed technique, we demonstrate the spatial distribution of these enhancements. Our studies also suggest that Sb, Pt, and Co substitutions could be synthesized by experiments.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

Machine learning defect properties in Cd-based chalcogenides

Impurity energy levels in the band gap can have serious consequences for a semiconductor's performance as a photovoltaic absorber. Data-driven approaches can help accelerate the prediction of point defect properties in common semiconductors, and thus lead to the identification of potential deep lying impurity states. In this work, we use density functional theory (DFT) to compute defect formation energies and charge transition levels of hundreds of impurities in CdX chalcogenide compounds, where X = Te, Se or S. We apply machine learning techniques on the DFT data and develop on-demand predictive models for the formation energy and relevant transition levels of any impurity atom in any site. The trained ML models are general and accurate enough to predict the properties of any possible point defects in any Cd-based chalcogenide, as we prove by testing on a few selected defects in mixed chalcogen compounds CdTe 0.5 Se 0.5 and CdSe 0.5 S 0.5 . The ML framework used in this work can be extended to any class of semiconductors.

density functional theory↗
DOE OSTI2021

Epitaxial oxide thin films for oxygen electrocatalysis: A tutorial review

Epitaxial thin films can offer unprecedented definition of an electrocatalyst surface, defining the crystallographic orientation, interaction with a current collector, and providing an atomically flat surface for quantitative spectroscopy yielding mechanistic insight. This review introduces experts in thin films to the important parameters of electrochemical setups, techniques, and methods for quantifying electrocatalytic activity and comparing it across the literature. Examples are drawn from the literature measuring oxygen electrocatalysis on epitaxial oxide thin films. Furthermore, we share best practices in the robust measurement of intrinsic activity of thin films, including methods of electrical contact and assessment of charge transport in situ. We conclude by highlighting recent insights from epitaxial films in understanding oxygen electrocatalysis.

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

Transient Unfolding and Long-Range Interactions in Viral BCL2 M11 Enable Binding to the BECN1 BH3 Domain

Viral BCL2 proteins (vBCL2s) help to sustain chronic infection of host proteins to inhibit apoptosis and autophagy. However, details of conformational changes in vBCL2s that enable binding to BH3Ds remain unknown. Using all-atom, multiple microsecond-long molecular dynamic simulations (totaling 17 μs) of the murine γ-herpesvirus 68 vBCL2 (M11), and statistical inference techniques, we show that regions of M11 transiently unfold and refold upon binding of the BH3D. Further, we show that this partial unfolding/refolding within M11 is mediated by a network of hydrophobic interactions, which includes residues that are 10 Å away from the BH3D binding cleft. We experimentally validate the role of these hydrophobic interactions by quantifying the impact of mutating these residues on binding to the Beclin1/BECN1 BH3D, demonstrating that these mutations adversely affect both protein stability and binding. To our knowledge, this is the first study detailing the binding-associated conformational changes and presence of long-range interactions within vBCL2s.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Tailoring Corrosion Resistance of Additively Manufactured Metals with Atomic Layer Deposition

The goal of this work is to develop a scalable post-manufacturing capability at LANL that utilizes atomic layer deposition (ALD) for corrosion-resistant coatings onto additively manufactured (AM) metals. This will provide fundamentally new and essential capabilities towards the interchangeability of AM metals with traditionally wrought or machined material. This will be accomplished by (1) formulating novel ALD precursor doses and film thicknesses onto as-printed coupons of AM metals and (2) providing electrochemical characterization of corrosion susceptibility as a function of deposited film thickness. While ALD is well-established, the unique aspect (risk) of this work is application to rough AM surfaces. This risk is mitigated by repeating ALD deposition cycles, where thicker coatings laterally branch, resulting in a uniform coating. This risk will be assessed in the phase 1 feasibility study, wherein the application of ALD coatings to small AM coupon surfaces will be demonstrated using existing hardware and characterization tools at Sigma. This study alone will provide ground-breaking insight on the compatibility of ALD coating techniques with AM metals. Going beyond the phase 1 study, a standalone deposition chamber will be built, so full AM components can be coated and tested. While this MFR investment is focused on corrosion mitigation, it is critical to highlight external benefits of this technique outside the scope of this proposed study. ALD has extraordinary universality, whereby almost any material can be given almost any coating or graded density coating with atomic precision; and, the only limiting factors to scalability is the size of the dosing chamber. The self-limiting reactions intrinsic to ALD offers high conformity to part geometry and tolerance, even for internal surfaces where electroplating or conventional chemical vapor deposition falls short due to differential diffusion. This uniquely positions ALD at the forefront of post-manufacturing strategies for AM components, especially for those with complex geometry and internal surfaces.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Recommendations for improving rigor and reproducibility in site specific characterization

Heterogeneous catalysis is driven by the interaction of reactant molecules and the catalyst surface. The locus of this interaction as well as the surrounding ensemble of atoms is referred to as the catalyst active site. Active site characterization attempts to distinguish active catalytic sites from inactive surface sites, to elucidate the structural and chemical nature of active sites, and to quantify active site concentration. Numerous techniques have been demonstrated to provide compositional and structural information about the active sites within a catalyst. However, each technique has its own limitations and experimental pitfalls that can lead to data misinterpretation or irreproducible results. Further, this work aims to provide an overview of the types of data that can be collected, to outline common experimental challenges and how to avoid them, and to assemble relevant references for the most used active site characterization techniques. More broadly, we aim to outline best practices for researchers to collect, interpret, and report active site characterization data in a way that provides the most benefit to the broader catalysis community. Increasing the rigor and reproducibility of active site characterization offers a strategy to better link properties with catalytic performance and to enable the community to develop consensus concerning these relationships.

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

Cryotrapping peroxide in the active site of human mitochondrial manganese superoxide dismutase crystals for neutron diffraction

Structurally identifying the enzymatic intermediates of redox proteins has been elusive due to difficulty in resolving the H atoms involved in catalysis and the susceptibility of ligand complexes to photoreduction from X-rays. Cryotrapping ligands for neutron protein crystallography combines two powerful tools that offer the advantage of directly identifying hydrogen positions in redox-enzyme intermediates without radiolytic perturbation of metal-containing active sites. However, translating cryogenic techniques from X-ray to neutron crystallography is not straightforward due to the large crystal volumes and long data-collection times. Here, methods have been developed to visualize the evasive peroxo complex of manganese superoxide dismutase (MnSOD) so that all atoms, including H atoms, could be visualized. The subsequent cryocooling and ligand-trapping methods resulted in neutron data collection to 2.30 Å resolution. The P 6 1 22 crystal form of MnSOD is challenging because it has some of the largest unit-cell dimensions ( a = b = 77.8, c = 236.8 Å) ever studied using high-resolution cryo-neutron crystallography. The resulting neutron diffraction data permitted the visualization of a dioxygen species bound to the MnSOD active-site metal that was indicative of successful cryotrapping.

59 BASIC BIOLOGICAL SCIENCES↗