On the reduced damage tolerance of fine-grained nuclear graphite at elevated temperatures using in situ 4D tomographic imaging
Not Available
SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Not Available
Not Available
Abstract Progress towards the energy breakthroughs needed to combat climate change can be significantly accelerated through the efficient simulation of atomistic systems. However, simulation techniques based on first principles, such as density functional theory (DFT), are limited in their practical use due to their high computational expense. Machine learning approaches have the potential to approximate DFT in a computationally efficient manner, which could dramatically increase the impact of computational simulations on real-world problems. However, they are limited by their accuracy and the cost of generating labeled data. Here, we present an online active learning framework for accelerating the simulation of atomic systems efficiently and accurately by incorporating prior physical information learned by large-scale pre-trained graph neural network models from the Open Catalyst Project. Accelerating these simulations enables useful data to be generated more cheaply, allowing better models to be trained and more atomistic systems to be screened. We also present a method of comparing local optimization techniques on the basis of both their speed and accuracy. Experiments on 30 benchmark adsorbate-catalyst systems show that our method of transfer learning to incorporate prior information from pre-trained models accelerates simulations by reducing the number of DFT calculations by 91%, while meeting an accuracy threshold of 0.02 eV 93% of the time. Finally, we demonstrate a technique for leveraging the interactive functionality built in to Vienna ab initio Simulation Package (VASP) to efficiently compute single point calculations within our online active learning framework without the significant startup costs. This allows VASP to work in tandem with our framework while requiring 75% fewer self-consistent cycles than conventional single point calculations. The online active learning implementation, and examples using the VASP interactive code, are available in the open source FINETUNA package on Github.
Humans are driving biodiversity change, which also alters community functional traits. However, how changes in the functional traits of the community alter ecosystem functions—especially belowground—remains an important gap in our understanding of the consequences of biodiversity change. We test hypotheses for how the root traits of the root economics space (composed of the collaboration and conservation gradients) are associated with proxies for ecosystem functioning across grassland and forest ecosystems in both observational and experimental datasets from 810 plant communities. First, we assessed whether community-weighted means of the root economics space traits adhered to the same trade-offs as species-level root traits. Then, we examined the relationships between community-weighted mean root traits and aboveground biomass production, root standing biomass, soil fauna biomass, soil microbial biomass, decomposition of standard and plot-specific material, ammonification, nitrification, phosphatase activity, and drought resistance. We found evidence for a community collaboration gradient but not for a community conservation gradient. Yet, links between community root traits and ecosystem functions were more common than we expected, especially for aboveground biomass, microbial biomass, and decomposition. These findings suggest that changes in species composition, which alter root trait means, will in turn affect critical ecosystem functions.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Here, the success of Y6-type nonfullerene small molecule acceptors (NF-SMAs) in polymer solar cells (PSCs) can be attributed to their unique honeycomb stacking style, which leads to favorable thin-film morphologies. The intermolecular interactions related to the crystallization tendency of these NF-SMAs is closely governed by their electron accepting end groups. For example, the high performance Y6 derivative L8-BO (BTP-4F) presents three types of stacking modes in contrast to two stacking modes of Y6. Hence, it is ultimately interesting to obtain more insight on the packing properties and the preferences influenced by chemical modifications such as end group engineering. This work designs and synthesizes asymmetric and symmetric L8-BO derivatives with brominated end groups and explores the stacking preferences in various modes. The asymmetric BTP-3FBr displays an optimized crystallization tendency and thin film morphology, leading to a decent power conversion efficiency (PCE) of 18.34% in binary devices and a top PCE of 19.32% in ternary devices containing 15 wt% IDIC as the second acceptor.
The sluggish kinetics and insufficient durability of platinum-based catalysts remain crucial barriers limiting proton-exchange-membrane fuel cells (PEMFCs) deployment. Here, we report a theory-guided synthesis combined with rare-earth templating to realize a previously inaccessible Pt 5 Co-like phase with tailored atomic-scale strain. Guided by density functional theory (DFT) calculations, we identified that a Pt 5 Co-like sublayer can induce a unique mild compressive strain (−1.24%) to the Pt(111) shell and an optimal *OH binding energy shift (ΔE ≈ 0.11 eV). This shift positions the alloy catalyst near the apex of the oxygen reduction reaction activity volcano. This prediction guided the synthesis of ternary alloy Pt 5 (Ce)Co@Pt multilayer nanoparticles, featuring a Ce-stabilized core, a Pt 5 Co-like sublayer, and a Pt-rich shell. This catalyst demonstrates both exceptionally high activity and durability, achieving a mass activity of 2.6 A∙mg Pt −1 in rotating disk electrode testing. In fuel cell membrane electrode assembly tests, Pt 5 (Ce)Co@Pt achieves a current density of 1.9 A∙cm −2 at 0.7 V under heavy-duty vehicle conditions. Remarkably, it maintains 1.2 A∙cm −2 after 1 80 000 AST cycles, doubling the U.S. DOE 2025 target. This work demonstrates a rational design strategy that DFT-guided strain engineering integrates with rare-earth templating to advance Pt-based catalysts for fuel cell applications.
ABSTRACT Urban areas experience the impact of natural disasters, such as heatwaves and flash floods, disparately in different neighbourhoods across a city. The demand for precise urban hydrometeorological and hydroclimatological modelling to examine this disparity, and the interacting challenges posed by climate change and urbanisation, has thus surged. The Weather Research and Forecasting (WRF) model has served such operational and research purposes for decades. Recent advancements in WRF, including enhanced numerical schemes and sophisticated urban atmospheric‐hydrological parameterizations, have empowered the simulation of urban geophysical processes at high resolution (~1 km), but even this resolution misses significant urban microclimate variability. This study applies the large‐eddy simulations (LES) mode within WRF, coupled with single‐layer urban canopy models (SLUCM), to enable even finer‐scale modelling (150 m) of the Urban Heat Island (UHI) effect in the Baltimore metropolitan area. We run nine scenarios to evaluate various methods of initializing soil moisture and various spinup lead times, and to assess the impact of WRF's Mosaic approach in depicting subgrid‐scale processes. We evaluate the scenarios by comparing the WRF simulated land surface temperature (LST) against Landsat LST and the WRF simulated hourly 2‐m air temperatures (AT) with observations from eight weather stations across the domain. Results underscore the paramount influence of the lead spinup time on the spatiotemporal distribution of simulated soil moisture, consequently shaping WRF's efficacy in predicting the UHI. Furthermore, interpolating soil moisture‐related parameters from the parent for child domain initialization yields a notable reduction in mean and root‐mean‐squared errors. This improvement was particularly evident in simulations with the longest spinup time, affirming the importance of carefully designing the initialization of soil moisture for improved urban temperature predictions.
We give a rigorous proof that in any free quantum field theory with a finite group global symmetry G, on a compact spatial manifold, at sufficiently high energy, the density of states ρ α (E) for each irreducible representation α of G obeys a universal formula as conjectured by Harlow and Ooguri. We further prove that this continues to hold in a weakly coupled quantum field theory, given an appropriate scaling of the coupling with temperature. This generalizes similar results that were previously obtained in (1 + 1)-D to higher spacetime dimension. We discuss the role of averaging in the density of states, and we compare and contrast with the case of continuous group G, where we prove a universal, albeit different, behavior.
A vast array of (metastable) vacuum solutions arise from string compactifications, each leading to different 4-d laws of physics. The space of these solutions, known as the string landscape, allows for an environmental solution to the cosmological constant problem. We examine the possibility of an environmental solution to the gauge hierarchy problem. We argue that the landscape favors softly broken supersymmetric models over particle physics models containing quadratic divergences, such as the Standard Model. We present a scheme for computing relative probabilities for supersymmetric models to emerge from the landscape. The probabilities are related to the likelihood that the derived value of the weak scale lies within the Agrawal et al. (ABDS) allowed window of values leading to atoms as we know them. This then favors natural SUSY models over unnatural (SUSY and other) models via a computable probability measure.
Methane (CH 4 ) emissions from wetland ecosystems are controlled by redox conditions in the soil, which are currently underrepresented in Earth system models. Plant-mediated radial oxygen loss (ROL) can increase soil O 2 availability, affect local redox conditions, and cause heterogeneous distribution of redox-sensitive chemical species at the root scale, which would affect CH 4 emissions integrated over larger scales. In this study, we used a subsurface geochemical simulator (PFLOTRAN) to quantify the effects of incorporating either spatially homogeneous ROL or more complex heterogeneous ROL on model predictions of porewater solute concentration depth profiles (dissolved organic carbon, methane, sulfate, sulfide) and column integrated CH 4 fluxes for a tidal coastal wetland. From the heterogeneous ROL simulation, we obtained 18% higher column averaged CH 4 concentration at the rooting zone but 5% lower total CH 4 flux compared to simulations of the homogeneous ROL or without ROL. This difference is because lower CH 4 concentrations occurred in the same rhizosphere volume that was directly connected with plant-mediated transport of CH 4 from the rooting zone to the atmosphere. Sensitivity analysis indicated that the impacts of heterogeneous ROL on model predictions of porewater oxygen and sulfide concentrations will be more important under conditions of higher ROL fluxes or more heterogeneous root distribution (lower root densities). Despite the small impact on predicted CH 4 emissions, the simulated ROL drastically reduced porewater concentrations of sulfide, an effective phytotoxin, indicating that incorporating ROL combined with sulfur cycling into ecosystem models could potentially improve predictions of plant productivity in coastal wetland ecosystems.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Test article vibration test responses will vary from one test laboratory to another due to differences in shaker and fixture dynamic characteristics. This is also seen between the field and laboratory due to the differing dynamic characteristics between field assembly loading and boundary conditions compared to the laboratory configuration. This work introduces a technique called Impedance Fixture Neutralization which customizes input forces to cause consistent responses for the Device Under Test across different vibration testing conditions. The customized force neutralizes the dynamic variations between configurations for the Device Under Test. The device responses can be replicated in several situations: when using a different fixture with the same attachment points, when the test article mounting location on the fixture changes, when the force location changes, or any combination of these situations. Impedance Fixture Neutralization uses the uncoupled dynamic characteristics of the test article and excitation fixtures to customize the input thereby causing the same test article responses between two mounting configurations (either field to laboratory or two different laboratory configurations). The application of the technique is shown using an analytical model of a two-beam system and analytically using experimental FRFs from plate and frame component hardware characterization tests. In both cases a device under test is attached to two test fixtures wherein the dynamic differences in the configurations are neutralized.