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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 1,549 records · Page 86
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

Semiconductor/relaxor 0–3 type composites without thermal depolarization in Bi 0.5 Na 0.5 TiO 3 -based lead-free piezoceramics

Commercial lead-based piezoelectric materials raised worldwide environmental concerns in the past decade. Bi 0.5 Na 0.5 TiO 3 -based solid solution is among the most promising lead-free piezoelectric candidates; however, depolarization of these solid solutions is a longstanding obstacle for their practical applications. Here we use a strategy to defer the thermal depolarization, even render depolarization-free Bi 0.5 Na 0.5 TiO 3 -based 0–3-type composites. This is achieved by introducing semiconducting ZnO particles into the relaxor ferroelectric 0.94Bi 0.5 Na 0.5 TiO 3 –0.06BaTiO 3 matrix. The depolarization temperature increases with increasing ZnO concentration until depolarization disappears at 30 mol% ZnO. The semiconducting nature of ZnO provides charges to partially compensate the ferroelectric depolarization field. These results not only pave the way for applications of Bi 0.5 Na 0.5 TiO 3 -based piezoceramics, but also have great impact on the understanding of the mechanism of depolarization so as to provide a new design to optimize the performance of lead-free piezoelectrics.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Stabilization of high-performance rock-salt LiMnSbTe3 thermoelectrics with embedded van der Waals-like gaps

Rock-salt-structured compounds like lead chalcogenides are promising thermoelectrics, as their high symmetry, strong anharmonicity, and favorable phase behavior collectively lead to high performance by enabling large power factors and ultralow thermal conductivity. Here, we report LiMnSbTe 3 , a new rock-salt semiconductor stabilized through targeted chemical design by combining hexagonal MnTe with cubic LiSbTe 2 . Embedded in the highsymmetry matrix, van der Waals-like gaps form due to Sb 2 Te 3 nanoscale segregation, which acts as effective phonon-scattering centers, leading to a low lattice thermal conductivity of 0.37Wm- 1 K -1 at 873 K with alloy scattering from disordered cations. The ordered local structure of Sb2Te 3 -type vdW-like gaps and the cross-gap interaction facilitate the carrier transport. Aided by energyconverged valence bands and a paramagnon drag effect, high Seebeck coefficients and enhanced power factor can be achieved, leading to a high ZT of 1.2 at 873 K. Furthermore, introducing Mn deficiency increases ZT to 1.5, highlighting the potential for higher performance through optimized doping or alloying. A segmented single-leg thermoelectric module achieves an output power density of 0.52 Wcm -2 and an efficiency of 8.7% under ΔT of 478 K, further demonstrating its promising thermoelectric applications.

42 ENGINEERING↗
DOE OSTI2024

Active learning of ternary alloy structures and energies

Abstract Machine learning models with uncertainty quantification have recently emerged as attractive tools to accelerate the navigation of catalyst design spaces in a data-efficient manner. Here, we combine active learning with a dropout graph convolutional network (dGCN) as a surrogate model to explore the complex materials space of high-entropy alloys (HEAs). We train the dGCN on the formation energies of disordered binary alloy structures in the Pd-Pt-Sn ternary alloy system and improve predictions on ternary structures by performing reduced optimization of the formation free energy, the target property that determines HEA stability, over ensembles of ternary structures constructed based on two coordinate systems: (a) a physics-informed ternary composition space, and (b) data-driven coordinates discovered by the Diffusion Maps manifold learning scheme. Both reduced optimization techniques improve predictions of the formation free energy in the ternary alloy space with a significantly reduced number of DFT calculations compared to a high-fidelity model. The physics-based scheme converges to the target property in a manner akin to a depth-first strategy, whereas the data-driven scheme appears more akin to a breadth-first approach. Both sampling schemes, coupled with our acquisition function, successfully exploit a database of DFT-calculated binary alloy structures and energies, augmented with a relatively small number of ternary alloy calculations, to identify stable ternary HEA compositions and structures. This generalized framework can be extended to incorporate more complex bulk and surface structural motifs, and the results demonstrate that significant dimensionality reduction is possible in thermodynamic sampling problems when suitable active learning schemes are employed.

Chemistry↗
DOE OSTI2026

Operating advanced scientific instruments with AI agents that learn on the job

Advanced scientific user facilities, such as next generation X-ray light sources and self-driving laboratories, are revolutionizing scientific discovery by automating routine tasks and enabling rapid experimentation and characterizations. However, these facilities must continuously evolve to support new experimental workflows, adapt to diverse user projects, and meet growing demands for more intricate instruments and experiments. This continuous development introduces significant operational complexity, necessitating a focus on usability, reproducibility, and intuitive human-instrument interaction. In this work, we explore the integration of agentic AI, powered by Large Language Models (LLMs), as a transformative tool to achieve this goal. We present our approach to developing a human-in-the-loop pipeline for operating advanced instruments including an X-ray nanoprobe beamline and an autonomous robotic station dedicated to the design and characterization of materials. Specifically, we evaluate the potential of various LLMs as trainable scientific assistants for orchestrating complex, multi-task workflows, which also include multimodal data, optimizing their performance through optional human input and iterative learning. We demonstrate the ability of AI agents to bridge the gap between advanced automation and user-friendly operation, paving the way for more adaptable and intelligent scientific facilities.

Large Language Models↗
DOE OSTI2022

Intrinsic ferroelectricity in Y-doped HfO 2 thin films

Ferroelectric HfO 2 -based materials hold great potential for the widespread integration of ferroelectricity into modern electronics due to their compatibility with existing Si technology. Earlier work indicated that a nanometre grain size was crucial for the stabilization of the ferroelectric phase. This constraint, associated with a high density of structural defects, obscures an insight into the intrinsic ferroelectricity of HfO 2 -based materials. Here we demonstrate that stable and enhanced polarization can be achieved in epitaxial HfO 2 films with a high degree of structural order (crystallinity). In this work, an out-of-plane polarization value of 50 μC cm –2 has been observed at room temperature in Y-doped HfO 2 (111) epitaxial thin films, with an estimated full value of intrinsic polarization of 64 μC cm –2 , which is in close agreement with density functional theory calculations. The crystal structure of films reveals the $Pca2_1$ orthorhombic phase with small rhombohedral distortion, underlining the role of the structural constraint in stabilizing the ferroelectric phase. Our results suggest that it could be possible to exploit the intrinsic ferroelectricity of HfO 2 -based materials, optimizing their performance in device applications.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Two excited-state datasets for quantum chemical UV-vis spectra of organic molecules

Abstract We present two open-source datasets that provide time-dependent density-functional tight-binding (TD-DFTB) electronic excitation spectra of organic molecules. These datasets represent predictions of UV-vis absorption spectra performed on optimized geometries of the molecules in their electronic ground state. The GDB-9-Ex dataset contains a subset of 96,766 organic molecules from the original open-source GDB-9 dataset. The ORNL_AISD-Ex dataset consists of 10,502,904 organic molecules that contain between 5 and 71 non-hydrogen atoms. The data reveals the close correlation between the magnitude of the gaps between the highest occupied molecular orbital (HOMO) and the lowest unoccupied molecular orbital (LUMO), and the excitation energy of the lowest singlet excited state energies quantitatively. The chemical variability of the large number of molecules was examined with a topological fingerprint estimation based on extended-connectivity fingerprints (ECFPs) followed by uniform manifold approximation and projection (UMAP) for dimension reduction. Both datasets were generated using the DFTB+ software on the “Andes” cluster of the Oak Ridge Leadership Computing Facility (OLCF).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Wind and structural loads data measured on parabolic trough solar collectors at an operational power plant

Abstract Wind loading is a primary contributor to structural design costs of concentrating solar-thermal power collectors, such as heliostats and parabolic troughs. These structures must resist the mechanical forces generated by turbulent wind, while the reflector surfaces must maintain optimal optical performance. Studying wind-driven loads at a full-scale, operational concentrating solar-thermal power plant provides insights into the wind impact on the solar collector field beyond the capabilities of wind tunnel tests or state-of-the-art simulations. We conducted comprehensive field measurements of the atmospheric turbulent wind conditions and the resulting structural wind loads on parabolic troughs at the Nevada Solar One plant over a two-year period. The measurement setup included meteorological masts and structural load sensors on four trough rows. Additionally, a lidar scanned the horizontal plane above the trough field. In this study, we describe the high-resolution dataset characterizing the complex flow field and resulting structural loads. This first-of-its-kind dataset will enhance the understanding of wind loading on collector structures and will help in designing the next-generation solar collectors and photovoltaic trackers.

14 SOLAR ENERGY↗
DOE OSTI2025

Quantum mechanical dataset of 836k neutral closed-shell molecules with up to 5 heavy atoms from C, N, O, F, Si, P, S, Cl, Br

Abstract We introduce the Vector-QM24 (VQM24) dataset comprehensively covering all possible neutral closed-shell small organic and inorganic molecules with up to five heavy (p-block) atoms: C, N, O, F, Si, P, S, Cl, Br. All valid stoichiometries, Lewis-rule-consistent graphs, and stable conformers (identified via GFN2-xTB) were enumerated combinatorially, yielding 577k conformational isomers spanning 258k constitutional isomers and 5,599 unique stoichiometries. DFT (ωB97X-D3/cc-pVDZ) optimizations were performed for all, and diffusion quantum Monte Carlo (DMC@PBE0(ccECP/cc-pVQZ)) energies are provided for 10,793 lowest-energy conformers with up to 4 heavy atoms. VQM24 includes structures, vibrational modes, rotational constants, thermodynamic properties (Gibbs free energies, enthalpies, ZPVEs, entropies, heat capacities), and electronic properties such as atomization, electron interaction, exchange-correlation, dispersion energies, multipole moments (dipole to hexadecapole), alchemical potentials, Mulliken charges, and wavefunctions. Machine learning models of atomization energies on this dataset reveal significantly higher complexity than QM9, with none achieving chemical accuracy. VQM24 offers a rigorous, high-fidelity benchmark for evaluating quantum machine learning models.

Science & Technology - Other Topics↗
DOE OSTI2019

Structural determination of Enzyme-Graphene Nanocomposite Sensor Material

State-of-the-art ultra-sensitive blood glucose-monitoring biosensors, based on glucose oxidase (GOx) covalently linked to a single layer graphene (SLG), will be a valuable next generation diagnostic tool for personal glycemic level management. We report here our observations of sensor matrix structure obtained using a multi-physics approach towards analysis of small-angle neutron scattering (SANS) on graphene-based biosensor functionalized with GOx under different pH conditions for various hierarchical GOx assemblies within SLG. We developed a methodology to separately extract the average shape of GOx molecules within the hierarchical assemblies. The modeling is able to resolve differences in the average GOx dimer structure and shows that treatment under different pH conditions lead to differences within the GOx at the dimer contact region with SLG. The coupling of different analysis methods and modeling approaches we developed in this study provides a universal approach to obtain detailed structural quantifications, for establishing robust structure-property relationships. This is an essential step to obtain an insight into the structure and function of the GOx-SLG interface for optimizing sensor performance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Evaluation of polyanionic cyclodextrins as high affinity binding scaffolds for fentanyl

Abstract Cyclodextrins (CDs) have been previously shown to display modest equilibrium binding affinities ( K a ~ 100–200 M -1 ) for the synthetic opioid analgesic fentanyl. In this work, we describe the synthesis of new CDs possessing extended thioalkylcarboxyl or thioalkylhydroxyl moieties and assess their binding affinity towards fentanyl hydrochloride. The optimal CD studied displays a remarkable affinity for the opioid of K a = 66,500 M −1 , the largest value reported for such an inclusion complex to date. One dimensional 1 H Nuclear Magnetic Resonance (NMR) as well as Rotational Frame Overhauser Spectroscopy (2D-ROESY) experiments supported by molecular dynamics (MD) simulations suggest an unexpected binding behavior, with fentanyl able to bind the CD interior in one of two distinct orientations. Binding energies derived from the MD simulations work correlate strongly with NMR-derived affinities highlighting its utility as a predictive tool for CD candidate optimization. The performance of these host molecules portends their utility as platforms for medical countermeasures for opioid exposure, as biosensors, and in other forensic science applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

An open-source data storage and visualization platform for collaborative qubit control

Developing collaborative research platforms for quantum bit control is crucial for driving innovation in the field, as they enable the exchange of ideas, data, and implementation to achieve more impactful outcomes. Furthermore, considering the high costs associated with quantum experimental setups, collaborative environments are vital for maximizing resource utilization efficiently. However, the lack of dedicated data management platforms presents a significant obstacle to progress, highlighting the necessity for essential assistive tools tailored for this purpose. Current qubit control systems are unable to handle complicated management of extensive calibration data and do not support effectively visualizing intricate quantum experiment outcomes. In this paper, we introduce Qubit Control Storage and Visualization ( QubiCSV ), a platform specifically designed to meet the demands of quantum computing research, focusing on the storage and analysis of calibration and characterization data in qubit control systems. As an open-source tool, QubiCSV facilitates efficient data management of quantum computing, providing data versioning capabilities for data storage and allowing researchers and programmers to interact with qubits in real time. The insightful visualization are developed to interpret complex quantum experiments and optimize qubit performance. QubiCSV not only streamlines the handling of qubit control system data but also improves the user experience with intuitive visualization features, making it a valuable asset for researchers in the quantum computing domain.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Deployment and validation of predictive 6-dimensional beam diagnostics through generative reconstruction with standard accelerator elements

Understanding the 6-dimensional phase space distribution of particle beams is essential for optimizing accelerator performance. Conventional diagnostics such as use of transverse deflecting cavities offer detailed characterization but require dedicated hardware and space. Generative phase space reconstruction (GPSR) methods have shown promise in beam diagnostics, yet prior implementations still rely on such components. Here we present the first experimental implementation and validation of the GPSR methodology, realized by the use of standard accelerator elements including accelerating cavities and dipole magnets, to achieve complete 6-dimensional phase space reconstruction. Through simulations and experiments at the Pohang Accelerator Laboratory X-ray Free Electron Laser facility, we successfully reconstruct complex, nonlinear beam structures. Furthermore, we validate the methodology by predicting independent downstream measurements excluded from training, revealing the reconstruction closely resembling ground truth. This advancement establishes a pathway for predictive diagnostics across beamline segments while reducing hardware requirements and expanding applicability to various accelerator facilities.

Kim, Seongyeol [Pohang Univ. of Science and Techno↗
DOE OSTI2022

A general synthesis of single atom catalysts with controllable atomic and mesoporous structures

The control of single metal atomic sites has been extensively studied in the field of single atom catalysts. By contrast, the precise control of the mesoporous structure in the matrix material, which directly correlates with mass diffusions and may play a dominant role in delivering industrially relevant reaction rates, has been overlooked. In this work, we report a general method for the synthesis of a single atom catalyst with control of the atomic structure of the single atomic site as well as the mesoporous structure of the carbon support for optimized catalytic performance. Various combinations of metal centres (Ni, Co, Mn, Zn, Cu, Sc and Fe) and mass diffusion channels in two dimensions and three dimensions were achieved. Using CO 2 reduction to CO as an example, our Ni single atom catalyst with three-dimensional diffusion channels delivered a practical current of 350 mA cm –2 while maintaining a 93% CO Faradaic efficiency, representing a sixfold improvement in turnover frequency compared to two-dimensional counterparts.

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

CHAPTER 1: Introduction. In: Organic Thermoelectric Materials

© The Royal Society of Chemistry 2020. Organic thermoelectric materials and generators are a promising field of research due to numerous attractive characteristics that suggest the ability to overcome the shortcomings of existing inorganic TE systems. This book aims to reprise some of the key directions in this field and provide some context on the key developments and opportunities that remain to be explored. Therefore, to facilitate comprehension of the book, this introduction chapter will present a brief overview of essential topics spanning the fields of chemistry, materials science, engineering, and physics, including a brief historical overview of organic TE materials. TE measurement techniques, including carrier-energy and thermal transport in organic TE systems, will also be briefly discussed, as they are relevant to understanding what challenges remain in understanding and optimizing the performance of these organic TE materials.

Choi, J↗
DOE OSTI2015

Designing interchain and intrachain properties of conjugated polymers for latent optical information encoding

Building molecular-design insights for controlling both the intrachain and the interchain properties of conjugated polymers (CPs) is essential to determine their characteristics and to optimize their performance in applications. However, most CP designs have focused on the conjugated main chain to control the intrachain properties, while the design of side chains is usually used to render CPs soluble, even though the side chains critically affect the interchain packing. Here, we present a straightforward and effective design strategy for modifying the optical and electrochemical properties of diketopyrrolopyrrole-based CPs by controlling both the intrachain and interchain properties in a single system. The synthesized polymers, P1, P2 and P3, show almost identical optical absorption spectra in solution, manifesting essentially the same intrachain properties of the three CPs having restricted effective conjugation along the main chain. However, the absorption spectra of CP films are gradually tuned by controlling the interchain packing through the side-chain design. Here, based on the tailored optical properties, we demonstrate the encoding of latent optical information utilizing the CPs as security inks on a silica substrate, which reveals and conceals hidden information upon the reversible aggregation/deaggregation of CPs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Modeling gas-diffusion electrodes for CO 2 reduction

CO 2 reduction conducted in electrochemical cells with planar electrodes immersed in an aqueous electrolyte is severely limited by mass transport across the hydrodynamic boundary layer. This limitation can be minimized by use of vapor-fed, gas-diffusion electrodes (GDEs), enabling current densities that are almost two orders of magnitude greater at the same applied cathode overpotential than what is achievable with planar electrodes in an aqueous electrolyte. The addition of porous cathode layers, however, introduces a number of parameters that need to be tuned in order to optimize the performance of the GDE cell. In this study, we develop a multiphysics model for gas diffusion electrodes for CO 2 reduction and used it to investigate the interplay between species transport and electrochemical reaction kinetics. The model demonstrates how the local environment near the catalyst layer, which is a function of the operating conditions, affects cell performance. Finally, we also examine the effects of catalyst layer hydrophobicity, loading, porosity, and electrolyte flowrate to help guide experimental design of vapor-fed CO 2 reduction cells.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Continuous electrochemical heat engines

Given the large magnitude of energy in waste heat, its efficient conversion to electrical power offers a significant opportunity to lower greenhouse gas emissions. Furthermore, it has been difficult to optimize the performance of new direct energy conversion approaches because of the coupling between entropy change and thermal and electrical transport in continuously operating devices. With electrochemical cells driving flowing electrolytes in symmetric redox reactions at different temperatures, we demonstrate two continuous electrochemical heat engines that operate at 10–50 °C and at 500–900 °C, respectively. Simulations of kilowatt-scale systems using electrochemical cells stacked in series suggest efficiencies over 30% of the Carnot limit and areal power densities competitive with solid-state thermoelectrics at maximum power. Although entropy change, thermal transport and electrical transport are inherently coupled in solid-state thermoelectrics, they can be somewhat circumvented in electrochemical systems, thus offering new opportunities to engineer efficient energy conversion systems.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Reforming of methane with carbon dioxide over cerium oxide promoted nickel nanoparticles deposited on 4-channel hollow fibers by atomic layer deposition

Ni nanoparticles were deposited on four-channel structured α-Al2O3 hollow fibers by atomic layer deposition (ALD). CeO 2 was loaded by a liquid phase incipient wetness method to promote Ni catalysts for dry reforming of methane. For Ni/Al 2 O 3 prepared by ALD, inactive NiAl2O4 that originated from the Ni ALD process was incompletely reduced. The introduced CeO 2 was found to weaken the NiO–Al 2 O 3 interaction, free NiO from NiO–Al 2 O 3 or NiAl 2 O 4 , and improve the reducibility of NiO. The higher reducibility of NiAl 2 O 4 , tuned by CeO 2 , further activated the catalyst during DRM, because a larger proportion of NiAl 2 O 4 was gradually reduced to metallic nickel by the reaction products. The optimal catalytic performance reached a methane reforming rate of 2410 L h -1 gNi -1 at 850 °C. The CeO 2 promoted catalyst also exhibited an excellent performance after regeneration. In addition, the inhibition effect of CeO 2 on coke formation was observed, due to the enhanced CO 2 dissociative adsorption by CeO 2 .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗