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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 829 records · Page 46
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

The N‐terminal intrinsically disordered region of Ncb5or docks with the cytochrome b 5 core to form a helical motif that is of ancient origin

Abstract NADH cytochromeb 5 oxidoreductase (Ncb5or) is a cytosolic ferric reductase implicated in diabetes and neurological conditions. Ncb5or comprises cytochromeb 5 (b 5 ) and cytochromeb 5 reductase (b 5 R) domains separated by a CHORD‐Sgt1 (CS) linker domain. Ncb5or redox activity depends on proper inter‐domain interactions to mediate electron transfer from NADH or NADPH via FAD to heme. While full‐length human Ncb5or has proven resistant to crystallization, we have succeeded in obtaining high‐resolution atomic structures of theb 5 domain and a construct containing the CS andb 5 R domains (CS/b 5 R). Ncb5or also contains an N‐terminal intrinsically disordered region of 50 residues that has no homologs in other protein families in animals but features a distinctive, conserved L 34 MDWIRL 40 motif also present in reduced lateral root formation (RLF) protein in rice and increased recombination center 21 in baker's yeast, all attaching to ab 5 domain. After unsuccessful attempts at crystallizing a human Ncb5or construct comprising the N‐terminal region naturally fused to theb 5 domain, we were able to obtain a high‐resolution atomic structure of a recombinant rice RLF construct corresponding to residues 25–129 of human Ncb5or (52% sequence identity; 74% similarity). The structure reveals Trp 120 (corresponding to invariant Trp 37 in Ncb5or) to be part of an 11‐residue α‐helix (S 116 QMDWLKLTRT 126 ) packing against two of the four helices in theb 5 domain that surround heme (α2 and α5). The Trp 120 side chain forms a network of interactions with the side chains of four highly conserved residues corresponding to Tyr 85 and Tyr 88 (α2), Cys 124 (α5), and Leu 47 in Ncb5or. Circular dichroism measurements of human Ncb5or fragments further support a key role of Trp 37 in nucleating the formation of the N‐terminal helix, whose location in the N/b 5 module suggests a role in regulating the function of this multi‐domain redox enzyme. This study revealed for the first time an ancient origin of a helical motif in the N/b 5 module as reflected by its existence in a class of cytochromeb 5 proteins from three kingdoms among eukaryotes.

Biochemistry & Molecular Biology↗
DOE OSTI2025

Uncovering Sequence and Structural Characteristics of Fungal Expansin‐Related Proteins With Potential to Drive Substrate Targeting

Expansins loosen plant cell wall networks through disrupting non-covalent bonds between cellulose microfibrils and matrix polysaccharides. Whereas expansins were first discovered in plants, expansin-related proteins have since been identified in bacteria and fungi. The biological function of microbial expansins remains unclear; however, several studies have shown distinct binding preferences toward different structural polysaccharides. Earlier studies of bacterial expansin-related proteins uncovered sequence and structural features that correlate to substrate binding. Herein, 20 fungal expansin-related sequences were recombinantly produced in Komagataella phaffii, and the purified proteins were compared in terms of substrate binding to cellulosic and chitinous substrates. The impact of pH on the zeta potential of prioritized substrates was also measured, and Principal Component Analysis was performed to uncover correlations between protein characteristics (e.g., pI, hydrophobicity, surface charge distribution) and measured substrate binding preferences. Whereas acidic proteins with a predicted pI less than 5.0 preferentially bound to chitin, basic proteins with pI greater than 8.0 preferentially bound to xylan and xylan-containing fiber. Similar to many cellulases, binding to cellulose was correlated to relatively high aromatic amino acid content in the protein sequence and presence of a carbohydrate binding module (CBM), which in the case of expansins is a C-terminal CBM63. Whereas overall sequence characteristics could be correlated to substrate binding preference, the identity of amino acids occupying conserved positions that impact protein activity was better correlated with loosenin versus expansin classifications.

chitin↗
DOE OSTI2021

A high-order WENO-limited finite-volume algorithm for atmospheric flow using the ADER-differential transform time discretization

A high-order-accurate weighted essentially non-oscillatory (WENO) limited upwind finite-volume scheme is detailed for the compressible, nonhydrostatic, inviscid Euler equations using an arbitrary derivatives (ADER) time-stepping scheme based on differential transforms (DTs). A second-order-accurate alternating Strang dimensional splitting is compared against multidimensional simulation with 2D transport using solid body rotation of various data. The two were found to give nearly identical accuracy in orthogonal, Cartesian coordinates. Orders of convergence are demonstrated at up to ninth-order accuracy with 2D transport. 1D transport is used to confirm that error decreases monotonically with increasing order of accuracy with WENO limiting even for discontinuous data. Further, WENO limiting always decreased the error compared with simulation without limiting in the L 1 norm. A series of standard 2D compressible nonhydrostatic Euler equation test cases were validated against previous results from literature. Finally, it was demonstrated that increasing the order of accuracy led to better resolved features and increased power for kinetic energy at small wavelengths.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Hybrid Quantum–Classical Graph Transformers for Efficient Sentiment Analysis

Quantum Machine Learning (QML) offers a promising paradigm that leverages quantum computing principles to develop efficient and expressive models for learning from complex and structured data. Recent advances in natural language processing (NLP) and artificial intelligence (AI) have demonstrated capabilities in understanding, generating, and reasoning over linguistic and multimodal information. In this work, we present the Quantum Graph Transformer (QGT), a hybrid quantum–classical architecture that extends graph transformer capabilities through quantum self-attention. The QGT models variable-length sentences as token graphs, where both the embedding encoding and the self-attention mechanisms are implemented using parameterized quantum circuits (PQCs), enabling efficient contextual learning with significantly fewer trainable parameters. We train QGT using both fully connected and 𝑘 -nearest-neighbor graph structures and evaluate it on five benchmark sentiment-classification datasets. Experimental results show that QGT consistently achieves higher or comparable accuracy to existing quantum NLP models and outperforms a Classical Graph Transformer (CGT) baseline with identical architecture, achieving 29.4 × fewer parameters while requiring 3–5 × fewer samples to reach comparable performance. These findings highlight the potential of graph-based quantum models as scalable and data-efficient architectures for natural language understanding.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2023

Depth profiling and standardization from the back side of a sample for accurate analyses: Emphasis on quantifying low‐fluence, shallow implants in diamond‐like carbon

Rationale Back‐side thinning of wafers is used to eliminate issues with transient sputtering when analyzing near‐surface element distributions. Precise and accurate calibrated implants are created by including a standard reference material during the implantation. Combining these methods allows accurate analysis of low‐fluence, shallow features even if matrix effects are a concern. Methods Implanted Na (<2.0 × 10 11 ions/cm 2 , peaking <50 nm) in diamond‐like carbon (DLC) film on silicon (solar wind returned by NASA's Genesis mission) was prepared for measurement as follows. Implanted surfaces of samples were epoxied to wafers and back‐side‐thinned using physical or chemical methods. Thinned samples were then implanted with reference ions for accurate quantification of the solar wind implant. Analyses used a CAMECA IMS 7f‐GEO SIMS in depth‐profiling mode. Results Back‐side‐implanted reference ions reduced the need to change sample mounts or stage position and could be spatially separated from the solar wind implant even when measuring monoisotopic ions. Matrix effects in DLC were mitigated and the need to find an identical piece of DLC for a reference implant was eliminated. Accuracy was only limited by the back‐side technique itself. Conclusions Combining back‐side depth profiling with back‐side‐implanted internal standards aides quantification of shallow mono‐ and polyisotopic implants. This technique helps mitigate matrix effects and keeps measurement conditions consistent. Depth profile acquisition times are longer, but if sample matrices are homogeneous, procedural changes can decrease measurement times.

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

Enhancement of Magneto‐Mechanical Actuation of Micropillar Arrays by Anisotropic Stress Distribution

Abstract Magnetically active shape‐reconfigurable microarrays undergo programmed actuation according to the arrangement of magnetic dipoles within the structures, achieving complex twisting and bending deformations. Cylindrical micropillars have been widely used to date, whose circular cross‐sections lead to identical actuation regardless of the actuating direction. In this study, micropillars with triangular or rectangular cross‐sections are designed and fabricated to introduce preferential actuation directions and explore the limits of their actuation. Using such structures, controlled liquid wetting is demonstrated on micropillar surfaces. Liquid droplets pinned on magnetic micropillar arrays undergo directional spreading when the pillars are actuated as depinning of the droplets is enabled only in certain directions. The enhanced deformation due to direction dependent magneto‐mechanical actuation suggests that micropillar arrays can be fundamentally tailored to possess application specific responses and opens up opportunities to exploit more complex designs such as micropillars with polygonal cross sections. Such tunable wetting of liquids on microarray surfaces has potential to improve printing technologies via contactless reconfiguration of stamp geometry by magnetic field manipulation.

Park, Jeong Eun↗
DOE OSTI2021

High Performance Air Breathing Flexible Lithium–Air Battery

Lithium–oxygen (Li–O 2 ) batteries possess the highest theoretical energy density (3500 Wh kg –1 ), which makes them attractive candidates for modern electronics and transportation applications. In this work, an inexpensive, flexible, and wearable Li–O 2 battery based on the bifunctional redox mediator of InBr 3 , MoS 2 cathode catalyst, and Fomblin-based oxygen permeable membrane that enable long-cycle-life operation of the battery in pure oxygen, dry air, and ambient air is designed, fabricated, and tested. The battery operates in ambient air with an open system air-breathing architecture and exhibits excellent cycling up to 240 at the high current density of 1 A g –1 with a relative humidity of 75%. The electrochemical performance of the battery including deep-discharge capacity, and rate capability remains almost identical after 1000 cycle in a bending fatigue test. Furthermore, this finding opens a new direction for utilizing high performance Li–O 2 batteries for applications in the field of flexible and wearable electronics.

25 ENERGY STORAGE↗
DOE OSTI2026

Interfacial and Kinetic Origins of Voltage Loss in Neutral Zinc‐Air Batteries

Rechargeable zinc-air batteries are promising candidates for grid-scale energy storage; however, their practical deployment is limited by oxygen electrocatalysis inefficiencies and interfacial instabilities, particularly outside conventional alkaline electrolytes. Here, in this work, zinc-air batteries operating under neutral electrolyte conditions using ZnCl 2 soaked KC-PAA-PAM gel polymer electrolytes and electrochemically synthesized Ni/Fe layered double hydroxide electrocatalysts is investigated. Ni/Fe-LDH is intentionally employed as an OER-biased benchmark catalyst to diagnose electrolyte and interface driven limitations rather than as a bifunctional ORR/OER solution. Full cells exhibit highly stable cycling over hundreds of hours, yet operate at substantially suppressed charge and discharge voltages relative to the thermodynamic value. Electrochemical impedance analysis shows that ohmic losses contribute only minimally to this voltage suppression. Post-mortem X-ray photoelectron spectroscopy reveals metallic zinc accumulation on the air cathode and chloride-containing species on the anode, indicating parasitic interfacial processes. Synchrotron-based soft X-ray absorption spectroscopy confirms stable Ni 2+ and Fe 3+ oxidation states during cycling, consistent with OER-biased catalytic behavior, while neutral-electrolyte oxygen evolution measurements demonstrate strong electrolyte-induced suppression of oxygen kinetics. Together, these results show that electrolyte chemistry and cathode-side parasitic processes, rather than catalyst identity alone, dominate voltage losses in neutral zinc-air batteries, providing mechanistic insight into the fundamental challenges associated with neutral electrolyte operation.

Long duration energy storage↗
DOE OSTI2023

From Inside Out: How the Buried Interface, Shell Defects, and Surface Chemistry Conspire to Determine Optical Performance in Nonblinking Giant Quantum Dots

“Giant” or core/thick‐shell quantum dots (gQDs) are an important class of solid‐state quantum emitter characterized by strongly suppressed blinking and photobleaching under ambient conditions, and reduced nonradiative Auger processes. Together, these qualities provide distinguishing and useful functionality as single‐ and ensemble‐photon sources. For many applications, operation at elevated temperatures and under intense photon flux is desired, but performance is strongly dependent on the synthetic method employed for thick‐shell growth. Here, a comprehensive analysis of gQD structural properties “from the inside out” as a function of shell‐growth method is reported: successive ionic layer adsorption and reaction (SILAR) and high‐temperature continuous injection (HT‐CI), or sequential combinations of the two. Key correlations across synthesis methods, structural features (interfacial alloying, stacking‐fault density and surface‐ligand identity), and performance metrics (quantum yield, single‐gQD photoluminescence under thermal/photo stress, charging behavior and quantum‐optical properties) are identified. Surprisingly, it is found that interfacial alloying is the strongest indicator of gQD stability under stress, but this parameter is not the determining factor for Auger suppression. Furthermore, quantum yield is strongly influenced by surface chemistry and can approach unity even in the case of high shell‐defect density, while introduction of zinc‐blende stacking faults increases the likelihood that a gQD exhibits charged‐state emission.

77 NANOSCIENCE AND NANOTECHNOLOGY↗
DOE OSTI2024

The Central Role of Oxo Clusters in Zirconium‐Based Esterification Catalysis

Oxo clusters are a unique link between oxide nanocrystals and Metal‐Organic Frameworks (MOFs), representing the limit of downscaling each of the respective crystals. Herein, the superior catalytic activity of clusters, compared to zirconium MOF UiO‐66 and nanocrystals is shown. Focus is on esterification reactions given their general importance in consumer products and the challenge of converting large substrates. Oxo clusters have a higher surface‐to‐volume ratio than nanocrystals, rendering them more active. For large substrates, for example, oleic acid, MOF UiO‐66 has negligible catalytic activity while clusters provide almost quantitative conversion, a fact we ascribe to limited diffusion of large substrates through the MOF pores. Clusters do not suffer from limited mass transfer and we also obtain high conversion in solvent‐free reactions with sterically hindered alcohols (hexanol, 2‐ethyl hexanol, benzyl alcohol, and neopentyl alcohol). The cluster catalyst can be recovered and shows identical activity when reused. The structural integrity of the cluster is confirmed using X‐ray total scattering and pair distribution function analysis. Moreover, when homogeneous zirconium alkoxides are used as catalysts, the same oxo cluster is retrieved, showing that oxo clusters are the active catalytic species, even in previously assumed homogeneously catalyzed reactions.

Pulparayil Mathew, Jikson↗
DOE OSTI2025

Degradation and Accelerated Recovery of Surface Passivation in n+, p+, and Intrinsic Poly-Si/SiOx Passivating Contacts for Silicon Solar Cells

We report on the degradation and recovery of surface passivation of fired poly-Si/SiOx passivating contacts with hydrogen containing Al2O3 during annealing in the dark and under illumination. Upon firing to a peak temperature of 670 degrees C, the iVoc for symmetric test structures with n+, p+, and intrinsic poly-Si/SiOx contacts decreases due to a loss of surface passivation. Upon further annealing over the temperature range of 200-350 degrees C in the dark, depending on the type of doping, the surface passivation either shows further degradation followed by recovery, or direct recovery to the initial iVoc. Annealing at higher temperatures and/or higher illumination intensities accelerates the kinetics for both degradation and recovery processes. We show that the degradation and recovery processes are thermally activated and proceed identically in subsequent firing and annealing steps showing their cyclic nature. We present a series reaction model to explain the kinetics of degradation and recovery processes for n+ and intrinsic poly-Si/SiOx contacts. By fitting the model's rate expressions to the data, the determined effective activation energy barriers for degradation and recovery for n+ poly-Si/SiOx contacts in the dark are 1.24 and 1.51 eV, which are lowered under 7.5 Suns illumination to 0.76 and 1.15 eV, respectively.

14 SOLAR ENERGY↗
DOE OSTI2026

Impact of Salt Chemistry and Interlayer Architecture on Solid Electrolyte Interphase Composition and Performance

Reversible lithium metal anodes require interphases that are chemically stable, mechanically robust, and compatible with scalable processing. Here, we use slot-die-coated, salt-rich PEO interlayers to design SEI nanolayers on Cu current collectors and isolate how salt anion chemistry controls interphase composition and lithium plating/stripping behavior in architectures relevant to Li plating and anode-free cells. Mesoscale modeling shows that dendrite initiation at SEI defects is governed by a tradeoff among interphase thickness, ionic conductivity, and stiffness through defect-driven current focusing, motivating controlled formation from thin precursor layers. Using UHV-transferred XPS and ToF-SIMS depth profiling after controlled Li deposition, we find that fluorinated sulfonylimide salts (LiTFSI, LiFSI) form mixed inorganic/organic interphases containing LiF and PEO-derived alkoxides, with LiFSI producing a more LiF-dominant Li-facing surface than LiTFSI under identical conditions. In contrast, LiNO 3 -containing interlayers yield a comparatively thick nitrate-derived interphase that reduces upon Li contact to an N-rich inorganic layer (Li–N/N 3− , LiN x O y ). In Cu| |Li cells, these designed interlayers reduce interfacial resistance and improve Coulombic efficiency (CE) and critical current density (CCD) relative to uncoated controls, with LiFSI- and LiNO 3 -based interlayers providing the highest CE/CCD. Overall, the results demonstrate a manufacturing-relevant approach to engineer SEI nanolayers by salt chemistry, providing pathways to improve reversible lithium metal anodes while reducing reliance on LiTFSI.

anion chemistry↗
DOE OSTI2021

NanoSIP: NanoSIMS Applications for Microbial Biology

High-resolution imaging with secondary ion mass spectrometry (nanoSIMS) has become a standard method in systems biology and environmental biogeochemistry and is broadly used to decipher ecophysiological traits of environmental microorganisms, metabolic processes in plant and animal tissues, and cross-kingdom symbioses. When combined with stable isotope-labeling—an approach we refer to as nanoSIP—nanoSIMS imaging offers a distinctive means to quantify net assimilation rates and stoichiometry of individual cell-sized particles in both low- and high-complexity environments. While the majority of nanoSIP studies in environmental and microbial biology have focused on nitrogen and carbon metabolism (using 15 N and 13 C tracers), multiple advances have pushed the capabilities of this approach in the past decade. The development of a high-brightness oxygen ion source has enabled high-resolution metal analyses that are easier to perform, allowing quantification of metal distribution in cells and environmental particles. New preparation methods, tools for automated data extraction from large data sets, and analytical approaches that push the limits of sensitivity and spatial resolution have allowed for more robust characterization of populations ranging from marine archaea to fungi and viruses. Further, NanoSIMS studies continue to be enhanced by correlation with orthogonal imaging and ‘omics approaches; when linked to molecular visualization methods, such as in situ hybridization and antibody labeling, these techniques enable in situ function to be linked to microbial identity and gene expression. Here we present an updated description of the primary materials, methods, and calculations used for nanoSIP, with an emphasis on recent advances in nanoSIMS applications, key methodological steps, and potential pitfalls.

07 ISOTOPE AND RADIATION SOURCES↗
DOE OSTI2022

Virtual Infrastructure Twins: Software Testing Platforms for Computing-Instrument Ecosystems

Science ecosystems are being built by federating computing systems and instruments located at geographically distributed sites over wide-area networks. These computing-instrument ecosystems are expected to support complex workflows that incorporate remote, automated AI-driven science experiments. Their realization, however, requires various designs to be explored and software components to be developed, in order to support the orchestration of distributed computations and experiments. It is often too expensive, infeasible, or disruptive for the entire ecosystem to be available during the typically long software development and testing periods. We propose a Virtual Infrastructure Twin (VIT) of the ecosystem that emulates its network and computing components, and incorporates its instrument software simulators. It provides a software environment nearly identical to the ecosystem to support early development and testing, and design space exploration. We present a brief overview of previous digital infrastructure twins that culminated in the VIT concept, including (i) the virtual science network environment for developing software-defined networking solutions, and (ii) the virtual federated science instrument environment for testing the federation software stack and remote instrument control software. We briefly describe VITs for Nion microscope steering and access to GPU systems.

Rao, Nageswara↗
DOE OSTI2024

Design and Characterization of Hierarchically-Strengthened, Cast Al-Ce-Ni-Mn-Sc-Zr Alloys for High-Temperature Applications

We characterize the microstructure and mechanical properties of cast Al-Ce-Ni-Mn-Sc-Zr alloys designed for structural use above 300 °C. We first report on the effect of Ni on the as-cast phase formation, where all alloys consist of fine Al11Ce3 and Ni-rich eutectic phases, but the identity of the Ni-rich phase varies with Ni content. Furthermore, these alloys contain four coarsening-resistant strengthening phases as determined from SEM and APT investigations: (i) Al11Ce3 and (ii) Ni-rich micron-scale platelets formed during eutectic solidification, (iii) L12-Al3(Sc,Zr) nanoprecipitates formed during secondary aging, and (iv) Mn solute in the α-Al matrix. The Al-Ce-Ni-Mn-Sc-Zr alloy possesses higher microhardness and creep resistance than compositionally simpler alloys containing fewer of the strengthening phases, indicating that these strengthening mechanisms synergize to provide increased ambient- and elevated-temperature strength.

Ekaputra, Clement↗
DOE OSTI2025

Robustness of Deep Learning Classification to Adversarial Input on GPUs: Asynchronous Parallel Accumulation Is a Source of Vulnerability

The ability of machine learning (ML) classification models to resist small, targeted input perturbations—known as adversarial attacks—is a key measure of their safety and reliability. We show that floating-point non associativity (FPNA) coupled with asynchronous parallel programming on GPUs is sufficient to result in misclassification, without any perturbation to the input. Additionally, we show that this misclassification is particularly significant for inputs close to the decision boundary and that standard adversarial robustness results may be overestimated up to 4.6 when not considering machine-level details. We first study a linear classifier, before focusing on standard Graph Neural Network (GNN) architectures and datasets used in robustness assessments. We develop a novel black-box attack using Bayesian optimization to discover external workloads that can change the instruction scheduling which bias the output of reductions on GPUs and reliably lead to misclassification. Motivated by these results, we present a new learnable permutation (LP) gradient-based approach to learning floating-point operation orderings that lead to misclassifications. The LP approach provides a worst-case estimate in a computationally efficient manner, avoiding the need to run identical experiments tens of thousands of times over a potentially large set of possible GPU states or architectures. Finally, using instrumentation-based testing, we investigate parallel reduction ordering across different GPU architectures under external background workloads, when utilizing multi-GPU virtualization, and when applying power capping. Our results demonstrate that parallel reduction ordering varies significantly across architectures under the first two conditions, substantially increasing the search space required to fully test the effects of this parallel scheduler-based vulnerability. These results and the methods developed here can help to include machine-level considerations into adversarial robustness assessments, which can make a difference in safety and mission critical applications.

Shanmugavelu, Sanjif [Maxeler Technologies, a Groq↗
DOE OSTI2026

IntraShuffler: A Privacy Preserving Framework for Heterogeneous DP Federated Learning

Heterogeneous Differential Privacy (HDP) in Federated Learning (FL) allows clients to select individual privacy budgets () according to institutional policies and data sensitivity. In practice, many HDP-FL systems employ -aware server aggregation to improve model utility by re-weighting client updates according to their declared privacy budgets. However, gradient updates in FL retain structural patterns induced by non-independent and identically-distributed (non-IID) data, and these additional signals exposed by -aware aggregation create new opportunities for inference by an honest-but-curious server. In this work, we first show that a server equipped with gradient denoising and surrogate modeling can mount a Privacy Inference Attack that infers distributional attributes of clients and links updates from the same client across training rounds, measured via surrogate inference accuracy and linkage success, under realistic knowledge constraints. The Shuffle-Model has been widely studied as a defense against such inference risks by anonymizing update sources, but it is fundamentally incompatible with HDP-FL -aware aggregation. To address this challenge, we propose IntraShuffler, a middleware defense framework designed for HDP-FL systems. IntraShuffler introduces a privacy-aware shuffling mechanism that groups clients into privacy-compatible buckets and performs parameter-level shuffling within each bucket to disrupt persistent gradient structure while preserving -aware aggregation. Experiments across four different datasets show that IntraShuffler reduces gradient recoverability by over 60% and decreases surrogate inference accuracy from 0.78 to 0.33 while maintaining comparable model utility across multiple FL aggregation rules.

Riya, Farhin Farhad [ORNL]↗