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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 703 records · Page 39
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Reactive Modified Epoxy Resin and Its Miscible Blends Based on Recycled Oligomers from Solvolysis

Chemical depolymerization of fully cured epoxy resin with 20% reactive modifier was successfully performed via a solvent-assisted solvolysis process into low molecular weight recyclable oligomers (RO) at 240 °C in a pressure vessel at 650 psi for 4 h. The thermoset epoxy resin was depolymerized into transparent brown viscous fluid with a higher viscosity than the uncured epoxy resin with approximately 93% yield. Different concentrations of the RO were homogeneously mixed with the pure epoxy resin, and their curing kinetics, viscosity, FTIR, mechanical properties, DMA, and cross-link density were investigated. The curing kinetics of the pure reactive modified epoxy resin (baseline) and its mixtures with RO of different concentrations were investigated under both isothermal and nonisothermal conditions using small amplitude oscillatory shear flow. The elastic and viscous moduli (G′ and G″), complex viscosity (η*), and tan δ values were evaluated at different curing times and temperatures. The G′, G″, and η* increased dramatically, while tan δ decreased strongly by several orders of magnitude at the gel point. The zero-shear viscosity (η 0 ) was determined from the angular frequency dependent on η* based on the Cross model for different blend compositions in the liquid state before curing. The composition dependence of η 0 showed a positive deviation from the linear mixing rule and was well described by the Lecyar model. Here, the apparent activation energy of curing (E a ) was also evaluated according to the Arrhenius equation and was found to be 46 ± 2 kJ/mol regardless of the different contents of RO. For all blends up to 40 wt % RO, only one tan δ peak systematically shifting to lower temperatures with increasing content of RO was observed in the DMA measurements, indicating that the epoxy resin and the RO are miscible with up to 40 wt % RO.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Chemical Composition and Mixing State of Wintertime Aerosol from the European Arctic Site of Ny-Ålesund, Svalbard

The Arctic is rapidly warming, and aerosols play an increasingly important role by scattering and absorbing sunlight and by participating in cloud formation. Their optical and cloud-forming properties depend on the mixing state and chemical composition, but observations of these features remain limited. This study comprehensively characterizes 25,254 individual particles collected at Ny-Ålesund, Svalbard (November −December 2020), using microspectroscopy techniques to investigate their size, morphology, mixing state, and chemical composition. Fresh sea salt aerosols (SSA) were identified as the most abundant (∼85%), of the total observed aerosol population, with potential sources from sea spray and blowing snow. Air masses originating from the Arctic Ocean surrounding Svalbard likely contribute to increased concentrations of sub-micrometer “Fresh SSA” particles. “Aged SSA” particles (7.4%) are enriched in sulfur and nitrogen, compared to “Fresh SSA”. These elevated ratios may result from various atmospheric aging processes including the uptake of sulfuric and nitric acids. Here, our results suggest that aged SSA, with sizes larger than 300 nm, likely underwent chlorine depletion by sulfuric and nitric acids during transport. Additionally, elemental analysis reveals that both fresh and aged SSA can mix with dust particles, regardless of the SSA size (49.9% in sub-micrometer size and 50.1% in super-micrometer size, respectively). Dust particles are efficient ice-nucleating particles (INPs), and SSA is known to act as cloud condensation nuclei (CCN), and therefore, their mixtures may inherit both properties. The non-negligible number (4.4%) of SSA-dust mixtures underscores the importance of these particles as potential sources of CCN and INP in the Arctic atmosphere.

Arctic↗
DOE OSTI2025

Learning to Simulate Aerosol Dynamics with Graph Neural Networks

Aerosol effects on climate, weather, and air quality depend on characteristics of individual particles, which are tremendously diverse and change in time. Particle-resolved models are the only models able to capture this diversity in particle physiochemical properties, and these models are computationally expensive. As a strategy for accelerating particle-resolved microphysics models, we introduce Graph-based Learning of Aerosol Dynamics (GLAD) and use this model to train a surrogate of the particle-resolved model PartMC-MOSAIC. GLAD implements a Graph Network-based Simulator (GNS), a machine learning framework that has been used to simulate particle-based fluid dynamics models. In GLAD, each particle is represented as a node in a graph, and the evolution of the particle population over time is simulated through learned message passing. Here, we demonstrate our GNS approach on a simple aerosol system that includes condensation of sulfuric acid onto particles composed of sulfate, black carbon, organic carbon, and water. A graph with particles as nodes is constructed, and a graph neural network (GNN) is then trained using the model output from PartMC-MOSAIC. The trained GNN can then be used for simulating and predicting aerosol dynamics over time. Results demonstrate the framework's ability to accurately learn chemical dynamics and generalize across different scenarios, achieving efficient training and prediction times. We evaluate the performance across four scenarios, highlighting the framework's robustness and adaptability in modeling aerosol microphysics and chemistry.

aerosol chemistry dynamics↗
DOE OSTI2024

Water, Solute, and Ion Transport in De Novo-Designed Membrane Protein Channels

Biological organisms engineer peptide sequences to fold into membrane pore proteins capable of performing a wide variety of transport functions. Synthetic de novo-designed membrane pores can mimic this approach to achieve a potentially even larger set of functions. Here, in this work, we explore water, solute, and ion transport in three de novo designed β-barrel membrane channels in the 5–10 Å pore size range. We show that these proteins form passive membrane pores with high water transport efficiencies and size rejection characteristics consistent with the pore size encoded in the protein structure. Ion conductance and ion selectivity measurements also show trends consistent with the pore size, with the two larger pores showing weak cation selectivity. MD simulations of water and ion transport and solute size exclusion are consistent with the experimental trends and provide further insights into structure–function correlations in these membrane pores.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Computational Investigation of a CO 2 Conversion Strategy via Diels–Alder Reaction in a Carbon Capture Solvent

Molecular-level insights into reactive separations are crucial for the design of new conversion pathways of carbon dioxide (CO 2 ). This work explores a postulated pathway that directs CO 2 to undergo inverse-electron-demand Diels–Alder reactions to produce heterocycles using the CO 2 chemically fixed on water-lean solvent molecules. Density functional theory calculations are applied to evaluate the lowest unoccupied molecular orbital (LUMO) energies of three types of reactants (1,3-butadiene, 1,3-cyclohexadiene, and 1,2,4,5-tetrazine) with various functional substituents. These calculations also provide a data set (5.8k data) for developing a machine learning model to efficiently predict LUMO energies. A computational screening of LUMO energies for an additional 47k diene and tetrazine candidates is performed, and a list of candidates with lowered LUMO energies by electron-withdrawing substituents is provided. These candidates are further examined by their reaction energy barriers computed from the interatomic potential or density functional theory. Two major energy barriers are identified, one for the proton transfer within the water-lean solvent and the other for the CO 2 transfer from the solvent molecule to the reactant candidate (diene or tetrazine). The functional substituents have a more significant impact on the second barrier but a very slight one on the first barrier. This exploratory work demonstrates a new possibility for guiding experimental efforts toward the chemical conversion of fixated CO 2 to value-added compounds.

Chemical reactions↗
DOE OSTI2026

Substrate-Directed Dimensional and Phase Control of Peptide Assemblies on Two-Dimensional van der Waals Materials

Understanding and controlling biomolecular self-assembly on van der Waals (vdW) materials has the potential to advance hybrid bioelectronic devices by enabling precise tuning of the interface and modulation of the resulting electronic properties of the biomolecule-vdW heterostructure. However, how surface properties of vdW materials direct biomolecule assembly remains poorly understood. To fill this knowledge gap, we investigated the assembly of a peptide known to assemble into two-dimensional (2D) crystalline films on MoS 2 on three representative vdW surfaces: WS 2 , MoS 2 , and highly oriented pyrolytic graphite (HOPG). Using in situ atomic force microscopy (AFM), we find that assembly is substrate-dependent, resulting in multilayers on WS 2 , monolayers on MoS 2 , and multiple coexisting phases on HOPG. WS 2 exhibits a higher negative charge, strong long-range electrostatic interactions, and extensive hydration layering that may promote multilayer stacking. In contrast, MoS 2 has stronger short-range interactions with the peptides but much weaker long-range interactions and hydration structure, which may favor monolayer formation. Molecular dynamics simulations predict a corresponding switch from monolayer to multilayer aggregates of the adsorbed monomers, reflected in their relative mobilities. On hydrophobic HOPG, the peptides bind most strongly and remain as monomers with high surface mobility. The peptide dimers comprising the basic unit of the crystals are more compact on HOPG, which has a smaller lattice constant than WS 2 or MoS 2 , suggesting strain contributes to stabilizing multiple phases. Our results provide mechanistic insights into how surface charge and hydration structure, and the lattice structure of the substrates governs peptide assembly on vdW materials, offering a framework to rationally control the 2D peptide-vdW heterostructures.

Molecular dynamics simulations↗
DOE OSTI2025

The Power of Many: An Ensemble Approach to Spectral Similarity

Quantifying the similarity between two mass spectra─a known reference mass spectrum and an unidentified sample mass spectrum─is at the heart of compound identification workflows in gas chromatography–mass spectrometry (GC-MS). The reference spectrum most like the sample is assigned as its identification (provided some quantitative similarity threshold is met, e.g., 80%) and thus accurately measuring similarity is essential. Significant research has gone toward developing metrics for this purpose, each of which has attempted to improve upon existing methods by incorporating GC-MS-specific information (e.g., peak ratios or retention times) or adopting various statistical and algorithmic frameworks. While this active development has led to a plethora of similarity metrics with demonstrated value across different contexts, the unfortunate consequence has been confusion surrounding which metric should be used as a global standard. No such metric is currently accepted as the standard method because different metrics have demonstrated optimal performance in different contexts. In this work, we propose an ensemble approach to spectral similarity scoring that combines the collective information from across existing similarity metrics to form an improved, globally representative similarity metric as a step toward establishing a global standard method. In conclusion, the resulting ensemble metrics are evaluated on over 88,000 spectra of varying complexity and demonstrate improved abilities to accurately rank the correct reference spectrum as the top-matching candidate for a sample relative to the rankings generated by individual similarity scores.

Carbohydrates↗
DOE OSTI2025

Intensifying Renewable Energy Droughts in the Western U.S. Amid Evolving Infrastructure and Climate

If renewable energy resources continue to become a larger part of the generation mix in the United States (U.S.), so does the potential impact of prolonged periods of low wind and solar generation, known as variable renewable energy (VRE) droughts. In such a future, naturally occurring VRE droughts need to be evaluated for their potential impact on grid reliability. This study is the first of its kind to examine the impacts of compound VRE energy droughts in the Western U.S. across a range of potential future climate and infrastructure scenarios. We find that compound VRE drought severity may increase significantly in the future, primarily due to the dramatic increase in wind and solar generation needed in some future infrastructure scenarios. We find that in our future climate scenario, the variability of energy drought severity increases, which has implications for sizing energy storage necessary for mitigating drought events. We also examine the spatial patterns of compound VRE drought events that effect multiple regions of the grid simultaneously. These co-occurring events have distinct spatial patterns depending on the season. We observed overall fewer connected events in the future with the combined effect of potential climate and infrastructure changes, although in the fall we observe a climate-induced shift toward events which impact more regions simultaneously.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2024

On the Prediction of Aerosol-Cloud Interactions Within a Data-Driven Framework

Aerosol-cloud interactions (ACI) pose the largest uncertainty for climate projection. Among many challenges of understanding ACI, the question of whether ACI can be deterministically predicted has not been explicitly answered. Here we attempt to answer this question by predicting cloud droplet number concentration N c from aerosol number concentration N a and ambient conditions using a data-driven framework. We use aerosol properties, vertical velocity fluctuations, and meteorological states from the ACTIVATE field observations (2020–2022) as predictors to estimate N c . We show that the campaign-wide N c can be successfully predicted using machine learning models despite the strongly nonlinear and multi-scale nature of ACI. However, the observation-trained machine learning model fails to predict N c in individual cases while it successfully predicts N c of randomly selected data points that cover a broad spatiotemporal scale. This suggests that, within a data-driven framework, the N c prediction is uncertain at fine spatiotemporal scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Relationships Between Mesoscale Convective System Properties and Midlevel Dynamic Perturbations

Abstract Past studies implicate dynamic anomalies operating on subsynoptic scales as a possible initiation source of summertime (July–August) mesoscale convective systems (MCSs) in the central United States during northwesterly flow regimes. To improve our understanding of warm season MCSs occurring over a variety of flow regimes, we track midlevel (600 hPa) vorticity perturbations (“MPs”) as 2D objects comprising wavelengths of 500–2,500 km over the central US from May–August of 2004–2021. We perform statistical analysis of relationships between metrics of MP objects (e.g., duration, size, intensity, and origin) and high‐resolution MCS precipitation characteristics (e.g., duration, total rainfall, rain coverage area, and motion) that occur while collocated with or in the absence of MPs to discern predictive capability of background dynamic features on storm precipitation potential. Although the majority of MPs collocated with MCS initiation occur during July–August, a significant number (40%) occur between May and June. Northwesterly flow MPs comprise a relative minority of our events, suggesting that MPs can affect MCSs across a variety of warm season flow regimes. MPs affecting MCSs initiated primarily over the high plains near the central Rockies. Only approximately 20% of tracked MCS initiation events were collocated with MPs, but these storms produced ∼25% greater lifetime rainfall and coverage area, and ∼29% more stratiform rain than non‐MP‐induced MCSs. In general, larger and more vigorous MPs resulted in more hydrologically impactful MCSs. The most directly attributable benefit to MCS initiation was from MP‐enhanced background vertical motion and thermodynamic instability (e.g., increased CAPE).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Evaluation of Marine Boundary Layer Clouds Over the Northeast Pacific During the CSET Campaign in E3SM Version 2

It is still challenging to reproduce marine boundary layer (MBL) clouds well in large-scale models despite their importance to the Earth's radiation budget and hydrological cycle. This study evaluates representation of the MBL and clouds in the Energy Exascale Earth System Model (E3SM) version 2. This study compares the E3SM simulation results with remote sensing and reanalysis data during the Cloud System Evolution in the Trades (CSET) field campaign to better understand the stratocumulus-to-cumulus cloud transition (SCT) over the northeast Pacific. E3SM results are extracted along the CSET Lagrangian trajectories. The comparison shows that the E3SM simulation applying horizontal wind nudging performs well in reproducing thermodynamic variables of the MBL and evolution trends of cloud variables along the trajectories. However, substantial overestimations of aerosol and cloud drop number (N d ) are observed, which is explained as an issue with version 2 of the model. Cloud fraction (CF) does decrease from the Californian coast to Hawaii in the E3SM simulation, but most CF values indicate either an overcast or almost clear sky, which differs from satellite and reanalysis data. The effect of N d overestimation on CF evolution is assessed via prescribed-N d simulations. Those simulations with N d modifications show negligible CF changes. A comparison of estimated inversion strength (EIS) also shows that the simulated EIS values are similar to those of reanalysis data. Our study suggests that cloud macrophysics and boundary layer processes are more important in improving the simulation to capture the SCT than refining the model's thermodynamics or cloud microphysics.

Choi, Kyoung Ock [Univ. of Washington, Seattle, WA↗
DOE OSTI2025

Ensemble‐Based Spatially Distributed CLM5 Hydrological Parameter Estimation for the Continental United States

Abstract One of the major challenges in large‐domain hydrological modeling efforts lies in the estimation of spatially distributed hydrological parameters while simultaneously accounting for their associated uncertainties. Addressing this challenge is particularly difficult in ungauged locations. With growing societal demands for large‐scale streamflow projections to inform water resource management and long‐term planning, evaluating and constraining hydrological parameter uncertainty is increasingly vital. This study introduces a hybrid regionalization approach to enhance hydrological predictions of the Community Land Model version 5 (CLM5) across the Continental United States (CONUS), with a total of 50,629 1/8° grid cells. This hybrid method combines the strengths of two existing techniques: parameter regionalization and streamflow signature regionalization. It identifies ensemble behavioral parameters for each 1/8° grid cell across the CONUS domain, tailored to three distinct streamflow signatures focused on low flows, high flows, and annual water balance. Evaluating this hybrid method for 464 CAMELS (Catchment Attributes and Meteorology for Large‐sample Studies) basins demonstrates a significant improvement in CLM5 hydrological predictions, even in challenging arid regions. In CONUS applications, the derived spatially distributed parameter sets capture both spatial continuity and variation of parameters, highlighting their heterogeneous nature within specific regions. Overall, this hybrid regionalization approach offers a promising solution to the complex task of improving hydrological modeling over large domains for important hydrological applications.

CLM5↗
DOE OSTI2025

The Impacts of Rotational Mixing on the Precipitation Simulated by a Convection Permitting Model

With increased availability of computational resources, regional and global scale convection-permitting model (CPM, Δx ~ 1–10 km) simulations are becoming more common. CPMs have improved accuracy in their representation of deep convection and mesoscale convective systems (MCSs) compared to coarser resolution models. However, CPMs still exhibit convective cloud and precipitation biases relative to observations, notably a lesser frequency of light precipitation rates and greater frequency of heavy precipitation rates. In this work we hypothesize that these CPM biases are related to under-resolved mixing between convective updrafts and their surrounding environment. To test this hypothesis, we introduce a parameterization to the Weather Research and Forecasting model (WRF) that adds a small angular rotation of the grid-scale flow about the axis perpendicular to the plane of convective drafts. This rotated flow is then allowed to alter advection of moisture and hydrometeors. The effects of such mixing on precipitation characteristics are evaluated in month-long 4-km grid spacing simulations over the Amazon. The enhanced mixing transports moisture and condensate from convective cores to other areas including downdrafts. This increases the frequency of low-precipitable water and light precipitation. It also decreases the frequency of intense precipitation from isolated deep convection and MCSs, increases cloud top temperatures, reduces radar echo-top heights, and increases overall precipitation by altering the relationship of precipitation with precipitable water, in better agreement with observations. The results suggest when optimized using multiple observations, such an approach may provide a path toward more accurate representation of convection and precipitation statistics in convection-permitting simulations.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2025

Understanding the Recent Increase in Landfalling Tropical Cyclones Over Florida's Gulf Coast

Unlike Florida's Atlantic Coast, the Gulf Coast of Florida has seen heightened tropical cyclone (TC) activity in recent decades with several destructive landfalls. Here, we attempt to understand this regional contrast using a suite of observations for the period 1979–2024. First, we demonstrate that while the El Niño Southern Oscillation (ENSO), the Atlantic Multidecadal Oscillation (AMO) and the North Atlantic Oscillation (NAO) can explain ~23% of the interannual variability in landfalls over the Gulf Coast, the variance explained by them for the Atlantic Coast is statistically insignificant. Next, we show that this striking difference may be attributed to the regional patterns of wind shear, steering flow and air-sea thermodynamic state excited by those modes of variability. The differential control exerted by ENSO, AMO, and NAO on landfalling Florida TCs, in combination with decadal trends in those modes, is likely responsible for the observed increases in landfalls over Florida's Gulf Coast.

Florida↗
DOE OSTI2026

Spring Dust in Colorado Plateau: Transport Pathways and Interannual Variability Derived From Two Decades of MERRA‐2 Reanalysis

Spring dust storms in the U.S. Southwest significantly impact environmental and human systems, yet their climatological patterns and driving mechanisms remain poorly understood. Using two decades of MERRA-2 reanalysis data and self-organizing map clustering, we identified four distinct dust transport pathways from surrounding and remote deserts for the top 10% of spring dust events impacting the Colorado Plateau: (a) intense southwesterly near-surface transport where eastward upward transport is blocked by topography, (b) substantial remote transport plus weaker surface emissions, (c) moderate southwesterly transport with stronger upper-level transport, and (d) rare northwesterly pattern linked to the Great Basin sources, which accounts for 38%, 14%, 37%, and 11% of total events, respectively. The frequency and intensity of these events strongly correlate with large-scale climate variability, with increased dust events during La Niña. Our results provide a robust framework for improving seasonal dust forecasts and understanding future dust dynamics under climate change.

aerosol transport↗
DOE OSTI2026

Tendencies of Soil Microbial NO Emissions During HI‐SCALE as Predicted by a Nitrification/Denitrification Scheme

Many atmospheric chemical processes, including the formation of secondary organic aerosol (SOA), are strongly modulated by the reactions of NO and NO 2 (NO x ). Though NO x is controlled by anthropogenic emissions near urban areas, in rural areas soil microbes can be a significant contribution to NO emission globally. The relative rates of emissions of different nitrogen-containing species (e.g., NO, N 2 O, HONO, and N 2 ) are strong functions of soil properties such as temperature, moisture content, pH, and soil carbon and nitrogen pools. However, typical large-scale biogeochemical models either express these emissions simplistically, or not at all. Here we investigate the potential impact of soil NO emissions on atmospheric chemistry and SOA formation over regional and monthly time scales, specifically the 2016 spring and summer Intensive Observational Periods (IOPs) of the Holistic Interactions of Shallow Clouds, Aerosols and Land Ecosystems (HI-SCALE) field campaign. We implement the soil NO nitrification/denitrification parameterization of Rasool et al. (2019) into the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem), supplemented by a 1-km soil moisture analysis. We then simulate both IOPs over the U.S. Great Plains, evaluating against ground stations and flight data. We show that soil NO emissions can account for a large fraction of total NO x and locally increase O 3 concentrations by up to 25%, while alleviating negative biases of gases and aerosols toward observations. Soil moisture and temperature changes between IOP1 and IOP2 lead to overall differences in emissions, but with large regional variability due to heterogeneous surface characteristics.

Atmoshpheric Chemistry↗
DOE OSTI2026

ELM‐MOSART‐DOC: A Large‐Scale Riverine Dissolved Organic Carbon Model and Its Application Over the United States

Riverine dissolved organic carbon (DOC), primarily sourced from soil organic carbon (SOC), plays a crucial role in regional and global carbon cycles. However, the complexities of the underlying mechanisms and limited observations present significant challenges for predictive understanding of DOC at regional or larger scales. Recently, we developed a machine learning‐based (ML) map of DOC transformation rates, bridging the gap between SOC and DOC leaching flux and simplifying terrestrial DOC representation. Building on this advancement, we introduce ELM‐MOSART‐DOC, a DOC module integrated into the riverine component of the Energy Exascale Earth System Model (E3SM)—the Model for Scale Adaptive River Transport (MOSART). ELM‐MOSART‐DOC simulates DOC transport and transformation across both headwater streams and river networks, including those managed. Model validation demonstrates the ability of ELM‐MOSART‐DOC to accurately capture long‐term average DOC concentrations, with Kling‐Gupta Efficiency (KGE) scores of 0.58 and 0.76 at large and local stations, respectively. We further assess the impact of reservoirs through different simulation schemes, revealing that reservoirs significantly alter DOC fluxes by regulating streamflow patterns and promoting DOC mineralization. Model simulations indicate that reservoirs reduce total DOC flux from the Mississippi River into the ocean by 7.5%, with the long‐term average annual export decreasing from 3.34 to 3.14 teragrams (Tg) per year. ELM‐MOSART‐DOC integrates process‐based modeling with ML parameterization to enhance the predictive understanding of riverine biogeochemical processes. This approach reduces uncertainties in modeling regional and global carbon cycle ESMs and provides new insights into carbon cycling and its implications for global environmental change.

Li, Lingbo [Univ. of Houston, TX (United States); ↗