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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 289 records · Page 16
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Thermodynamic Water Activity Explains the Unusual Electrochemical Stability of Aqueous Deep Eutectic Solvents

The presence of water in nonaqueous deep eutectic solvent (DES) electrolytes has been debated in recent years, with efforts ranging from its complete removal to willful addition. It was shown that controlled amounts of water can be beneficial, as it not only enhances the physicochemical properties of these electrolytes but also has no significant detrimental effects on their electrochemical stability. Despite these advantages, there is still limited understanding of how water interacts with DES systems at the molecular level. This study examines the water activity in ethylene glycol and glycerol, as well as their binary mixtures with choline chloride to form the DESs ethaline and glyceline, respectively. In this work, we show that the high electrochemical stability of glyceline is related to its lower water activity compared to ethaline and can be attributed to the robust H-bonding network formed by the three hydroxyl groups of glycerol. Its 3D H-bond network effectively integrates water molecules within its solvent structure, reducing degradation and maintaining stability at higher water contents. The deviations from the ideal Raoult's law behavior are reflected in the water activity and activity coefficients, which highlight the intricate H-bond interactions within DES-water mixtures. Water acts like a lubricant within the more viscous DES mixtures without being detrimental to their electrochemical performance. The presented results emphasize the necessity of customizing DES-water compositions to enhance their performance as electrolytes, especially in flow battery applications where electrochemical stability, ionic conductivity, and fluidity are of utmost importance.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Leaf traits and canopy structure together explain canopy functional diversity: an airborne remote sensing approach

Plant functional diversity is strongly connected to photosynthetic carbon assimilation in terrestrial ecosystems. However, many of the plant functional traits that regulate photosynthetic capacity, including foliar nitrogen concentration and leaf mass per area, vary significantly between and within plant functional types and vertically through forest canopies, resulting in considerable landscape-scale heterogeneity in three dimensions. Hyperspectral imagery has been used extensively to quantify functional traits across a range of ecosystems but is generally limited to providing information for top-of-canopy leaves only. On the other hand, lidar data can be used to retrieve the vertical structure of forest canopies. Because these data are rarely collected at the same time, there are unanswered questions about the effect of forest structure on the 3-D spatial patterns of functional traits across ecosystems. In the United States, the National Ecological Observatory Network's Airborne Observation Platform (NEON AOP) provides an opportunity to address this structure-function relationship by collecting lidar and hyperspectral data together across a variety of ecoregions. With a fusion of hyperspectral and lidar data from the NEON AOP and field-collected foliar trait data, we assessed the impacts of forest structure on spatial patterns of N. In addition, we examine the influence of abiotic gradients and management regimes on top of canopy %N and total canopy N (i.e. the total amount of N (g/m 2 ) within a forest canopy) at a NEON site consisting of a mosaic of open longleaf pine and dense broadleaf deciduous forests. Here, our resulting maps suggest that in contrast with top-of-canopy values, total canopy N variation is dampened across this landscape resulting in relatively homogeneous spatial patterns. At the same time, we found that leaf functional diversity and canopy structural diversity showed distinct dendritic patterns related to the spatial distribution of plant functional types.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Functional divergence in nitrogen uptake rates explains diversity–productivity relationship in microalgal communities

Abstract The relationship between biodiversity and productivity has emerged as a central theme in ecology. Mechanistic explanations for this relationship suggest that the role organisms play in the ecosystem (i.e., niches or functional traits) is a better predictor of ecosystem stability and productivity than taxonomic richness. Here, we tested the capacity of functional diversity in nitrogen uptake in experimental microalgal communities to predict the complementarity effect (CE) and selection effect (SE) of biodiversity on productivity. We grew five algal species as monocultures and as polycultures in pairwise combinations in homogeneous (ammonium, nitrate, or urea alone) and heterogeneous nitrogen (mixed nitrogen) environments to determine whether complementarity between species may be enhanced in heterogeneous environments. We show that the positive diversity effects on productivity in heterogeneous environments resulted from complementarity effects with no positive contribution by species‐specific SEs. Positive biodiversity effects in homogeneous environments, when present (nitrate and urea treatments but not ammonium), were driven both by CE and SE. Our results suggest that functional diversity increases species complementarity and productivity mainly in heterogeneous resource environments. These results provide evidence that the positive effect of functional diversity on community productivity depends on the diversity of resources present in the environment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Explainability and extrapolation of machine learning models for predicting the glass transition temperature of polymers

Abstract Machine learning (ML) offers promising tools to develop surrogate models for polymers' structure–property relations. Surrogate models can be built upon existing polymer data and are useful for rapidly predicting the properties of unknown polymers. The accuracy of such ML models appears to depend on the feature space representation of polymers, the range of training data, and learning algorithms. Here, we establish connections between these factors for predicting the glass transition temperature (T g ) of polymers. Our analysis suggests linear models with fewer fitting parameters are as accurate as nonlinear models with many hidden and unexplainable parameters. Also, the performance of a monomer topology‐based ML model is found to be qualitatively identical to that of a physicochemical descriptor‐based ML model. We find that the ML models's performance in the extrapolative region is enhanced as the property range of the training data increases. Moreover, we establish newT g – polymer chemistry correlations via ML. Our work illustrates how ML can advance the fundamental understanding of polymer structure–property correlations and its efficacy for extrapolation problems.

Polymer Science↗
DOE OSTI2018

Not as easy as π: An insertional residue does not explain the π‐helix gain‐of‐function in two‐component FMN reductases

Abstract The π‐helix located at the tetramer interface of two‐component FMN‐dependent reductases contributes to the structural divergence from canonical FMN‐bound reductases within the NADPH:FMN reductase family. The π‐helix in the SsuE FMN‐dependent reductase of the alkanesulfonate monooxygenase system has been proposed to be generated by the insertion of a Tyr residue in the conserved α4‐helix. Variants of Tyr118 were generated, and their X‐ray crystal structures determined, to evaluate how these alterations affect the structural integrity of the π‐helix. The structure of the Y118A SsuE π‐helix was converted to an α‐helix, similar to the FMN‐bound members of the NADPH:FMN reductase family. Although the π‐helix was altered, the FMN binding region remained unchanged. Conversely, deletion of Tyr118 disrupted the secondary structural properties of the π‐helix, generating a random coil region in the middle of helix 4. Both the Y118A and Δ118 SsuE SsuE variants crystallize as a dimer. The MsuE FMN reductase involved in the desulfonation of methanesulfonates is structurally similar to SsuE, but the π‐helix contains a His insertional residue. Exchanging the π‐helix insertional residue of each enzyme did not result in equivalent kinetic properties. Structure‐based sequence analysis further demonstrated the presence of a similar Tyr residue in an FMN‐bound reductase in the NADPH:FMN reductase family that is not sufficient to generate a π‐helix. Results from the structural and functional studies of the FMN‐dependent reductases suggest that the insertional residue alone is not solely responsible for generating the π‐helix, and additional structural adaptions occur to provide the altered gain of function.

McFarlane, Jeffrey S.↗
DOE OSTI2015

Sneutrino Higgs models explain lepton non-universality in eejj, eνjj excesses

Recent searches for first-generation leptoquarks and heavy right-handed W R bosons have seen excesses in final states with electrons and jets. A bizarre property of these excesses is that they appear to violate lepton universality. With these results in mind, we study the phenomenology of supersymmetric models in which the Higgs arises as the sneutrino in an electron supermultiplet. Since the electron is singled out in this approach, one can naturally account for the lepton flavor structure of the excesses. In this work, we show that in such a framework, one can significantly alleviate the tension between the Standard Model and the data and yet evade current constraints from other searches. Finally we point out that correlated excesses are expected to be seen in future multilepton searches.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2016

Explaining dark matter and B decay anomalies with an L μ - L τ model

We present a dark sector model based on gauging the L μ - L τ symmetry that addresses anomalies in b→ sμ + μ - decays and that features a particle dark matter candidate. The dark matter particle candidate is a vector-like Dirac fermion coupled to the Z' gauge boson of the L μ - L τ symmetry. We compute the dark matter thermal relic density, its pair-annihilation cross section, and the loop-suppressed dark matter-nucleon scattering cross section, and compare our predictions with current and future experimental results. We demonstrate that after taking into account bounds from Bs meson oscillations, dark matter direct detection, and the CMB, the model is highly predictive: B physics anomalies and a viable particle dark matter candidate, with a mass of ~ (5 - 23) GeV, can be accommodated only in a tightly-constrained region of parameter space, with sharp predictions for future experimental tests. The viable region of parameter space expands if the dark matter is allowed to have L μ - L τ charges that are smaller than those of the SM leptons.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2021

Explaining and predicting human behavior and social dynamics in simulated virtual worlds: reproducibility, generalizability, and robustness of causal discovery methods

Ground Truth program was designed to evaluate social science modeling approaches using simulation test beds with ground truth intentionally and systematically embedded to understand and model complex Human Domain systems and their dynamics Lazer et al. (Science 369:1060–1062, 2020). Our multidisciplinary team of data scientists, statisticians, experts in Artificial Intelligence (AI) and visual analytics had a unique role on the program to investigate accuracy, reproducibility, generalizability, and robustness of the state-of-the-art (SOTA) causal structure learning approaches applied to fully observed and sampled simulated data across virtual worlds. In addition, we analyzed the feasibility of using machine learning models to predict future social behavior with and without causal knowledge explicitly embedded. In this paper, we first present our causal modeling approach to discover the causal structure of four virtual worlds produced by the simulation teams—Urban Life, Financial Governance, Disaster and Geopolitical Conflict. Our approach adapts the state-of-the-art causal discovery (including ensemble models), machine learning, data analytics, and visualization techniques to allow a human-machine team to reverse-engineer the true causal relations from sampled and fully observed data. We next present our reproducibility analysis of two research methods team’s performance using a range of causal discovery models applied to both sampled and fully observed data, and analyze their effectiveness and limitations. We further investigate the generalizability and robustness to sampling of the SOTA causal discovery approaches on additional simulated datasets with known ground truth. Our results reveal the limitations of existing causal modeling approaches when applied to large-scale, noisy, high-dimensional data with unobserved variables and unknown relationships between them. We show that the SOTA causal models explored in our experiments are not designed to take advantage from vasts amounts of data and have difficulty recovering ground truth when latent confounders are present; they do not generalize well across simulation scenarios and are not robust to sampling; they are vulnerable to data and modeling assumptions, and therefore, the results are hard to reproduce. Finally, when we outline lessons learned and provide recommendations to improve models for causal discovery and prediction of human social behavior from observational data, we highlight the importance of learning data to knowledge representations or transformations to improve causal discovery and describe the benefit of causal feature selection for predictive and prescriptive modeling.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2014

What are hot and what are not in an urban landscape: quantifying and explaining the land surface temperature pattern in Beijing, China

Understanding how landscape components affect the urban heat islands is crucial for urban ecological planning and sustainable development. The purpose of this research was to quantify the spatial pattern of land surface temperatures (LSTs) and associated heat fluxes in relation to land-cover types in Beijing, China, using portable infrared thermometers, thermal infrared imagers, and the moderate resolution imaging spectroradiometer. The spatial differences and the relationships between LSTs and the hierarchical landscape structure were analyzed with in situ observations of surface radiation and heat fluxes. Large LST differences were found among various land-use/land-cover types, urban structures, and building materials. Within the urban area, the mean LST of urban impervious surfaces was about 6–12°C higher than that of the urban green space. LSTs of built-up areas were on average 3–6°C higher than LSTs of rural areas. The observations for surface radiation and heat fluxes indicated that the differences were caused by different fractions of sensible heat or latent heat flux in net radiation. LSTs decreased with increasing elevation and normalized difference vegetation index. Variations in building materials and urban structure significantly influenced the spatial pattern of LSTs in urban areas. By contrast, elevation and vegetation cover are the major determinants of the LST pattern in rural areas. In summary, to alleviate urban heat island intensity, urban planners and policy makers should pay special attention to the selection of appropriate building materials, the reasonable arrangement of urban structures, and the rational design of landscape components.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2025

Explainable discrepancy checker and diagnosis for digital Twin-based supervisory control system

By virtually representing a physical object and process, a digital twin (DT) enables optimal autonomous operations by combining classical and novel frameworks in sensors, state predictions, and multi-input/multi-output systems. A DT’s values depend on how well models estimate quantities of interest and on how uncertainty is handled. Moreover, DTs often combine physics-based and data-driven models with mixed fidelities, where classical uncertainty quantification (UQ) struggles with many sources of uncertainty and real-time constraints. Here, this work presents a UQ-based discrepancy checking and diagnosis tool for a DT-based supervisory control system. The tool is developed using metadata from an automated DT development process to learn correlations between sources of uncertainties and outcomes. During operation, it compares predictions with measurements, attributes discrepancies to dominant sources, and recommends parameter and configuration updates. We verify the workflow on a synthetic temperature-control problem and deploy it on a virtual Thermal Energy Delivery System, reducing mismatch and improving control robustness.

22 - GENERAL STUDIES OF NUCLEAR REACTORS↗