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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 1,045 records · Page 58
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Field-free platform for Majorana-like zero mode in superconductors with a topological surface state

Superconducting materials exhibiting topological properties are emerging as an exciting platform to realize fundamentally new excitations from topological quantum states of matter. In this letter, we explore the possibility of a field-free platform for generating Majorana zero energy excitations by depositing magnetic Fe impurities on the surface of candidate topological superconductors, LiFeAs and PbTaSe 2 . We use scanning tunneling microscopy to probe localized states induced at the Fe adatoms on the atomic scale and at sub-Kelvin temperatures. We find that each Fe adatom generates a striking zero-energy bound state inside the superconducting gap, which do not split in magnetic fields up to 8 T, underlining a nontrivial topological origin. Finally, our findings point to magnetic Fe adatoms evaporated on bulk superconductors with topological surface states for exploring Majorana zero modes and quantum information science under field-free conditions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2022

Nucleation of Grain Boundary Phases

Here, we derive a theory that describes homogeneous nucleation of grain boundary (GB) phases. Our analysis takes account of the energy resulting from the GB phase junction, the line defect separating two different GB structures, which is necessarily a dislocation as well as an elastic line force due to the jump in GB stresses. The theory provides analytic forms for the elastic interactions and the core energy of the GB phase junction that, along with the change in GB energy, determines the nucleation barrier. We apply the resulting nucleation model to simulations of GB phase transformations in tungsten. Our theory explains why under certain conditions GBs cannot spontaneously change their structure even to a lower energy state.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Monte Carlo Simulations of Crystal Defects in Open Ensembles

Zero- and two-dimensional crystal defects form in open statistical ensembles, such as the grand canonical, that are usually inaccessible with conventional simulation techniques. This longstanding challenge is overcome with a new Hamiltonian Monte Carlo method that samples energy-biased gradual transformations. In conclusion, the method enables free energy calculations for nonideal point defects and the direct prediction of finite-temperature interface structures.

Grain boundaries↗
DOE OSTI2023

Amazon drought resilience – emerging results point to new empirical needs

Will climate change turn tropical forests from a carbon sink to a source (Pan et al., 2011)? Tropical forests cycle more carbon, water, and energy than any other biome (Bonan, 2008). The Amazon is the largest tropical forest, spanning one-third of South America, and any perturbation in its function has repercussions on the global climate. Mild and severe droughts in the Amazon are predicted to double and triple in area, respectively, by 2100, while the area under wet extremes will increase (Duffy et al., 2015). Our understanding of the role of plants' hydrological environments in determining their drought response, however, is limited (Chitra-Tarak et al., 2018, 2021). An important review by Costa et al. (2023; pp. 714–733) published in this issue of New Phytologist highlights that shallow-water table (WT) forests constitute c. 50% of the Amazon and may act as hydrological refugia during droughts, yet they have been neglected in Amazon forest research to date. Emerging results suggest that the shallow-WT forests that dominate the Amazon basin may increase in productivity under moderate droughts due to relief from hypoxia, suggesting a potential buffer from drought relative to deeper WT forests (Fig. 1). During severe droughts, however, shallow-WT forests may be vulnerable to collapse due to drought-intolerant traits. Addressing the underrepresentation of relatively drought-resilient shallow-WT forests in the Amazon's forest inventories may help resolve the much-debated incongruence in recent studies of the Amazon's drought resilience. Data from forest inventories (biased toward deep-WT forests) indicate that the Amazon's carbon sink is declining, partially because of drought impacts (Phillips et al., 2009; Brienen et al., 2015), whereas basin-wide satellite-based measures of gross primary productivity, which include shallow-WT forests, indicate varied regional responses to droughts (Saleska et al., 2007; Brando et al., 2010). Costa et al.'s (2023) descriptive insights of the intricate balance of geology, topography, hydrology, vegetation, and drought on ecosystem function, as well as their conceptual predictive framework, are useful for developing new empirical research in these understudied ecosystems and improving Earth system models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2026

Observational Data for Next-Generation Climate Model Evaluation: Requirements, Considerations, and Best Practices

Climate model simulations are an important source of information about our planet’s climate system and also enable informed decision-making under different future scenarios. As a new archive of results from the next generation of climate models is anticipated to become available with the Coupled Model Intercomparison Project phase 7 (CMIP7), the need to develop efficient and robust methods to evaluate models is paramount. Observations are an integral part of model evaluation, providing a means to quantify and understand the degree to which climate models can faithfully reproduce Earth system processes. Such analysis is critical for constraining climate projections, identifying areas of focus for model development, and assisting analysts in deciphering the utility of models for specific applications. Observations of Earth system come from a diversity of sources, span different space–time domains, and are produced by different communities, and each dataset features different data structures and formats, metadata standards, and its own unique uncertainties. Uncertainties in an observational dataset may stem from gaps in temporal and spatial coverage, instrumentation errors, or assumptions in retrieval and processing methods. How then does one ensure that observational data are ready for use and utilized in the most appropriate way for robust, rapid, and routine climate model evaluation? The CMIP7 Model Benchmarking Task Team with input from the broader climate modeling, model evaluation, and observational data communities present a vision and considerations for best practices toward the optimal and appropriate use of observational data to support next-generation climate model evaluation.

Climate models↗
DOE OSTI2022

Variable wildfire impacts on the seasonal water temperatures of western US streams: A retrospective study

Recent increases in the burn area and severity of wildfires in the western US have raised concerns about the impact on stream water temperature–a key determinant of cold-water fish habitats. However, the effect on seasonal water temperatures of concern, including winter and summer, are not fully understood. In this study, we assessed the impact of wildfire burns at Boulder Creek (Oregon), Elk Creek (Oregon), and Gibbon River (Wyoming) watersheds on the downstream winter and summer water temperatures for the first three post-fire years. To obtain results independent of the choice of the analytical method, we evaluated the consequence of each burn using three different statistical approaches that utilize local water temperature data. Our results from the three approaches indicated that the response of water temperatures to wildfire burns varied across seasons and sites. Wildfire burns were associated with a median increase of up to 0.56°C (Standard Error; S.E. < 0.23°C) in the summer mean water temperatures (MWT) and 62 degree-day Celsius (DDC; S.E. < 20.7 DDC) in the summer accumulated degree days (ADD) for the three subsequent years across studied stream sites. Interestingly, these burns also corresponded to a median decrease of up to 0.49°C (S.E. < 0.45°C) in the winter MWT and 39 DDC (S.E. < 40.5 DDC) in the winter ADD for the same period across sites. Wildfire effects on the downstream water temperatures diminished with increasing site distance from the burn perimeter. Our analyses demonstrated that analytical methods that utilize local watershed data could be applied to evaluate fire effects on downstream water temperatures.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Novel Magnesium Pretreatment Coatings Offering Excellent Corrosion Protection

There is a growing interest in using magnesium alloys for automotive, industrial, and aerospace applications due to their low density, high strength-to-weight ratio, good machinability, and low cost. These alloys have the potential to lighten cars and aircraft, reducing fuel consumption and emissions. A major drawback to the use of magnesium alloys is their highly reactive nature, which can result in corrosion; as a result, most existing pretreatment systems for these alloys require the use of chromium, which is increasingly coming under regulatory pressures, as a corrosion inhibitor. Through a project funded by the Department of Energy and administered by the U.S. Automotive Materials Partnership (USAMP), PPG developed chromium-free metal pretreatment solutions for ZEK-100 and AZ21 magnesium alloys. In a major technical advance, several chromium-free cleaning and pretreatment processes were developed that offer excellent corrosion protection when compared to a typical automotive pretreatment systems. To expand the use cases for these systems beyond single-alloy substrates to broader multi-metal assemblies, these pretreatment solutions were also optimized for use in multi-metal applications: compatibility with magnesium and other metals was demonstrated without the need for additional process steps.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Ice nucleating particle concentrations in the Arctic during MOSAiC 2020

This data set contains ice nucleating particle (INP) measurements, using Colorado State University’s (CSU) Ice Spectrometer (IS) of samples collected during the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC) field campaign. Samples were collected from five different media: aerosol filters, meltwater, ice cores, snow pits, and bulk sea water (BSW).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Novel Magnesium Pretreatment Coatings Offering Excellent Corrosion Protection

There is a growing interest in using magnesium alloys for automotive, industrial, and aerospace applications due to their low density, high strength-to-weight ratio, good machinability, and low cost. These alloys have the potential to lighten cars and aircraft, reducing fuel consumption and emissions. A major drawback to the use of magnesium alloys is their highly reactive nature, which can result in corrosion; as a result, most existing pretreatment systems for these alloys require the use of chromium, which is increasingly coming under regulatory pressures, as a corrosion inhibitor. Through a project funded by the Department of Energy and administered by the U.S. Automotive Materials Partnership (USAMP), PPG developed chromium-free metal pretreatment solutions for ZEK-100 and AZ21 magnesium alloys. In a major technical advance, several chromium-free cleaning and pretreatment processes were developed that offer excellent corrosion protection when compared to a typical automotive pretreatment systems. To expand the use cases for these systems beyond single-alloy substrates to broader multi-metal assemblies, these pretreatment solutions were also optimized for use in multi-metal applications: compatibility with magnesium and other metals was demonstrated without the need for additional process steps.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Strategies for Post-Synthetic Functionalization of Mesoporous Carbon Nanomaterial Surfaces

Most widely used carbon-based materials feature a bulk framework tolerant of extreme pH and temperature conditions while maintaining physical robustness, electrical conductivity, and biological relevance due to its inherent nontoxicity. At the nanoscale, the study of porous carbons has been of interest because of the enhanced accessible surface area relative to nonporous analogues. The evolution of synthetic techniques has developed structural allotropes with tunable internal pore diameter averages spanning from sub-nanometer to micron-plus sizes. Within the mesopore range, surface chemical phenomena abide by a kinetic-limited ruleset (ignoring the effects of bulk diffusion, as they do not pertain to this size regime). Further, mesopores provide sufficient volume and surface area for hosting guest species, including metal nanoparticles (MNP), polymers, small organics, and enzymes. These two unique features of mesoporous carbon (MC) drive interest in surface modifications to create new physicochemical functionality to these systems and their hosted species. As a result, the evolution of surface modification techniques has been scattered to disparate niches across the literature. This review compiles the array of elegant methods by which these carbon systems are topically engineered.

functionalization↗
DOE OSTI2023

Comparing the cooling effectiveness of operationalisable urban surface combination scenarios for summer heat mitigation

Extreme summer heat in cities exacerbates the vulnerability of urban communities to heatwaves. Vegetative and reflective urban surfaces can help reduce urban heat. This study investigated the impacts of urban trees, green roofs and cool roofs on heat mitigation during average and extreme summer conditions in temperate oceanic Melbourne, Australia. We simulated the city climate using ‘The Air Pollution Model’ (TAPM) at a 1 km spatial resolution over 10 years, which according to our review of the literature, was the most prolonged period for simulation in Melbourne. During a widespread heatwave event, some of the tested scenarios with combined surface parameters could reduce the extreme values of the energy budget components- sensible heat, latent heat, and storage heat fluxes up to seasonal averages compared to the existing situation for Melbourne (control). The scenario with the highest (reasonable maximum) ground-level vegetation, green roofs, and cool roofs could reduce air temperatures up to 2.4 °C. The simulations suggest that a combined strategy with vegetative and high-albedo surfaces will deliver higher effectiveness with maximum cooling benefits and cost-effectiveness than individual strategies in cities. These results suggest the importance of collaborative strategic planning of urban surfaces to make cities healthier, sustainable, and liveable.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Nucleation and growth of molybdenum disulfide grown by thermal atomic layer deposition on metal oxides

To enable greater control over thermal atomic layer deposition (ALD) of molybdenum disulfide (MoS 2 ), here we report studies of the reactions of molybdenum hexafluoride (MoF 6 ) and hydrogen sulfide (H 2 S) with metal oxide substrates from nucleation to few-layer films. In situ quartz crystal microbalance experiments performed at 150, 200, and 250 °C revealed temperature-dependent nucleation behavior of the MoF 6 precursor, which is attributed to variations in surface hydroxyl concentration with temperature. In situ Fourier transform infrared spectroscopy coupled with ex situ x-ray photoelectron spectroscopy (XPS) indicated the presence of molybdenum oxide and molybdenum oxyfluoride species during nucleation. Density functional theory calculations additionally support the formation of these species as well as predicted metal oxide to fluoride conversion. Residual gas analysis revealed reaction by-products, and the combined experimental and computational results provided insights into proposed nucleation surface reactions. With additional ALD cycles, Fourier transform infrared spectroscopy indicated steady film growth after ~13 cycles at 200 °C. XPS revealed that higher deposition temperatures resulted in a higher fraction of MoS 2 within the films. Deposition temperature was found to play an important role in film morphology with amorphous films obtained at 200 °C and below, while layered films with vertical platelets were observed at 250 °C. Finally, these results provide an improved understanding of MoS 2 nucleation, which can guide surface preparation for the deposition of few-layer films and advance MoS 2 toward integration into device manufacturing.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Effect of the Binder during Ultra-Precision Polishing of Tungsten Carbide Using a Semirigid Bonnet Tool

Tungsten carbide (WC) has the characteristics of high hardness, high strength, corrosion resistance, wear resistance and excellent fracture toughness. Accordingly, it has been commonly used as the material for cutting tools and molds in glass-forming techniques. To obtain ultra-smooth surfaces, fine polishing of WC is indispensable. However, the efficiency of WC polishing is low using the existing polishing methods, and the mechanism behind the polishing process requires further investigation. Specifically, the effect of the binder in WC polishing is not clear since there are different kinds of WC with various weight percentages of the binder. In this paper, we present the findings of a study on the polishing performance of two kinds of WC material, with and without the binder, using a semi-rigid (SR) bonnet polishing tool. A series of experiments were performed on a 6-DOF robotic polishing instrument to investigate the material-removal characteristics, surface integrity and sub-surface damage after polishing. The results demonstrate that the SR bonnet polishing tool successfully reduced the surface roughness of WC with and without the binder to the nanometric level, though the lowest surface roughness was obtained on binder-less WC. No obvious sub-surface damage was observed under SEM inspection, while the processing efficiency was greatly improved owing to the high material removal rate of the tool. Based on our analysis of key polishing parameters and corresponding surface integrities, the effect of the binder on the polishing performance is explained, which offers excellent guidance for WC polishing.

ultra-precision machining↗
DOE OSTI2020

Build orientation, surface roughness, and scan path influence on the microstructure, mechanical properties, and flexural fatigue behavior of Ti–6Al–4V fabricated by electron beam melting

In the present work, microstructure and fatigue behavior of Electron Beam Melted (EBM) Ti–6Al–4V have been systematically studied. Fatigue behavior was investigated using four-point bend tests of rectangular bars with internal features fabricated with different build orientations, scan paths, and surface conditions. Microstructural variations were evaluated by optical microscopy and Vickers micro-hardness indentations, both of which were found to be consistent throughout the build and within ranges reported in the literature. The fatigue performance of the EBM material was comparable to wrought Ti–6Al–4V when the loading direction was parallel to the build direction, but severely limited when loaded perpendicular to the build direction or if surface roughness defects were present. Scanning electron microscopy and interrupted fatigue tests were utilized to characterize fracture surfaces, failure mechanisms, crack initiation, and propagation behavior. Crack initiation sites in the vertically-oriented specimens were identified to be melt-related defects formed by insufficient melting.

36 MATERIALS SCIENCE↗
DOE OSTI2021

Deep Learning Estimation of Daily Ground–Level NO 2 Concentrations from Remote Sensing Data

The limited number of nitrogen dioxide (NO 2 ) surface measurements calls for the development of highly accurate approaches to estimating surface NO 2 concentrations. In this study, we leverage a new satellite instrument, the TROPOspheric Monitoring Instrument (TROPOMI), along with other predictor variables, to estimate daily surface NO 2 concentrations over Texas in 2019. We use the deep convolutional neural network (Deep-CNN), an advanced deep learning algorithm, to obtain estimates and achieve a correlation coefficient (R) of 0.91, an index of agreement (IOA) of 0.95, and a mean absolute bias (MAB) of 1.75 ppb in surface NO 2 estimation. Additionally, we leverage a novel approach, SHapley Additive exPlanations (SHAP), to describe how Deep-CNN understands each predictor variable. The SHAP results show that the Deep-CNN model has an advanced understanding of the dataset, revealing that TROPOMI closely captures levels of NO 2 . In addition, we show the superiority of our Deep-CNN model at estimating surface NO 2 over other well-known machine learning and regression models in the field, including the support vector machines (SVM), random forest (RF), and multiple linear regression (MLR). Although SVM and RF show strong capabilities at estimating surface NO 2 concentrations, their accuracy is inferior to that of the Deep-CNN model, ranking second and third in model accuracy in this study. The MLR, however, shows a poor ability at NO 2 estimation and ranks last among all models. Furthermore, testing the impact of sample size on model performance, we also show that, compared to other models, Deep-CNN needs more samples to trigger its strength at surface NO 2 estimation.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Identification of potential CO 2 leakage pathways and mechanisms in oil reservoirs using fault tree analysis

Geological storage of CO 2 technologies has become an important and effective way to reduce the greenhouse gas emissions, especially when it is combined with CO 2 enhanced oil recovery (EOR), which can not only trap CO 2 but also enhance oil recovery. However, the risk of CO 2 leakage has always been a prominent issue. In this paper, the mechanisms and pathways of CO 2 leakage during geological storage in oil reservoirs were analyzed using fault tree analysis (FTR). Besides, monitoring technologies were discussed and deployed in a CO 2 EOR demonstration project. The analysis results showed that the sealing failures of oil producer and CO2 injector wells, like well cement failure and casing failure, are the main reasons for the CO 2 leakage, which has been observed in the oil field monitoring project. The monitoring results indicated that there is no large-scale CO 2 leakage, while relatively high and abnormal CO 2 concentration in soil gas near some wellbores are observed, which indicates there is some leakage of CO 2 through incomplete cement ring and well casing string. FTR results provide guidelines for monitoring and preventing of CO 2 leakage during geological storage in oil reservoirs. Finally, the near-surface monitoring methods, especially the soil gas monitoring technologies, can effectively detect the leakage of CO 2 , and are a proper method for CO 2 leakage monitoring.

58 GEOSCIENCES↗
DOE OSTI2020

Spectroscopic Probe Molecule Selection Using Quantum Theory, First-Principles Calculations, and Machine Learning

Probe molecule vibrational spectra have a long history of being used to characterize materials including metals, oxides, metal-organic frameworks, and even human proteins. Furthermore, recent advances in machine learning have enabled computationally generated spectra to aid in detailed characterization of complex surfaces with probe molecules. Despite widespread use of probe molecules, the science of probe molecule selection is underdeveloped. Here, we develop physical concepts, including orbital interaction energy and the energy overlap integral, to explain and predict the ability of probe molecules to discriminate structural descriptors. We resolve the crystal orbital overlap population (COOP) to specific molecular orbitals and quantify their bonding character, which directly influences vibrational frequencies. Using only a single adsorbate calculation from density function theory (DFT), we compute the interaction energy of individual adsorbate molecular orbitals with adsorption site atomic orbitals across many different sites. Combining the molecular orbital resolved COOP and changes in orbital interaction energy enables probe molecule selection for improved discrimination of various sites. We demonstrate these concepts by comparing the predicted effectiveness of carbon monoxide (CO), nitric oxide (NO), and ethylene (C 2 H 4 ) to probe Pt adsorption sites. Finally, using a previously developed machine learning framework, we show that models trained on hundreds of thousand C 2 H 4 spectra, computed from DFT, which regress surface binding-type and generalized coordination number (GCN), outperform those trained using CO and NO spectra. Lastly, a python package, pDOS_overlap, for implementing the electron density based analysis on any combination of adsorbates and materials, is also made available.

77 NANOSCIENCE AND NANOTECHNOLOGY↗