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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 397 records · Page 22
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Sulfidation and selenidation of nickel nanoparticles

Abstract Transition metal chalcogenide nanoparticles (NPs) are of interest for energy applications, including batteries, supercapacitors, and electrocatalysis. Many methods have been established for synthesizing Ni NPs, and conversion chemistry to form Ni oxide and phosphides from template Ni NPs is well‐understood. Sulfidation and selenidation of Ni NPs have been much less explored, however. We report a method for the conversion of Ni template NPs into sulfide and selenide product NPs using elemental sulfur, 1‐hexadecanthiol, thiourea, trioctylphosphine sulfide, elemental selenium, and selenourea. While maintaining mole ratios of 2 mmol sulfur/selenium precursor: mmol Ni, products with phases of Ni 3 S 2 , Ni 9 S 8 , NiS, NiSO 4 ·6H 2 O, Ni 3 S 4 , Ni 3 Se 2 , and NiSe have been obtained. The products have voids that form through the Kirkendall effect during interdiffusion. Trends relating the chemical properties of the precursors to the phases of the products have been identified. While some precursors contained phosphorus, there was no significant incorporation of phosphorus in any of the products. An increase of the NP size during sulfidation and selenidation is consistent with ripening. The application of Ni sulfide and selenide NPs as electrocatalysts for the hydrogen evolution reaction is also demonstrated.

Lynch, Brian B.↗
DOE OSTI2020

Complementing Nanoscale Galvanic Exchange with Redox Manipulation toward Architectural Control of Multimetallic Hollow Nanostructures

Galvanic exchange occurring within the confinement by nanoparticulate templates provides a unique pathway to controllably transform solid metallic nanoparticles into architecturally more sophisticated multimetallic hollow nanostructures under facile reaction conditions. As such, in this Minireview, we elaborately discuss how the nanoparticle-templated galvanic exchange can be deliberately coupled with redox manipulation to synthesize a large library of complex multimetallic hollow nanostructures exhibiting precisely tailored architectures and compositions, with a particular emphasis on important mechanistic insights concerning the dynamic interplay among multiple structure-remodeling processes that synergistically dictate the versatile nanoparticle transforming behaviors during kinetically and regioselectively modulated galvanic exchange reactions.

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

Kinetics and Mechanisms of ZnO to ZIF-8 Transformations in Supercritical CO2 Revealed by in situ X-ray Diffraction

ZIF-8 was synthesized in supercritical carbon dioxide (scCO2). In situ powder X-ray diffraction, ex situ microscopy and simulations, provides a encompassing view of the formation of ZIF-8 and intermediary ZnO@ZIF-8 composites in this nontraditional solvent. Time-resolved imaging exposed divergent physicochemical reaction pathways from previous studies of the growth of anisotropic ZIF-8 core@shell structures in traditional solvents. Synthetically relevant physio-chemical properties of scCO2 were integrated into classical nucleation theory, relating interfacial forces with 3D nu-cleation outcomes. The kinetics of crystallization were examined and displayed a characteristic signature of time- and temperature-dependent mechanisms over the extent of the reaction. Lastly, we show that subtle factors, such as the extent of reaction and the size/shape of sacrificial templates can tailor ZIF-8 composition and size, eliciting control over hierarchical porosity in a nonconventional green solvent.

Sinnwell, Michael A.↗
DOE OSTI2025

Modeling Protein–Protein and Protein–Ligand Interactions by the ClusPro Team in CASP16

ABSTRACT In the CASP16 experiment, our team employed hybrid computational strategies to predict both protein–protein and protein–ligand complex structures. For protein–protein docking, we combined physics‐based sampling—using ClusPro FFT docking and molecular dynamics—with AlphaFold (AF)‐based sampling, followed by AF‐based refinement. Our method produced numerous high‐accuracy complex models, including cases where AF alone failed, underscoring the critical role of physics‐based sampling alongside deep learning‐based refinement. For protein–ligand docking, we integrated the ClusPro LigTBM template‐based approach with a machine learning‐based confidence model for rescoring. The method preserves conserved interaction fragments derived from homologous complexes, followed by local resampling using physics‐based sampling and a diffusion model. Our template‐based strategy achieved a mean lDDT‐PLI of 0.69 across 233 targets, which was highly competitive. These results demonstrate that combining physics‐based modeling with AI‐driven refinement can significantly enhance the accuracy of both protein–protein and protein–ligand structure predictions.

Ashizawa, Ryota [Department of Applied Mathematics↗
DOE OSTI2021

Intrinsically Honeycomb-Patterned Hydrogenated Graphene

Since the advent of graphene ushered the era of 2D materials, many forms of hydrogenated graphene have been reported, exhibiting diverse properties ranging from a tunable bandgap to ferromagnetic ordering. Patterned hydrogenated graphene with micron-scale patterns has been fabricated by lithographic means. We report successful millimeter-scale synthesis of an intrinsically honeycomb-patterned form of hydrogenated graphene on Ru(0001) by epitaxial growth followed by hydrogenation is reported. Combining scanning tunneling microscopy observations with density-functional-theory (DFT) calculations, it is revealed that an atomic-hydrogen layer intercalates between graphene and Ru(0001). The result is a hydrogen honeycomb structure that serves as a template for the final hydrogenation, which converts the graphene into graphane only over the template, yielding honeycomb-patterned hydrogenated graphene (HPHG). In effect, HPHG is a form of patterned graphane. DFT calculations find that the unhydrogenated graphene regions embedded in the patterned graphane exhibit spin-polarized edge states. This type of growth mechanism provides a new pathway for the fabrication of intrinsically patterned graphene-based materials.

2D material↗
DOE OSTI2024

Facile Synthesis of Rhodium‐Based Nanocrystals in a Metastable Phase and Evaluation of Their Thermal and Catalytic Properties

Abstract Controlling the polymorphism of metal nanocrystals is a promising strategy for enhancing properties and discovering new phenomena. However, previous studies on Rh nanocrystals have focused on their thermodynamically stable face‐centered‐cubic (fcc) phase. Herein, a facile synthesis of Rh‐based nanocrystals featuring the metastable hexagonal close‐packed (hcp) phase is reported by using Ru seeds in their native hcp phase to template the deposition of Rh atoms. The success of such phase‐controlled synthesis relies on the templating effect promoted by the small lattice mismatch between Ru and Rh and the slow dropwise titration of the precursor at an elevated temperature, ensuring the layer‐by‐layer growth mode and thus the formation of a conformal hcp‐Rh shell. Faster injection rate of Rh(III) precursor leads to the formation of a rough Rh shell in the conventional fcc phase due to accelerated reaction kinetics. Considering both thermodynamic and kinetic aspects of this system, the hcp‐Rh phase is favored when the low surface energy from smooth overlayers balances the high bulk energy of the metastable phase, achieved through tight control of reaction rates and deposition patterns. These Ru hcp @Rh hcp core–shell nanocrystals demonstrate thermal stability up to 400 °C, while exhibiting higher catalytic activity toward ethanol oxidation reaction compared to Ru hcp @Rh fcc counterparts.

Nguyen, Quynh N.↗
DOE OSTI2024

Silica–Derived Nanostructured Electrode Materials for ORR, OER, HER, CO 2 RR Electrocatalysis, and Energy Storage Applications: A Review**

Silica-derived nanostructured catalysts (SDNCs) are a class of materials synthesized using nanocasting and templating techniques, which involve the sacrificial removal of a silica template to generate highly porous nanostructured materials. The surface of these nanostructures is functionalized with a variety of electrocatalytically active metal and non-metal atoms. SDNCs have attracted considerable attention due to their unique physicochemical properties, tunable electronic configuration, and microstructure. These properties make them highly efficient catalysts and promising electrode materials for next generation electrocatalysis, energy conversion, and energy storage technologies. The continued development of SDNCs is likely to lead to new and improved electrocatalysts and electrode materials. This review article provides a comprehensive overview of the recent advances in the development of SDNCs for electrocatalysis and energy storage applications. It analyzes 337,061 research articles published in the Web of Science (WoS) database up to December 2022 using the keywords “silica”, “electrocatalysts”, “ORR”, “OER”, “HER”, “HOR”, “CO 2 RR”, “batteries”, and “supercapacitors”. The review discusses the application of SDNCs for oxygen reduction reaction (ORR), oxygen evolution reaction (OER), hydrogen evolution reaction (HER), carbon dioxide reduction reaction (CO 2 RR), supercapacitors, lithium-ion batteries, and thermal energy storage applications. It concludes by discussing the advantages and limitations of SDNCs for energy applications.

25 ENERGY STORAGE↗
DOE OSTI2022

Domain-Specific Type-Safe APIs for Hierarchical Scientific Data with Modern C++

General-purpose library application programming interfaces (APIs) for self-describing hierarchical scientific data storage, such as the HDF5 and NetCDF libraries, are traditionally of runtime nature. Runtime errors for entry existence and data types are typically caught later in the development process of higher-level application-specific APIs. In this paper, we propose exploiting modern C++ metaprogramming features to add compile-time type-safety to improve the interaction with a well-defined metadata-rich scientific schema in domain-specific hierarchical datasets. We tackle two aspects of common use: (i) direct data access, (ii) flexible “in-memory” index models for efficient search and data processing. The proposed APIs use C++17’s template type auto deduction features, C++11’s enum class for type-safety and C-style preprocessor macros for generative templated code. We showcase the pros and cons of our initial work on the standard NeXus schema used for annotating and storing experimental neutron scattering data at several facilities around the world on top of HDF5. Extendable compile-time type-safe APIs are a desirable feature that could be indexed by any modern integrated development environment (IDE). Hence, such APIs can help ease the learning curve for domain scientists using a less error-prone software interaction to enhance the findability of their data without resorting to a domain-specific language (DSL).

Godoy, William↗
DOE OSTI2020

On the ATLAS top mass measurements and the potential for stealth stop contamination

The discovery of the stop — the Supersymmetric partner of the top quark — is a key goal of the physics program enabled by the Large Hadron Collider. Although much of the accessible parameter space has already been probed, all current searches assume the top mass is known. This is relevant for the “stealth stop” regime, which is characterized by decay kinematics that force the final state top quark off its mass shell; such decays would contaminate the top mass measurements. We investigate the resulting bias imparted to the template method based ATLAS approach. A careful recasting of these results shows that effect can be as large as 2.0 GeV, comparable to the current quoted uncertainty on the top mass. Thus, a robust exploration of the stealth stop splinter requires the simultaneous consideration of the impact on the top mass. Additionally, we explore the robustness of the template technique, and point out a simple strategy for improving the methodology implemented for the semi-leptonic channel.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2023

Searches for lepton-flavour-violating decays of the Higgs boson into $eτ$ and $μτ$ in $ \sqrt{s}$ = 13 TeV pp collisions with the ATLAS detector

This paper presents direct searches for lepton flavour violation in Higgs boson decays, H → eτ and H → μτ, performed using data collected with the ATLAS detector at the LHC. The searches are based on a data sample of proton-proton collisions at a centre-of-mass energy $\sqrt{s}$ = 13 TeV, corresponding to an integrated luminosity of 138 fb –1 . Leptonic (τ → ℓν ℓ ν τ ) and hadronic (τ → hadrons ν τ ) decays of the τ-lepton are considered. Two background estimation techniques are employed: the MC-template method, based on data-corrected simulation samples, and the Symmetry method, based on exploiting the symmetry between electrons and muons in the Standard Model backgrounds. No significant excess of events is observed and the results are interpreted as upper limits on lepton-flavour-violating branching ratios of the Higgs boson. The observed (expected) upper limits set on the branching ratios at 95% confidence level, $\mathcal{B}$(H → eτ) < 0.20% (0.12%) and $\mathcal{B}$(H → μτ ) < 0.18% (0.09%), are obtained with the MC-template method from a simultaneous measurement of potential H → eτ and H → μτ signals. The best-fit branching ratio difference, $\mathcal{B}$(H → μτ) → $\mathcal{B}$(H → eτ), measured with the Symmetry method in the channel where the τ-lepton decays to leptons, is (0.25 ± 0.10)%, compatible with a value of zero within 2.5σ.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Diagnostic assessment of reservoir response to fracturing: a case study from Hydraulic Fracturing Test Site (HFTS) in Midland Basin

Abstract This paper outlines a data collection and diagnostics case study involving multiple horizontal shale wells. We look at well production profiles using rate transient analysis, differences in near wellbore complexity, geologic variations within the area of interest, as well as compositional differences in the rocks based on cores obtained from within the stimulated reservoir. The Hydraulic Fracturing Test Site is a multi-well experiment involving stimulation of unconventional shale wells in the southeastern Midland portion of the Permian Basin. The targeted formations include both the upper as well as the middle Wolfcamp formations, also referred alternatively as Wolfcamp A and Wolfcamp B. Data integration and analysis shared in this paper help us understand the various geologic controls impacting well productivity, particularly the wide variance observed between the Wolfcamp A and Wolfcamp B formations. Rate transient analysis indicates similar system permeabilities for stimulated wells. However, we observe higher effective fracture half-lengths for upper Wolfcamp wells. Using observations from 3D seismic interpretations (such as pad scale faults) as well as petrophysical and image log data, we highlight the substantial differences in stimulation as we move along the well laterals from the heel toward the toe sections. These differences are further reconciled with observations from zones with high data density at the core locations through stimulated rock, as well as independent data such as microseismic emissions. At the test site, Wolfcamp A was found to be relatively quartz rich with significant heterogeneity whereas Wolfcamp B is richer in clay and organic content. This impacts the geomechanical characteristics of the rock mass with much higher natural fracture density in the shallower interval. Thus, the fracture growth is more uniform in the deeper interval and more heterogeneous with branching likely in upper interval. Increased complexity also leads to consistently better productivity from the wells in the shallower interval as demonstrated from RTA results. This case study is unique because it provides valuable insights from actual sampling of the stimulated zones in hydraulically fractured wells and helps understand impact of various factors that contribute toward variability in well production. The findings from this study provides insights into need for optimization of completion designs in the various Wolfcamp landing zones, such as optimization of cluster or fracture spacing in various Wolfcamp intervals. In addition, it provides a useful template for data collection and research direction in future field test sites of similar nature in unconventional reservoirs.

Energy & Fuels↗
DOE OSTI2020

Peptoid-based hierarchically-structured biomimetic nanomaterials: Synthesis, characterization and applications

Peptoids (or poly-N-substituted glycines) is a promising class of bioinspired sequence-defined polymers due to their highly efficient synthesis, high chemical stability, enzyme hydrolysis resistance, and biocompatibility. By tuning the side chain chemistry of peptoids, it allows for a precise control over sequences and achieving a large side-chain diversity. Due to these unique features, in the last several years, many amphiphilic peptoids were designed as highly tunable building blocks for the preparation of biomimetic nanomaterials with well-defined hierarchical structures and desired functionalities. In this paper, we provide an overview of the recent achievements in this area by dividing them into the following three aspects. First, mica- and silica-templated peptoid self-assembly was summarized. Here, the presence of inorganic substrates provides the guarantee of investigating their self-assembly mechanisms and interactions between peptoids and substrates using nanoscale characterization techniques, particularly in situ atomic force spectroscopy (AFM) and AFM-based dynamic force spectroscopy (AFM-DFS). Second, solution-phase self-assembly of peptoids into nanotubes and nanosheets was presented, as well as their self-repair properties. Third, the applications of peptoid based nanomaterials were outlined, including the construction of catalytic nanomaterials as a template and cytosolic delivery as cargoes.

36 MATERIALS SCIENCE↗
DOE OSTI2021

ZIF-8 derived carbon materials with multifunctional selective adsorption abilities

We report a family of carbon materials (denoted SFZ8C) prepared from templated carbonization of zeolitic imidazolate framework-8 (ZIF-8) in NaCl-ZnCl 2 eutectic salt. The hard-templating effect of the salt is utilized to sustain the framework and maximize the porosity during carbonization. In addition to rich surface nitrogen and oxygen species, SFZ8Cs have large specific surface area. These combined features give rise to excellent adsorption properties for light hydrocarbon gases. SFZ8Cs have both C 2 H 2 /CO 2 selective and C 3 /C 2 selective adsorption, as well as the ability to selectively remove impurities from methane. Particularly noteworthy is the excellent C 3 /C 2 selective adsorption performance by SFZ8C-900. Its propane/ethane selectivity (7.68) is on par with the best value (7.72) reported so far, yet SFZ8C-900 excels in propane uptake capacity. Here, the multifunctional properties together with the ease for the mass production make SFZ8Cs promising for a number of important applications.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Integrated Proteomic and Glycoproteomic Characterization of Human High-Grade Serous Ovarian Carcinoma

Many gene products exhibit great structural heterogeneity because of an array of modifications. These modifications are not directly encoded in the genomic template but often affect the functionality of proteins. Protein glycosylation plays a vital role in proper protein functions. However, the analysis of glycoproteins has been challenging compared with other protein modifications, such as phosphorylation. Here, we perform an integrated proteomic and glycoproteomic analysis of 83 prospectively collected high-grade serous ovarian carcinoma (HGSC) and 23 non-tumor tissues. Integration of the expression data from global proteomics and glycoproteomics reveals tumor-specific glycosylation, uncovers different glycosylation associated with three tumor clusters, and identifies glycosylation enzymes that were correlated with the altered glycosylation. In addition to providing a valuable resource, these results provide insights into the potentialroles of glycosylation in the pathogenesis of HGSC, with the possibility of distinguishing pathological outcomes of ovarian tumors from non-tumors, as well as classifying tumor clusters.

Hu, Yingwei↗
DOE OSTI2023

Controlled catalyst-transfer polymerization in graphene nanoribbon synthesis

Exercising direct control over the unusual electronic structures arising from quantum confinement effects in graphene nanoribbons (GNRs) is intimately linked to geometric boundary conditions imposed by the structure of the ribbon. Besides composition and position of substitutional dopant atoms, the symmetry of the unit cell, width, length, and termination of a GNR govern its electronic structure. Here, we present a rational design that integrates each of these interdependent variables within a modular bottom-up synthesis. Our hybrid chemical approach relies on a catalyst-transfer polymerization that establishes excellent control over length, width, and end groups. Complemented by a surface-assisted cyclodehydrogenation step, uniquely enabled by matrix-assisted direct (MAD) transfer protocols, geometry and functional handles encoded in a polymer template are faithfully mapped onto the structure of the corresponding GNR. Bond-resolved scanning tunneling microscopy (BRSTM) and spectroscopy (STS) validate the robust correlation between polymer template design and GNR electronic structure.

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

How to see hidden patterns in metamaterials with interpretable machine learning

Machine learning models can assist with metamaterials design by approximating computationally expensive simulators or solving inverse design problems. However, past work has usually relied on black box deep neural networks, whose reasoning processes are opaque and require enormous datasets that are expensive to obtain. Here, in this work, we develop two novel machine learning approaches to metamaterials discovery that have neither of these disadvantages. These approaches, called shape-frequency features and unit-cell templates, can discover 2D metamaterials with user-specified frequency band gaps. Our approaches provide logical rule-based conditions on metamaterial unit-cells that allow for interpretable reasoning processes, and generalize well across design spaces of different resolutions. The templates also provide design flexibility where users can almost freely design the fine resolution features of a unit-cell without affecting the user’s desired band gap.

36 MATERIALS SCIENCE↗
DOE OSTI2024

An engineered lactate oxidase based electrochemical sensor for continuous detection of biomarker lactic acid in human sweat and serum

Lactate levels in humans reveal intensity and duration of exertion and provide a critical readout for the severity of life-threatening illnesses such as pediatric sepsis. Using the lactate oxidase enzyme (Lox) from Aerococcus viridians, we demonstrated its functionality for lactate electrochemical sensing in physiological fluids in a lab setting. The structure and dynamics of LOx were validated by crystallography, X-ray scattering, and hydroxyl radical protein footprinting. This provided a validated protein template for understanding and designing an enzyme-based electrochemical sensing elements. Using this template, LOx enzyme variants were generated and compared. Comparison of the variants demonstrates that one exhibits effective lactate sensing at significantly reduced operating voltages. Additionally, we demonstrate that the four hexahistidine-tags on each enzyme tetramer are sufficient for immobilization to create a durable, functional sensor, with no need for a covalent attachment, enabling self-immobilization and eliminating the need for additional immobilization steps. The functionality of the LOx enzyme variants was verified at physiological lactate concentrations in both human serum (0–4 mM) and artificial sweat (0–100 mM) using 3-electrode setups for analysis of the three variants in parallel. Accuracy of measurement in both artificial sweat and human serum were high. Employing a microfluidic flow cell, we successfully monitored varying lactate levels in physiological fluids continuously over a 2h period. Overall, this optimized LOx enzyme, which self-immobilizes onto gold sensing electrodes, facilitates efficient and reliable lactate detection and continuous monitoring at reduced operating voltages suitable for further development towards commercial use.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Leveraging large language models to automate the identification of healthcare access barriers for veterans

Objective: To develop and evaluate an automated system for identifying healthcare barriers focusing on transportation issues in veterans’ clinical notes using large language models (LLMs) and to assess the impact of different prompting strategies on classification performance and explanation consistency. Methods: We developed a hybrid system combining pattern matching for templated notes with LLM analysis for free-text notes. Using 2000 manually annotated clinical notes, we compared four prompting strategies (dual-role short, dual-role long, analysis-first, analysis-only) across Mistral-7B and Llama-3.1 models. We evaluated classification performance using standard metrics and assessed explanation consistency through embedding similarity analysis. Results: The analysis-first strategy achieved superior performance, with Mistral-7B reaching an F1 score of 0.914, outperforming traditional machine learning approaches (GBM: 0.786, BERT: 0.811). LLMs demonstrated higher explanation consistency within models (mean cosine similarity 0.887–0.908) compared to cross-model similarities (0.767–0.872). Pattern matching successfully handled 6.7% of templated notes deterministically. Mistral-7B showed greater internal consistency but higher abstention rates compared to Llama-3.1. Conclusion: Requiring LLMs to analyze evidence before classification improves both accuracy and explanation consistency for identifying transportation barriers in clinical notes. This approach enables automated barrier detection at scale while providing clinically relevant explanations, supporting both population-level healthcare planning and individual patient care decisions.

Healthcare access barriers↗