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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 865 records · Page 48
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Passive heat removal in horizontally oriented micro-HTGRs

There are novel, horizontally oriented high-temperature gas-cooled micro-reactor (HTGR) designs under consideration because of their portability, simplicity, and reliable operation. These features of the micro-HTGR design can meet the fission battery attributes, such as economic, standardize, installed, unattended and reliable. Although there have been HTGR related safety studies, micro-HTGRs when horizontally oriented are expected to exhibit significantly different thermal physics. During the unavailability of helium circulation, the internal reactor core is designed to cool by block-to-block conduction and radiation, and the reactor vessel surface is cooled by the ambient air. This scenario is anticipated during the transport of the micro-HTGR in a shipping container. The conduction and radiation between the prismatic micro-HTGR blocks in the core can be influenced by variances in the thermal contacts. This work investigated the conduction within a simulated horizontal HTGR core. An experimental setup was used to validate a numerical model. The experimental setup consisted of a hexagonal assembly with scaled prismatic blocks placed within a high-temperature vacuum environment. The gaps between the blocks were well controlled and monitored. The experimental setup was designed in such a way that the temperature variation in the axial direction was minimal, such that the experiment could be observed as a 2D (r, ) heat transfer problem. The pressurized tube was equipped with Infra-Red (IR) transparent windows, and an IR camera was used to capture the spatio-temporal temperature evolution in the hexagonal block assembly during the cooldown experiments. The experimental scenario was computationally modeled with a finite element analysis (FEA) program. Once validated, the computational model was used to investigate the impact of gap conductance on overall decay heat removal. Using a conservative estimate for gap conductance value (100 W/m 2 -K) between the prismatic blocks, there is a negligible increase in temperature observed during decay heat generation.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Lateral Standing of the Pentacene Derivative 5,6,7-Trithiapentacene-13-one on Gold: A Combined STM, DFT, and NC-AFM Study

In this paper, high resolution scanning tunneling microscopy investigation of the pentacene derivative, 5,6,7-trithiapentacene-13-one (TTPO), on close-packed gold surfaces reveals nanoscale structures ranging from molecular chains at low coverage to highly ordered self-assembled molecular monolayers. Robust, thermally stable, and highly inert to photo-oxidation, TTPO is a promising organic semiconductor with potential applications in high-temperature photovoltaic devices. When adsorbed on a gold electrode, experiment and first-principle computation reveal a novel 3-D angular assembly of the TTPO molecules, with the long axis of the molecule parallel to the gold surface, distinctive from previously observed pentacene and pentacene derivative assemblies. Structures assembled are angularly dependent on TTPO molecular interactions while commensurate with the underlying gold substrate, allowing for potential tailoring of π-molecular orbital overlap through tilt-angle control. Finally, rigorous noncontact atomic force microscopy analysis was used to experimentally determine the molecular tilt of the molecule at 5 K, consistent with the calculated and observed structures at room temperature.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Adsorption Behavior of Organic Molecules: A Study of Benzotriazole on Cu(111) with Spectroscopic and Theoretical Methods

The adsorption of organic molecules on solid substrates is important to applications in fields such as catalysis, photovoltaics, corrosion inhibition, adhesion, and sensors. The molecular level description of the surface–molecule interaction and of the adsorption structures in these complex systems is crucial to understand their properties and function. Here, we present an investigation of one such system, benzotriazole (BTAH) on single-crystal Cu(111) in vacuum conditions. BTAH is the most widely used corrosion inhibitor for copper and thus a molecule of great industrial relevance. We show that the co-application of a wide range of spectroscopic techniques with theoretical methods provides unique insight in the description of the atomistic details of the adsorbed structures. Specifically, spectroscopic photoemission, absorption, and standing wave experiments combined with ab initio computational modeling allowed us to identify that benzotriazole forms overlayers of intact BTAH when deposited at low temperature, and it dissociates into BTA and H at room temperature and above. The dissociated molecule then forms complex structures of mixed chains and dimers of BTA bound to copper adatoms. Our work also reveals that copper adatoms at low concentrations, such as the theoretically predicted superstructures, cannot be resolved by means of current X-ray photoelectron spectroscopy as the modeled Cu 2p spectra are practically indistinguishable from those for a Cu surface without adatoms. Overall this study significantly deepens understanding of BTAH on Cu, a system studied for more than 50 years, and it highlights the benefits of combining spectroscopic and computational methods to obtain a complete picture of a complex adsorption system.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Exciton-Defect Interaction and Optical Properties from a First-Principles T-Matrix Approach

Understanding exciton-defect interactions is critical for optimizing optoelectronic and quantum information applications in many materials. However, ab initio simulations of material properties with defects are often limited to high defect density. Here, we study effects of exciton-defect interactions on optical absorption and photoluminescence spectra in monolayer MoS 2 using a first-principles T-matrix approach. We demonstrate that exciton-defect bound states can be captured by the disorderaveraged Green’s function with the T-matrix approximation and further analyze their optical properties. Our approach yields photoluminescence spectra in good agreement with experiments and provides a new, computationally efficient framework for simulating optical properties of disordered 2D materials from firstprinciples.

T-matrix↗
DOE OSTI2017

Optical nanoscopy of high T c cuprate nanoconstriction devices patterned by helium ion beams

Helium ion beams (HIB) focused to subnanometer scales have emerged as powerful tools for high-resolution imaging as well as nanoscale lithography, ion milling, or deposition. Quantifying irradiation effects is an essential step toward reliable device fabrication, but most of the depth profiling information is provided by computer simulations rather than the experiment. Here, we demonstrate the use of atomic force microscopy (AFM) combined with scanning near-field optical microscopy (SNOM) to provide three-dimensional (3D) dielectric characterization of high-temperature superconductor devices fabricated by HIB. By imaging the infrared dielectric response obtained from light demodulation at multiple harmonics of the AFM tapping frequency, we find that amorphization caused by the nominally 0.5 nm HIB extends throughout the entire 26.5 nm thickness of the cuprate film and by ~500 nm laterally. This unexpectedly widespread damage in morphology and electronic structure can be attributed to a helium depth distribution substantially modified by the internal device interfaces. Lastly, our study introduces AFM-SNOM as a quantitative tomographic technique for noninvasive 3D characterization of irradiation damage in a wide variety of nanoscale devices.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2018

Spring Onset Predictability in the North American Multimodel Ensemble

Abstract The predictability of spring onset is assessed using an index of its interannual variability (the “extended spring index” or SI‐x) and output from the North American Multimodel Ensemble reforecast experiment. The input data to compute SI‐x were treated with a daily joint bias correction approach, and the SI‐x outputs computed from the North American Multimodel Ensemble were postprocessed using an ensemble model output statistic approach—nonhomogeneous Gaussian regression. This ensemble model output statistic approach was used to quantify the effects of training period length and ensemble size on forecast skill. The lead time for predicting the timing of spring onset is found to be from 10 to 60 days, with the higher end of this range located along a narrow band between 35°N to 45°N in the eastern United States. Using continuous rank probability scores and skill score (SS) thresholds, this study demonstrates that ranges of positive predictability of SI‐x fall into two categories: 10–40 and 40–60 days. Using higher skill thresholds (SS equal to 0.1 and 0.2), predictability is confined to a lower range with values around 10–30 days. The postprocessing work using joint bias correction improves the predictive skill for SI‐x relative to the untreated input data set. Using nonhomogeneous Gaussian regression, a positive change in the SS is noted in regions where the skill with joint bias correction shows evidence of improvement. These findings suggest that the start of spring might be predictable on intraseasonal time horizons, which in turn could be useful for farmers, growers, and stakeholders making decisions on these time scales.

Carrillo, Carlos M.↗
DOE OSTI2020

Insights into the dynamics between viruses and their hosts in a hot spring microbial mat

Abstract Our current knowledge of host–virus interactions in biofilms is limited to computational predictions based on laboratory experiments with a small number of cultured bacteria. However, natural biofilms are diverse and chiefly composed of uncultured bacteria and archaea with no viral infection patterns and lifestyle predictions described to date. Herein, we predict the first DNA sequence-based host–virus interactions in a natural biofilm. Using single-cell genomics and metagenomics applied to a hot spring mat of the Cone Pool in Mono County, California, we provide insights into virus–host range, lifestyle and distribution across different mat layers. Thirty-four out of 130 single cells contained at least one viral contig (26%), which, together with the metagenome-assembled genomes, resulted in detection of 59 viruses linked to 34 host species. Analysis of single-cell amplification kinetics revealed a lack of active viral replication on the single-cell level. These findings were further supported by mapping metagenomic reads from different mat layers to the obtained host–virus pairs, which indicated a low copy number of viral genomes compared to their hosts. Lastly, the metagenomic data revealed high layer specificity of viruses, suggesting limited diffusion to other mat layers. Taken together, these observations indicate that in low mobility environments with high microbial abundance, lysogeny is the predominant viral lifestyle, in line with the previously proposed “Piggyback-the-Winner” theory.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Self-sustainable protonic ceramic electrochemical cells using a triple conducting electrode for hydrogen and power production

The protonic ceramic electrochemical cell (PCEC) is an emerging and attractive technology that converts energy between power and hydrogen using solid oxide proton conductors at intermediate temperatures. To achieve efficient electrochemical hydrogen and power production with stable operation, highly robust and durable electrodes are urgently desired to facilitate water oxidation and oxygen reduction reactions, which are the critical steps for both electrolysis and fuel cell operation, especially at reduced temperatures. In this study, a triple conducting oxide of PrNi 0.5 Co 0.5 O 3-δ perovskite is developed as an oxygen electrode, presenting superior electrochemical performance at 400-600°C. More importantly, the self-sustainable and reversible operation is successfully demonstrated by converting the generated hydrogen in electrolysis mode to electricity without any hydrogen addition. The excellent electrocatalytic activity is attributed to the considerable proton conduction, as confirmed by hydrogen permeation experiment, remarkable hydration behavior and computations.

08 HYDROGEN↗
DOE OSTI2024

Structure-aware graph neural network based deep transfer learning framework for enhanced predictive analytics on diverse materials datasets

Abstract Modern data mining methods have demonstrated effectiveness in comprehending and predicting materials properties. An essential component in the process of materials discovery is to know which material(s) will possess desirable properties. For many materials properties, performing experiments and density functional theory computations are costly and time-consuming. Hence, it is challenging to build accurate predictive models for such properties using conventional data mining methods due to the small amount of available data. Here we present a framework for materials property prediction tasks using structure information that leverages graph neural network-based architecture along with deep-transfer-learning techniques to drastically improve the model’s predictive ability on diverse materials (3D/2D, inorganic/organic, computational/experimental) data. We evaluated the proposed framework in cross-property and cross-materials class scenarios using 115 datasets to find that transfer learning models outperform the models trained from scratch in 104 cases, i.e., ≈90%, with additional benefits in performance for extrapolation problems. We believe the proposed framework can be widely useful in accelerating materials discovery in materials science.

Chemistry↗
DOE OSTI2018

Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specifc contextual clues and requires no training. This approach generalizes across diferent modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal refectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fdelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufcient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively.

47 OTHER INSTRUMENTATION↗
DOE OSTI2018

Role of filler and its heterostructure on moisture sorption mechanisms in polyimide films

Moisture sorption and diffusion exacerbate hygrothermal aging and can significantly alter the chemical and mechanical properties of polymeric-based components over time. In this study, we employ a multipronged multi-scale approach to model and understand moisture diffusion and sorption processes in polyimide polymers. A reactive transport model with triple-mode sorption (i.e., Henry’s, Langmuir, and pooling), experiments, and first principles atomistic computations were combined to synergistically explore representative systems of Kapton H and Kapton HN polymers. We find that the CaHPO 4 processing aid used in Kapton HN increases the total moisture uptake (~0.5 wt%) relative to Kapton H. Henry’s mode is found to play a major role in moisture uptake for both materials, accounting for >90% contribution to total uptake.However, the pooling mode uptake in Kapton HN was ~5 times higher than in Kapton H. First principles thermodynamics calculations based on density functional theory predict that water molecules chemisorb (with binding energy ~17–25 kcal/mol) on CaHPO 4 crystal surfaces. We identify significant anisotropy in surface binding affinity, suggesting a possible route to tune and mitigate moisture uptake in Kapton-based systems through controlled crystal growth favoring exposure of CaHPO 4 (101) surfaces during manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Publisher Correction: Flexible Learning-Free Segmentation and Reconstruction of Neural Volumes

Imaging is a dominant strategy for data collection in neuroscience, yielding stacks of images that often scale to gigabytes of data for a single experiment. Machine learning algorithms from computer vision can serve as a pair of virtual eyes that tirelessly processes these images, automatically detecting and identifying microstructures. Unlike learning methods, our Flexible Learning-free Reconstruction of Imaged Neural volumes (FLoRIN) pipeline exploits structure-specific contextual clues and requires no training. This approach generalizes across different modalities, including serially-sectioned scanning electron microscopy (sSEM) of genetically labeled and contrast enhanced processes, spectral confocal reflectance (SCoRe) microscopy, and high-energy synchrotron X-ray microtomography (μCT) of large tissue volumes. We deploy the FLoRIN pipeline on newly published and novel mouse datasets, demonstrating the high biological fidelity of the pipeline’s reconstructions. FLoRIN reconstructions are of sufficient quality for preliminary biological study, for example examining the distribution and morphology of cells or extracting single axons from functional data. Compared to existing supervised learning methods, FLoRIN is one to two orders of magnitude faster and produces high-quality reconstructions that are tolerant to noise and artifacts, as is shown qualitatively and quantitatively.

Shahbazi, Ali↗
DOE OSTI2021

Seeding the explosion of a high-current-density conductor in a controlled manner through the addition of micron-scale surface defects

Inhomogeneities in a current-carrying conductor promote non-uniform heating and expansion through the complex feedback between current density, electrical resistivity, Ohmic heating, temperature, and hydrodynamics. Three-dimensional-magnetohydrodynamic (3D-MHD) simulations suggest that μm-scale resistive inclusions or voids seed local overheating and through hydrodynamic explosion generate continuously growing craters which become several times larger than the initial perturbation. The ejected mass is the genesis of an electrothermally driven plasma filament which develops at lower current than plasmas on uniform surfaces adjacent to the defect. This result suggests that 1D or even 2D treatments are largely inadequate for detailed prediction of plasma formation. To test computational predictions, z-pinch experiments driven to 1 MA studied ultra-high-purity aluminum rods which were then machined to include pairs of quasi-hemispherical voids or “engineered defects (ED)” on the current-carrying surface. ED are the dominant current-density perturbation and reproducibly drive local overheating which can be compared with 3D-MHD simulation. Data from high-resolution-gated imagers of visible surface emissions confirm many simulation predictions, including the surface topography of local overheating, and the propensity for neighboring ED to prematurely source plasmas which then connect to form a plasma filament. Results also provide conditional support of theory which suggests heating similarity; that is, heating is independent of ED size for geometrically scaled ED.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Defect-induced phonon-resonant scattering and its influence on thermal transport of irradiated thorium-dioxide

Thermal transport in proton irradiated thorium-dioxide (ThO 2 ) is investigated. Using a combination of experiments and first-principles computational framework, the role of lattice defects on thermal conductivity is analyzed. A resonant-phonon scattering mechanism beyond the traditionally considered Rayleigh scattering is found to significantly influence low-temperature thermal transport in the presence of irradiation-induced point defects. The existence of localized phonon modes associated with irradiation-induced defects is suggested by the inability of the first-principles based thermal conductivity model—which considers only three-phonon interactions and phonon-defects scattering using the Tamura formalism—to predict the experimental results, unless a resonant scattering mechanism is included. The emergence of additional peaks in the Raman spectra in the proximity of phonon-resonant frequency provides further evidence for the existence of localized modes. Coupled with a microstructure evolution model, this analysis enables more accurate analysis for contrasting the contributions of different phonon scattering mechanisms across all irradiation doses and temperatures.

11 - NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2018

Interface Tracking Investigation of Geometric Effects on the Bubbly Flow in PWR Subchannels

We report that absorbing heat from the fuel rod surface, water as coolant can undergo subcooled boiling within a pressurized water reactor (PWR) fuel rod bundle. Because of the buoyancy effect, the vapor bubbles generated will then rise along and interact with the subchannel geometries. Reliable prediction of bubble behavior is of immense importance to ensure safe and stable reactor operation. However, given a complex engineering system like a nuclear reactor, it is very challenging (if not impossible) to conduct high-resolution measurements to study bubbly flows under reactor operation conditions. The lack of a fundamental two-phase-flow database is hindering the development of accurate two-phase-flow models required in more advanced reactor designs. In response to this challenge, first-principles–based numerical simulations are emerging as an attractive alternative to produce a complementary data source along with experiments. Leveraged by the unprecedented computing power offered by state-of-the-art supercomputers, direct numerical simulation (DNS), coupled with interface tracking methods, is becoming a practical tool to investigate some of the most challenging engineering flow problems. In the presented research, turbulent bubbly flow is simulated via DNS in single PWR subchannel geometries with auxiliary structures (e.g., supporting spacer grid and mixing vanes). The geometric effects these structures exert on the bubbly flow are studied with both a conventional time-averaging approach and a novel dynamic bubble tracking method. Lastly, the new insights obtained will help inform better two-phase models that can contribute to safer and more efficient nuclear reactor systems.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2023

Modeling the sequential dissociative double ionization of O 2 by ultrashort intense infrared laser pulses

A density matrix approach for sequential double ionization (DM-SDI) of molecules has been developed recently and was applied to the N 2 molecule. In this article, we extended the DM-SDI model to O 2 , which is a more complicated system to model than N 2 , due to its electronic structures and spin-orbit and laser couplings in the manifold of doubly charged states. We obtained a good agreement on the kinetic energy release spectrum of O + + O + from previous experiments. Thanks to the low computational cost of the model, we explored the mechanism behind the ionization and dissociation dynamics as well as the effects of lasers on the spectrum. Furthermore, this work will pave the way to model sequential dissociative double ionization of larger molecules and to probe molecular dynamics by measuring kinetic energy release spectra from this process.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

Recovering noise-free quantum observables

We introduce a technique for recovering noise-free observables in noisy quantum systems by combining the results of many slightly different experiments. Our approach is applicable to a variety of quantum systems, but we illustrate it with applications to quantum information and quantum sensing. The approach corresponds to repeating the same quantum evolution many times with known variations on the underlying systems' error properties, e.g., the spontaneous emission and dephasing times $T_1$ and $T_2$. As opposed to standard quantum error correction methods, which have an overhead in the number of qubits (many physical qubits must be added for each logical qubit), our method has only an overhead in the number of evaluations, allowing the overhead to, in principle, be hidden via parallelization. We show that the effective spontaneous emission $T_1$ and dephasing $T_2$ times can be increased using this method in both simulation and experiments on an actual quantum computer. We also show how to correct more complicated entangled states and how Ramsey fringes relevant to quantum sensing can be significantly extended in time.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2022

Electronic structure of intrinsic defects in c -gallium nitride: Density functional theory study without the jellium approximation

Here, we report the first nonjellium, systematic, density functional theory (DFT) study of intrinsic and extrinsic defects and defect levels in zinc-blende (cubic) gallium nitride. We use the local moment counter charge (LMCC) method, the standard Perdew-Becke-Ernzerhoff (PBE) exchange-correlation potential, and two pseudopotentials, where the Ga 3$\textit{d}$ orbitals are either in the core ($d^0$) or explicitly in the valence set ($d^{10}$). We studied 64, 216, 512, and 1000 atom supercells, and demonstrated convergence to the infinite limit, crucial for delineating deep from shallow states near band edges, and for demonstrating the elimination of finite cell-size errors. Contrary to common claims, we find that exact exchange is not required to obtain defect levels across the experimental band gap. As was true in silicon, silicon carbide, and gallium arsenide, the extremal LMCC defect levels of the aggregate of defects yield an effective LMCC defect band gap that is within 10% of the experimental gap (3.3 eV) for both pseudopotentials. We demonstrate that the gallium vacancy is more complicated than previously reported. There is dramatic metastability–a nearest-neighbor nitrogen atom shifts into the gallium site, forming an antisite, nitrogen vacancy pair, which is more stable than the simple vacancy for positive charge states. Our assessment of the $d^0$ and $d^{10}$ pseudopotentials yields minimal differences in defect structures and defect levels. The better agreement of the $d^0$ lattice constant with experiment suggests that the more computationally economical $d^0$ pseudopotentials are sufficient to achieve the fidelity possible within the physical accuracy of DFT, and thereby enable calculations in larger supercells necessary to demonstrate convergence with respect to finite size supercell errors.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗