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At least 649 records · Page 36
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Impacts of climate change on future hurricane induced rainfall and flooding in a coastal watershed: A case study on Hurricane Harvey

The warming climate is likely to increase hurricane-associated extreme rainfall and lead to sea-level rise (SLR). Thus, how the floods induced by intense hurricanes respond to these potential changes is of great concern. This study investigates the future warmer climate impacts on hurricane-induced extreme rainfall, and—more importantly—the subsequent compound flooding at the watershed scale (from an event-based analysis perspective). To this goal, a modeling framework is designed based on the Distributed Hydrology Soil Vegetation Model (DHSVM), the Two-Dimensional fvand the Regional Community Earth System Model (R-CESM). The framework was applied to Hurricane Harvey (2017) at the Clear Creek watershed (a coastal watershed in the southern Houston) as a case study. The results show that the projected maximum rainfall totals over the watershed would be exacerbated by 17.7 % and 49.7 % in the 2050s and 2090s (respectively) under Representative Concentration Pathway 8.5 (RCP 8.5). This means a 16.1 % increase in Harvey rainfall over the watershed per degree Celsius increase in Mean Surface Temperature over the Gulf of Mexico region (18°~31° N, 77°~98° W). Meanwhile, the increases in maximum inundation extent would be 11.0 % (2050s) and 19.5 % (2090s). Furthermore, considerable increases in maximum inundation depth and duration in regions along the middle and downstream of Clear Creek (and also those around Clear Lake) are expected. The projected SLR will have little effect on the maximum inundation depth and extent if storm surge changes are not taken into account; meanwhile, it will influence the inundation duration at downstream locations. In conclusion, this modeling framework can be also applied at other coastal watersheds to evaluate the projected climate change impacts on the compound flooding induced by extreme climate events.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Screening two-dimensional materials with topological flat bands

The topological flat band (TFB) has been proposed theoretically in various lattice models, to exhibit a rich spectrum of intriguing physical behaviors. However, the experimental demonstration of flat band (FB) properties has been severely hindered by the lack of materials realization. In this study, by screening materials from a first-principles materials database, we identify a group of two-dimensional materials with TFBs near the Fermi level, covering some simple line-graph and generalized line-graph FB lattice models. These include the kagome sublattice of O in Ti O 2 yielding a spin-unpolarized TFB, and that of V in ferromagnetic V 3 F 8 yielding a spin-polarized TFB. The monolayer Nb 3 Te Cl 7 and its counterparts from element substitution are found to be breathing-kagome-lattice crystals. The family of monolayer II I 2 V I 3 compounds exhibit a TFB representing the coloring-triangle lattice model. Re F 3 , Mn F 3 , and Mn Br 3 are all predicted to be diatomic-kagome-lattice crystals, with TFB transitions induced by atomic substitution. Finally, Hg F 2 , Cd F 2 , and Zn F 2 are discovered to host dual TFBs in the diamond-octagon lattice. Our findings pave the way to further experimental exploration of eluding FB materials and properties.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Random forest models accurately classify synthetic opioids using high-dimensionality mass spectrometry datasets

Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing number of threat chemicals whose exact structures are unknown. With the use of Machine Learning (ML) tools, we can guide the development of analytical methods for broad-spectrum detection of unbounded threat chemical families in complex mixtures. Toward this goal, we used nominal mass and high-resolution mass spectrometry data for hundreds of synthetic opioids and non-opioid compounds. We tested two ML techniques, logistic regression and random forest, to develop models towards a practical, implementable method for opioid detection. We found that of these tested ML methods, random forest models resulted in the highest validation accuracy (95+%) for both nominal mass and high-resolution classification of opioids versus non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds—five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats.

Chemistry↗
DOE OSTI2025

Random forest models accurately classify synthetic opioids using high-dimensionality mass spectrometry datasets

Detection of novel threat agents presents several challenges, a principle one being the development of untargeted methods to screen an increasing number of threat chemicals whose exact structures are unknown. With the use of Machine Learning (ML) tools, we can guide the development of analytical methods for broad-spectrum detection of unbounded threat chemical families in complex mixtures. Toward this goal, we used nominal mass and high-resolution mass spectrometry data for hundreds of synthetic opioids and non-opioid compounds. We tested two ML techniques, logistic regression and random forest, to develop models towards a practical, implementable method for opioid detection. We found that of these tested ML methods, random forest models resulted in the highest validation accuracy (95+%) for both nominal mass and high-resolution classification of opioids versus non-opioids, with low false positive and false negative rates. The RF models were then used to successfully predict the classification of 10 compounds—five opioids and five non-opioids not part of the training and validation analysis. This application of ML is a critical step towards the development of field-deployable nominal mass spectrometers with ML-driven analyses for classification of emergent threats.

Arasteh, Kourosh [Lawrence Livermore National Labo↗
DOE OSTI2023

Improving Protein–Ligand Interaction Modeling with cryo-EM Data, Templates, and Deep Learning in 2021 Ligand Model Challenge

Elucidating protein–ligand interaction is crucial for studying the function of proteins and compounds in an organism and critical for drug discovery and design. The problem of protein–ligand interaction is traditionally tackled by molecular docking and simulation, which is based on physical forces and statistical potentials and cannot effectively leverage cryo-EM data and existing protein structural information in the protein–ligand modeling process. In this work, we developed a deep learning bioinformatics pipeline (DeepProLigand) to predict protein–ligand interactions from cryo-EM density maps of proteins and ligands. DeepProLigand first uses a deep learning method to predict the structure of proteins from cryo-EM maps, which is averaged with a reference (template) structure of the proteins to produce a combined structure to add ligands. The ligands are then identified and added into the structure to generate a protein–ligand complex structure, which is further refined. The method based on the deep learning prediction and template-based modeling was blindly tested in the 2021 EMDataResource Ligand Challenge and was ranked first in fitting ligands to cryo-EM density maps. These results demonstrate that the deep learning bioinformatics approach is a promising direction for modeling protein–ligand interactions on cryo-EM data using prior structural information.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2021

Effective radiative forcing from emissions of reactive gases and aerosols - a multi-model comparison

This paper quantifies the pre-industrial (1850) to present-day (2014) effective radiative forcing (ERF) of anthropogenic emissions of NOX, volatile organic compounds (VOCs; including CO), SO2, NH3, black carbon, organic carbon, and concentrations of methane, N2O and ozone-depleting halocarbons, using CMIP6 models. Concentration and emission changes of reactive species can cause multiple changes in the composition of radiatively active species: tropospheric ozone, stratospheric ozone, stratospheric water vapour, secondary inorganic and organic aerosol, and methane. Where possible we break down the ERFs from each emitted species into the contributions from the composition changes. The ERFs are calculated for each of the models that participated in the AerChemMIP experiments as part of the CMIP6 project, where the relevant model output was available. The 1850 to 2014 multi-model mean ERFs (± standard deviations) are −1.03 ± 0.37 W/sq.m for SO2 emissions, −0.25 ± 0.09 W/sq.m for organic carbon (OC), 0.15 ± 0.17 W/sq.m for black carbon (BC) and −0.07 ± 0.01 W/sq.m for NH3. For the combined aerosols (in the piClim-aer experiment) it is −1.01 ± 0.25 W/sq.m. The multi-model means for the reactive well-mixed greenhouse gases (including any effects on ozone and aerosol chemistry) are 0.67 ± 0.17 W/sq.m for methane (CH4), 0.26 ± 0.07 W/sq.m for nitrous oxide (N2O) and 0.12 ± 0.2 W/sq.m for ozone-depleting halocarbons (HC). Emissions of the ozone precursors nitrogen oxides (NOx), volatile organic compounds and both together (O3) lead to ERFs of 0.14 ± 0.13, 0.09 ± 0.14 and 0.20 ± 0.07 W/sq.m respectively. The differences in ERFs calculated for the different models reflect differences in the complexity of their aerosol and chemistry schemes, especially in the case of methane where tropospheric chemistry captures increased forcing from ozone production.

radiative forcing↗
DOE OSTI2024

Modeling prebiotic chemistries with quantum accuracy at classical costs

Molecular Dynamics (MD) simulations using classical force-fields are commonly employed in numerous scientific investigations. However, many natural processes involve bond breaking and quantum forces. This complexity is compounded by the presence of multiple competing length and timescales. For example, accurately modeling the thermodynamics and dynamics of a chemical reaction requires accounting for the concerted movements of numerous solvent molecules and ions with their own fast or slow timescales. While widely used static Density Functional Theory (DFT) calculations at 0 temperature can be beneficial for such investigations, they do not account for dynamics, and lack precision in describing the molecular environments. They particularly fail at correct, rigorous treatments of finite-temperature fluctuations, and thus generalization to experimentally relevant conditions. In PNAS Benayad et al develop a scalable, generalizable approach for designing Neural Network Potentials (NNPs) that can handle chemical reactivity in solvated systems with quantum accuracy at classical costs. Specifically, they study phosphoester bond formation and rupture, which is fundamentally relevant to the Phosphorus-Oxygen bond formation central to life, and especially for the RNA world hypothesis. The framework developed here has the potential to generalize to different chemical reactions of energy and biological relevance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

A Three-Dimensional, Analytical Wind Turbine Wake Model: Flow Acceleration, Empirical Correlations, and Continuity

A new, three-dimensional, analytical, steady-state wake model is presented that includes local flow acceleration near the rotor, improving the wake description compared to existing models. Wake structures such as the momentum deficit and regions of accelerated flow are concisely described with compound and normal Gaussian functions. Large-eddy simulations (LES) are used as training data to develop the model using two in-line turbines under various inflow conditions parameterized by hub-height wind speed and turbulence intensity. Mass conservation is considered by fixing two components of the wake velocity model and optimizing the third to best satisfy continuity; after which, the model performs comparably if not better than existing work with regards to both relative error and mass consistency. The final model demonstrates a high degree of flexibility, making use of empirical correlations to scale across different inflow conditions. The inclusion of these effects is capable of revealing unused opportunities for enhanced power generation by aligning wake trajectories with these regions of accelerated flow.

modeling↗
NASA NTRS2003

Thermodynamic Modeling of the YO(l.5)-ZrO2 System

The YO1.5-ZrO2 system consists of five solid solutions, one liquid solution, and one intermediate compound. A thermodynamic description of this system is developed, which allows calculation of the phase diagram and thermodynamic properties. Two different solution models are used-a neutral species model with YO1.5 and ZrO2 as the components and a charged species model with Y(+3), Zr(+4), O(-2), and vacancies as components. For each model, regular and sub-regular solution parameters are derived fiom selected equilibrium phase and thermodynamic data.

Jacobson, Nathan S.↗
DOE OSTI2023

2.3.2.100 - Biological Lignin Valorization (BLV)

The Biological Lignin Valorization (BLV) project develops microbial strains and associated bioprocesses to convert lignin-derived aromatic compounds into value-added bioproducts. Our main objective in the BLV project is to achieve industrially relevant bioproduction metrics that can directly contribute to the economic viability and improved sustainability of the integrated lignocellulosic biorefinery, in collaboration with complementary BETO-funded lignin valorization projects. Specifically, the BLV project works closely with the BETO-funded Lignin Utilization project, which provides bio-available aromatic compounds from chemo-catalytic lignin depolymerization. We use the robust soil bacterium, Pseudomonas putida, as our primary microbial host for the conversion of lignin-derived compounds to bioproducts. To date, we have focused on atom-efficient bioproducts that can be used as either direct replacement chemicals or performance-advantaged bioproducts, including cis,cis-muconic acid, beta-ketoadipic acid, and 2-pyrone-4,6-dicarboxylic acid. From model aromatic substrates, we have achieved titers of each of these compounds approaching 40 g/L and productivity values ranging from 0.5 to over 1 g/L/hr, all at 90% molar yield or higher. From real lignin streams, we have thus far achieved 24 g/L, 0.66 g/L/hr, and theoretical yield of beta-ketoadipic acid. A major pursuit now is to reach industrially relevant performance metrics on an expanded slate of lignin-derived streams.

BIOMASS FUELS↗
NASA NTRS1981

Stratospheric hydroxyl radical concentrations - New limitations suggested by observations of gaseous and particulate sulfur

A one-dimensional aerosol model is employed in investigating the sensitivity of the stratospheric distributions of gaseous sulfur compounds and sulfate aerosol particles to changes in OH and CS2 concentrations, in eddy diffusion coefficients, and in important chemical rate constants. By comparing model predictions with recent observational data for SO2, OCS, and particulates, it is found that, with regard to atmospheric sulfur, CS2 is only a secondary source of sulfur for the stratosphere relative to OCS and that background tropospheric CS2 concentrations by volume are likely to be less than 70 parts per trillion. It is also established that under stratospheric conditions the rate coefficients for the reactions of OH with OCS and CS2 may be substantially smaller than the room temperature laboratory values of Kurylo (1978).

Turco, R. P.↗
DOE OSTI2023

Potential bioavailability of representative pyrogenic organic matter compounds in comparison to natural dissolved organic matter pools

Abstract. Pyrogenic organic matter (PyOM) from wildfires impacts river corridors globally and is widely regarded as resistant to biological degradation. Though recent work suggests PyOM may be more bioavailable than historically perceived, estimating bioavailability across its chemical spectrum remains elusive. To address this knowledge gap, we assessed potential bioavailability of representative PyOM compounds relative to ubiquitous dissolved organic matter (DOM) with a substrate-explicit model. The range of potential bioavailability of PyOM was greater than natural DOM; however, the predicted thermodynamics, metabolic rates, and carbon use efficiencies (CUEs) overlapped significantly between all OM pools. Compound type (e.g., natural versus PyOM) had approximately 6-fold less impact on predicted respiration rates than simulated carbon and oxygen limitations. Within PyOM, the metabolism of specific chemistries differed strongly between unlimited and oxygen-limited conditions – degradations of anhydrosugars, phenols, and polycyclic aromatic hydrocarbons (PAHs) were more favorable under oxygen limitation than other molecules. Notably, amino sugar-like, protein-like, and lignin-like PyOM had lower carbon use efficiencies relative to natural DOM of the same classes, indicating potential impacts in process-based model representations. Overall, our work illustrates how similar PyOM bioavailability may be to that of natural DOM in the river corridor, furthering our understanding of how PyOM may influence riverine biogeochemical cycling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

High-Pressure Electrides: A Quantum Chemical Perspective

It has long been assumed that all matter will adopt simple close-packed lattices and become metallic under pressure, in accordance with the Thomas–Fermi–Dirac (TFD) model. However, this model struggles to explain pressure-driven complex structural transitions that have been observed in elements, including sodium, challenging our conventional understanding of compressed matter. Moreover, in stark contrast to the TFD model, first-principles calculations suggest that various elements and compounds become electrides under pressure. Electrides, characterized by concentrations of charge density at interstitial regions, can be thought of as ionic compounds where electrons behave as the anions. Though ambient-pressure molecular electrides have been extensively studied via experiments and computations, high-pressure electrides (HPEs) are not well-understood. The identification and characterization of HPEs have been, to date, based purely on theory, including topological analysis of the electron density and the electron localization function. Here, we review these theoretical analysis tools and suggest guidelines that can be used to classify systems as electrides. Moreover, we describe models used to rationalize the electronic structure of HPEs, drawing parallels with ambient-pressure molecular systems, and encourage the development of experimental techniques that provide evidence for the theoretically calculated charge localization.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2008

Tide Corrections for Coastal Altimetry: Status and Prospects

Knowledge of global oceanic tides has markedly advanced over the last two decades, in no small part because of the near-global measurements provided by satellite altimeters, and especially the long and precise Topex/Poseidon time series e.g. [2]. Satellite altimetry in turn places very severe demands on the accuracy of tidal models. The reason is clear: tides are by far the largest contributor to the variance of sea-surface elevation, so any study of non-tidal ocean signals requires removal of this dominant tidal component. Efforts toward improving models for altimetric tide corrections have understandably focused on deep-water, open-ocean regions. These efforts have produced models thought to be generally accurate to about 2 cm rms. Corresponding tide predictions in shelf and near-coastal regions, however, are far less accurate. This paper discusses the status of our current abilities to provide near-global tidal predictions in shelf and near-coastal waters, highlights some of the difficulties that must be overcome, and attempts to divine a path toward some degree of progress. There are, of course, many groups worldwide who model tides over fairly localized shallow-water regions, and such work is extremely valuable for any altimeter study limited to those regions, but this paper considers the more global models necessary for the general user. There have indeed been efforts to patch local and global models together, but such work is difficult to maintain over many updates and can often encounter problems of proprietary or political nature. Such a path, however, might yet prove the most fruitful, and there are now new plans afoot to try again. As is well known, tides in shallow waters tend to be large, possibly nonlinear, and high wavenumber. The short spatial scales mean that current mapping capabilities with (multiple) nadir-oriented altimeters often yield inadequate coverage. This necessitates added reliance on numerical hydrodynamic models and data assimilation, which in turn necessitates very accurate bathymetry with high spatial resolution. Nonlinearity means that many additional compound tides and overtides must be accounted for in our predictions, which increases the degree of modeling effort and increases the amounts of data required to disentangle closely aliased tides.

Ray, Richard D.↗
NASA NTRS2001

Projected Impact of Climate Change on the Energy Budget of the Arctic Ocean by a Global Climate Model

The annual energy budget of the Arctic Ocean is characterized by a net heat loss at the air-sea interface that is balanced by oceanic heat transport into the Arctic. The energy loss at the air-sea interface is due to the combined effects of radiative, sensible, and latent heat fluxes. The inflow of heat by the ocean can be divided into two components: the transport of water masses of different temperatures between the Arctic and the Atlantic and Pacific Oceans and the export of sea ice, primarily through Fram Strait. Two 150-year simulations (1950-2099) of a global climate model are used to examine how this balance might change if atmospheric greenhouse gases (GHGs) increase. One is a control simulation for the present climate with constant 1950 atmospheric composition, and the other is a transient experiment with observed GHGs from 1950 to 1990 and 0.5% annual compounded increases of CO2 after 1990. For the present climate the model agrees well with observations of radiative fluxes at the top of the atmosphere, atmospheric advective energy transport into the Arctic, and surface air temperature. It also simulates the seasonal cycle and summer increase of cloud cover and the seasonal cycle of sea-ice cover. In addition, the changes in high-latitude surface air temperature and sea-ice cover in the GHG experiment are consistent with observed changes during the last 40 and 20 years, respectively. Relative to the control, the last 50-year period of the GHG experiment indicates that even though the net annual incident solar radiation at the surface decreases by 4.6 W(per square meters) (because of greater cloud cover and increased cloud optical depth), the absorbed solar radiation increases by 2.8 W(per square meters) (because of less sea ice). Increased cloud cover and warmer air also cause increased downward thermal radiation at the surface so that the net radiation into the ocean increases by 5.0 Wm-2. The annual increase in radiation into the ocean, however, is compensated by larger increases in sensible and latent heat fluxes out of the ocean. Although the net energy loss from the ocean surface increases by 0.8 W (per square meters), this is less than the interannual variability, and the increase may not indicate a long-term trend. The seasonal cycle of heat fluxes is significantly enhanced. The downward surface heat flux increases in summer (maximum 2 of 19 W per square meters or 23% in June) while the upward heat flux increases in winter (maximum of 16 W per square meters or 28% in November). The increased downward flux in summer is due to a combination of increases in absorbed solar and thermal radiation and smaller losses of sensible and latent heat. The increased heat loss in winter is due to increased sensible and latent heat fluxes, which in turn are due to reduced sea-ice cover. On the other hand, the seasonal cycle of surface air temperature is damped, as there is a large increase in winter temperature but little change in summer.

Miller, James R.↗
DOE OSTI2022

Sign-free determinant quantum Monte Carlo study of excitonic density orders in a two-orbital Hubbard-Kanamori model

While excitonic instabilities in multiorbital systems recently have come under scrutiny in a variety of transition-metal compounds, understanding emergence of these instabilities from strong electronic interactions has remained a challenge. In this work, we present a sign-problem-free determinant quantum Monte Carlo study of excitonic density orders in a half-filled two-orbital Hubbard-Kanamori model with broken orbital degeneracy, which accounts for the role of Hund's coupling in transition-metal compounds. For strong inverted (negative) Hund's exchange, we find numerical evidence for the emergence of excitonic density order, with competition between anti-ferro-orbital order and Q = (π, π) excitonic density order as a function of orbital splitting and Hund's coupling. While inverted Hund's coupling stabilizes a spin-singlet excitonic density phase for weak orbital splitting, positive Hund's coupling favors a spin-triplet excitonic density phase.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1976

Roller bearing geometry design

A theory of kinematic stabilization of rolling cylinders is extended and applied to the design of cylindrical roller bearings. The kinematic stabilization mechanism puts a reverse skew into the rolling elements by changing the roller taper. Twelve basic bearing modification designs are identified amd modeled. Four have single transverse convex curvature in their rollers while eight have rollers which have compound transverse curvature made up of a central cylindrical band surrounded by symmetric bands with slope and transverse curvature. The bearing designs are modeled for restoring torque per unit axial displacement, contact stress capacity, and contact area including dynamic loading, misalignment sensitivity and roller proportion. Design programs are available which size the single transverse curvature roller designs for a series of roller slopes and load separations and which design the compound roller bearings for a series of slopes and transverse radii of curvature. The compound rollers are proportioned to have equal contact stresses and minimum size. Design examples are also given.

Savage, M.↗
DOE OSTI2026

Coordination of Anle138b to Silver Results in Selective Reduction of a C-terminal truncated Alpha-synuclein Protein and Increased Aggregate Size

Parkinson’s disease (PD) is a prevalent age-related neurodegenerative syndrome, partially thought to be caused by a decrease in alpha-synuclein proteostasis. Anle138b = 5-(1,3-benzodioxol-5-yl)-3-(3-bromophenyl)-1H-pyrazole (HL), is undergoing clinical trials as a promising mitigator of alpha-synuclein aggregation. Because complexation to metals is known to modulate the activity of several drugs, we have prepared and characterized: H2L(ClO4), [CuI(µ-L)]3, and [AgI(µ-L)]3. To better understand the bioviability of these compounds, we monitored their effects in a cell culture model of alpha-synuclein protein aggregation using human alpha-synuclein pre-formed fibrils (PFFs). Using two different anti-alpha-synuclein antibodies, our data suggests that [AgI(µ-L)]3 decreases a C-terminal truncated protein that is approximately 12.4 kDa, as well as increases the size and alters the shape of PFF-induced aggregates. This indicates that [AgI(µ-L)]3 impacts aggregation in a manner different from HL and may serve as a novel tool for studying C-terminal truncation related aggregation chemistry.

Rue, Kelly L.↗