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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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Prompted by limited available data, we explore data-aggregation strategies for material datasets, aiming to boost machine learning performance. Our findings suggest that intuitive aggregation schemes are ineffective in enhancing predictive accuracy.
The plasticity of amorphous solids undergoing shear is characterized by quasi-localized rearrangements of particles. While many models of plasticity exist, the precise relationship between the plastic dynamics and the structure of a particle’s local environment remains an open question. Previously, machine learning was used to identify a structural predictor of rearrangements called “softness.” Although softness has been shown to predict which particles will rearrange with high accuracy, the method can be difficult to implement in experiments where data are limited and the combinations of descriptors it identifies are often difficult to interpret physically. Here, we address both of these weaknesses, presenting two major improvements to the standard softness method. First, we present a natural representation of each particle’s observed mobility, allowing for the use of statistical models that are both simpler and provide greater accuracy in limited datasets. Second, we employ persistent homology as a systematic means of identifying simple, topologically informed, structural quantities that are easy to interpret and measure experimentally. We test our methods on two-dimensional athermal packings of soft spheres under quasi-static shear. We find that the same structural information that predicts small variations in the response is also predictive of where plastic events will localize. We also find that an excellent accuracy is achieved in athermal sheared packings using simply a particle’s species and the number of nearest neighbor contacts.
Nanoelectronic devices emulating neuro-synaptic functionalities through their intrinsic physics at low operating energies is imperative toward the realization of brain-like neuromorphic computers. In this work, we leverage the non-linear voltage dependent partial polarization switching of a ferroelectric field effect transistor to mimic plasticity characteristics of biological synapses. Here, we provide experimental measurements of the synaptic characteristics for a 28nm high-k metal gate technology based device and develop an experimentally calibrated device model for large-scale system performance prediction. Decoupled read-write paths, ultra-low programming energies and the possibility of arranging such devices in a cross-point architecture demonstrate the synaptic efficacy of the device. Our hardware-algorithm co-design analysis reveals that the intrinsic plasticity of the ferroelectric devices has potential to enable unsupervised local learning in edge devices with limited training data
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This Perspective article provides a brief overview of the topic of wind and solar energy droughts (henceforth WSDs). It does not attempt to provide a complete literature review of the subject but rather highlights some of the main concepts associated with WSDs. These include wind and solar energy drought definitions and metrics; meteorological conditions producing WSDs; a comparison of their characteristics with hydrologic droughts and hydropower droughts; model-based and observational datasets useful for WSD analyses; the linkage of WSDs to transmission, storage, and demand response; the potential impacts of WSDs vs energy demand variations; wind and solar flood events; WSD predictability; WSD dependency on climate modes of variability; climate change impacts on WSDs; and the special challenge of evaluating the characteristics of WSDs in developing countries that have limited historical data available. Finally, the manuscript identifies research areas that the authors believe would provide immediate benefit to energy system planners.
Scientists seek to understand the causal processes that generate sustainability problems and determine effective solutions. Yet, causal inquiry in nature–society systems is hampered by conceptual and methodological challenges that arise from nature–society interdependencies and the complex dynamics they create. Here, we demonstrate how sustainability scientists can address these challenges and make more robust causal claims through better integration between empirical analyses and process- or agent-based modeling. To illustrate how these different epistemological traditions can be integrated, we present four studies of air pollution regulation, natural resource management, and the spread of COVID-19. The studies show how integration can improve empirical estimates of causal effects, inform future research designs and data collection, enhance understanding of the complex dynamics that underlie observed temporal patterns, and elucidate causal mechanisms and the contexts in which they operate. These advances in causal understanding can help sustainability scientists develop better theories of phenomena where social and ecological processes are dynamically intertwined and prior causal knowledge and data are limited. The improved causal understanding also enhances governance by helping scientists and practitioners choose among potential interventions, decide when and how the timing of an intervention matters, and anticipate unexpected outcomes. Methodological integration, however, requires skills and efforts of all involved to learn how members of the respective other tradition think and analyze nature–society systems.
There is overwhelming evidence that forest trees are locally adapted to climate. Thus, genecological models based on population phenotypes have been used to measure local adaptation, infer genetic maladaptation to climate, and guide assisted migration. However, instead of phenotypes, there is increasing interest in using genomic data for gene resource management. We used whole-genome resequencing and common-garden experiments to understand the genetic architecture of adaptive traits in black cottonwood. We studied the potential of using genome-wide association studies (GWAS) and genomic prediction to detect causal loci, identify climate-adapted phenotypes, and inform gene resource management. We analyzed population structure by partitioning phenotypic and genomic (single-nucleotide polymorphism) variation among 840 genotypes collected from 91 stands along 16 rivers. Most phenotypic variation (60 to 81%) occurred among populations and was strongly associated with climate. Population phenotypes were predicted well using genomic data (e.g., predictive abilityr> 0.9) but almost as well using climate or geography (r> 0.8). In contrast, genomic prediction within populations was poor (r< 0.2). We identified many GWAS associations among populations, but most appeared to be spurious based on pooled within-population analyses. Hierarchical partitioning of linkage disequilibrium and haplotype sharing suggested that within-population genomic prediction and GWAS were poor because allele frequencies of causal loci and linked markers differed among populations. Given the urgent need to conserve natural populations and ecosystems, our results suggest that climate variables alone can be used to predict population phenotypes, delineate seed zones and deployment zones, and guide assisted migration.
Boilers are combustion devices that provide process heat and are integral to many industrial facilities. Historically, outside of the pulp and paper industry, most boilers burned fossil fuels, although interest in decarbonization has been leading to an increased use of renewable fuels in boilers. These boilers, including those in the biorefineries, are often large sources of air pollutant emissions, and the characterization of these emissions is critical to obtaining air permits and ensuring protection of the surrounding air quality. Several industrial boilers and new biorefineries allow utilization of biomass-derived fuels (e.g. wastewater sludge, lignin, etc.) produced during their operation as a fuel for the boiler to meet process energy needs. However, there is limited empirical data on emission factors for the burning of unconventional fuels, such as solid-gas mixtures containing biomass residues. To fill this gap, we carry out a comprehensive data survey, collecting information on emission factors for boilers burning either a single or a mixture of solid and gaseous biomass-derived fuels. We review multiple hard-to-obtain and unconventional data sources, such as air permit applications, stack test data, and industry-sponsored data collection efforts, to compile emission factors for biomass-derived fuels. We then compare this data with wood residue emission factors from the U.S. Environmental Protection Agency’s AP-42 emission factor database. Our results indicate that the emission factors for boilers burning unconventional fuels vary widely depending on the fuel composition, boiler type, and fuel characteristics. Overall, we find that median emission factors of selected biomass-derived fuels are typically lower than those of wood residue boilers in AP-42. The information collected herein could be useful to permitting agencies and industries utilizing boilers who may want to reduce the carbon impact of their facilities by combusting biomass-derived wastes for process energy needs, for more accurate emission estimation for permitting.
Tritium breeding blankets within D-T-fueled fusion reactors contain lithium compounds and typically require neutron multiplier materials to achieve a tritium breeding ratio (TBR) consistent with self-sustaining operation. Liquid breeder blankets have some advantages over solid blankets, and previous blanket studies have investigated liquid metal as well as liquid salt–based blankets. Liquid salts have reduced magnetohydrodynamic effects as compared to liquid metals, but typically have a lower TBR. Recently, advanced fission reactor concepts have considered chloride-based salts in their design, and there is a significant amount of research work occurring to study these salts. Chloride salts have previously been considered for fusion reactors, but studies have typically found lower breeding ratios than for fluoride salts, such as 2(LiF)-BeF 2 (flibe) so they have not been further developed. In this work, we use a one-dimensional cylindrical radiation transport model of a conceptual tokamak fusion reactor to investigate the neutronics feasibility of using a chloride salt–based blanket that uses chlorine enriched in 37 Cl, which has both a low capture cross section and a substantial (n,2n) cross section. It is found that chloride salts (LiCl mixed with BeCl 2 and/or PbCl 2 ) can potentially achieve a ~3% to 5% higher TBR than fluoride molten salts, notably flibe, in the absence of a solid multiplier. Including a solid multiplier, however, does narrow this advantage, with TBRs estimated within ~1% of flibe with a 2-cm Be multiplier. Chloride salts can also reach lower melting points than flibe, potentially improving the scope for the use of reduced activation ferritic-martensitic steel as a structural material. There is substantial uncertainty in the calculations driven by limited thermochemical data for the Cl salts, plus cross-section uncertainties. The production of 36 Cl through 35 Cl(n,g) and 37 Cl(n,2n) has the potential to challenge the waste disposal rating of the blanket. Calculations indicate that, while this is not an immediate showstopper, this case depends upon the exact waste disposal rating criteria used for 36 Cl. Further work could reduce these uncertainties with improved thermochemical data, higher-fidelity modeling for downselected salts, and more refined waste disposal calculations and regulatory guidance. Lastly, it must be recognized that, as for all molten salts, corrosion and chemistry can present appreciable technical challenges that require further assessment in developing a practical blanket concept, and also that the enrichment of chlorine presents an additional technical and supply chain challenge.
We present a machine learning (ML) framework for predicting the structural dimensionality of hybrid metal halides (HMHs), including organic-inorganic perovskites, using a combination of chemically-informed feature engineering and advanced class-imbalance handling techniques. This study is motivated by the small and highly imbalanced nature of experimentally available HMH datasets, which limits the applicability and reliability of conventional ML approaches. The dataset, consisting of 494 HMH structures, is highly imbalanced across dimensionality classes (0D, 1D, 2D, 3D), posing significant challenges to predictive modeling. To mitigate this limitation, the dataset was augmented to 1336 samples using the synthetic minority oversampling technique, enabling improved learning of underrepresented dimensionality classes while preserving chemically meaningful feature relationships. We developed interaction-based descriptors designed to capture coupled steric and polarity effects relevant to dimensionality prediction, which are not readily captured by standard single-parameter or composition-only descriptors. These descriptors are integrated into a multi-stage workflow combining feature selection, ensemble stacking, and performance optimization. Our approach significantly improves F1-scores for underrepresented classes, achieving robust cross-validation performance across all dimensionalities. This work demonstrates a generalizable strategy for extracting reliable and interpretable structure–dimensionality relationships from limited experimental data, enabling pre-synthesis screening of organic cations and providing a practical blueprint for small-data ML in hybrid materials systems.
Abstract We study the collapse and expansion of a cavitation bubble in a deformable porous medium. We develop a continuum-scale model that couples compressible fluid flow in the pore network with the elastic response of a solid skeleton. Under the assumption of spherical symmetry, our model can be reduced to an ordinary differential equation that extends the Rayleigh–Plesset equation to bubbles in soft porous media. The extended Rayleigh–Plesset equation reveals that finite-size effects lead to the breakdown of the universal scaling relation between bubble radius and time that holds in the infinite-size limit. Our data indicate that the deformability of the porous medium slows down the collapse and expansion processes, a result with important consequences for wide-ranging phenomena, from drug delivery to spore dispersion.
Klebsiella aerogenes is an opportunistic pathogen and a growing cause of healthcare-associated infections, characterized by multidrug resistance and the emergence of global high-risk clones. However, regional genomic surveillance data remain limited. Here, we sought to characterize the population structure, transmission dynamics and resistance mechanisms of clinical K. aerogenes in Albuquerque, New Mexico. We sequenced 177 clinical isolates collected between 2021 and 2023. We also developed a novel, species-specific PopPUNK database to facilitate rapid, high-resolution typing. The New Mexico K. aerogenes population was diverse but dominated by two global pandemic lineages, ST93 (47.5%) and ST4 (7.9%), which were significantly enriched for the virulence factors yersiniabactin and colibactin. Genomic evidence for recent local transmission was rare, with only four putative transmission pairs identified. The resistome was characterized by intrinsic and adaptive mutations. Nearly all isolates possessed gyrA mutations associated with decreased fluoroquinolone susceptibility. Mutations in the AmpC regulator AmpD and the outer membrane porin Omp36 were common, particularly within the dominant ST93 lineage. These mutations have been associated with increased AmpC-mediated carbapenem resistance. Our findings underscore the critical importance of genomic surveillance to monitor the transmission and evolution of adaptive resistance.
The polarization of Λ hyperons is preserved in the angular distribution of their decay products. This property allows one to study the spin structure of the Λ. In semi-inclusive deep inelastic scattering where a high energy lepton interacts with a nucleon target and one or more hadrons and the scattered lepton are detected in the final state, the probability for a struck quark to impart the polarization of the lepton to the Λ may be measured. In particular, in electron-proton scattering this quantity may be related to the longitudinal light quark polarization of the Λ. Currently, limited experimental data cannot discriminate between different models of Λ spin structure. This work reports on the measurement of the longitudinal spin transfer 𝐷$^{Λ}_{𝐿𝐿′}$ to the Λ using data taken by the CLAS12 spectrometer at Jefferson Lab with a 10.6 GeV longitudinally polarized electron beam and an unpolarized hydrogen target. This measurement is the most precise to date, and, in comparison with theory predictions, it offers valuable insight into the relative dominance of current and target fragmentation in Λ production.
The neutron-deficient Ca isotopes continue to attract attention due to their importance for testing isospin symmetry and their relevance in capture reactions of interest for nova nucleosynthesis and the shape of light curves in Type I x-ray bursts. To date, spectroscopic information on 38,39 Ca is largely limited to data on lower-spin excited states. Here, we report in-beam γ-ray spectroscopy of complementary higher-spin, complex-structure states in 39 Ca populated in fast-beam-induced, momentum-dissipative processes leading to neutron pickup onto excited configurations of the projectile, 9 Be( 38 Ca*, 39 Ca + γ)X. Such a dissipative reaction was recently characterized for the case of inelastic scattering of 38 Ca off 9 Be, 9 Be( 38 Ca, 38 Ca + γ)X. Additional data and discussion on the nuclear structure of 38 Ca is also presented. Furthermore, an explanation for the more-complex-structure states, populated with small cross sections in one-nucleon knockout reactions, and observed in the tails of their longitudinal momentum distributions, is also offered.
Halo structure is an interesting exotic configuration developed in some light weakly bound nuclei, where a valence particle orbits a nuclear core. Signatures of halo structure can be observed in the angular distributions of the elastic scattering induced by these nuclei at energies around the Coulomb barrier. There are some well-studied reactions with neutron-rich halo nuclei, such as 6 He and 11 Li . However, the information is scarce on the proton-rich side. Recent works confirm the halo structure in the 8B nuclei but still lack more experimental studies for other proton-halo candidates, such as 12 N and 17 F. In this work, we report experimental data for the elastic scattering of 12 N on 197 Au target at 𝐸 lab = 70 MeV. The 12 N is a proton-rich nucleus with proton separation energy 𝑆 𝑝 = 600 keV, which is higher than the 8 B (𝑆 𝑝 = 137 keV) and almost the same as the 17 F (𝑆 𝑝 = 601 keV). Data were obtained at the Cyclotron Institute of Texas A&M University where the 12 N radioactive beam was produced by the momentum achromatic recoil spectrometer. The calculation of the optical model was used to fit the measured angular distribution for the elastic scattering and to obtain the reaction cross section 𝜎 𝑅 . We also performed continuum discretized coupled-channel calculations to compare with the experimental data. The angular distribution of the elastic cross sections exhibits a suppression of the Fresnel peak. From the fitting of the optical model, we obtain the total reaction cross section, 𝜎 𝑅 = 1269 ± 41 mb. The agreement between the CDCC calculation and the experimental elastic scattering data is limited and the breakdown does not exhaust the measured 𝜎 𝑅 . The resulting reduced reaction cross section 𝜎 Red for 12 N + 197 Au is large and comparable to the one obtained for the 8 B + 208 Pb system. This suggests a strong decoupling of the valence proton from the core because of the low binding energy and a dynamic polarization effect. Here, more research is required to estimate the contribution of core excitation.
Throughout U.S. Department of Energy (DOE) complexes, safety engineers employ the five-factor formula to calculate the source term (ST) that includes parameters of airborne release fraction (ARF), respirable fraction (RF) and damage ratio (DR). Limited experimental data on fragmentation of solids, such as ceramic pellets (i.e., PuO2), and container breach due to mechanical insults (i.e., drop and forklift impact), can be supplemented by modeling and simulation using high fidelity computational tools to estimate these parameters. This paper presents the use of Sandia National Laboratories' SIERRA solid mechanics (SM) finite element code to investigate the behavior of the widely utilized waste container (such as 7A Drum) subject to a range of free fall impact and puncture scenarios. The resulting behavior of the container is assessed, and the estimates are presented for bounding DRs from calculated breach areas for the various accident conditions considered. This paper also describes a novel multiscale constitutive model recently implemented in SIERRA/SM that simulates the fracture of brittle materials such as PuO 2 and determines ARF during the fracture process. Furthermore, comparisons are made between model predictions and simple bench-top experiments.
Abstract Novel high temperature (≥ 700°C) Heat Transfer Medias (HTMs, e.g., molten salts) and corrosion-resistant Containment Materials (CMs, e.g., metal alloys or ceramics) are necessary for concentrated solar power (CSP) given the emphasis on higher temperatures and high cycle efficiency in the 3rd generation CSP (Gen3 CSP) technologies. In early 2019, we sent out an online survey to the Gen3 CSP community to fully assess the communal needs for thermophysical properties measurements of which HTMs and CMs, and what temperature range and other testing environments would be ideal for those materials. Based on the recorded responses, seven unique HTMs and twenty-six unique CMs were identified. Since then the list has been constantly updated, following our interactions and inputs from the Gen3 CSP community, with some new materials substituting their older counterparts. Currently, there are total of ten unique HTMs and twenty-nine unique CMs that are under consideration by the Gen3 CSP community. By analyzing the available body of research to date and combining it with our survey data from within the Gen3 CSP community, this paper presents trends of what people in the CSP world are thinking regarding materials worth investigating and suggests which thermophysical property measurements are critical to advance high-temperature CSP systems.
The RNA-guided nuclease Cas9 has unlocked powerful methods for perturbing both the genome through targeted DNA cleavage and the regulome through targeted DNA binding, but limited biochemical data have hampered efforts to quantitatively model sequence perturbation of target binding and cleavage across diverse guide sequences. We present scalable, sequencing-based platforms for high-throughput filter binding and cleavage and then perform 62,444 quantitative binding and cleavage assays on 35,047 on- and off-target DNA sequences across 90 Cas9 ribonucleoproteins (RNPs) loaded with distinct guide RNAs. We observe that binding and cleavage efficacy, as well as specificity, vary substantially across RNPs; canonically studied guides often have atypically high specificity; sequence context surrounding the target modulates Cas9 on-rate; and Cas9 RNPs may sequester targets in nonproductive states that contribute to “proofreading” capability. Lastly, we distill our findings into an interpretable biophysical model that predicts changes in binding and cleavage for diverse target sequence perturbations.