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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 OSTI2023

Macroevolutionary diversity of traits and genomes in the model yeast genus Saccharomyces

Species is the fundamental unit to quantify biodiversity. In recent years, the model yeast Saccharomyces cerevisiae has seen an increased number of studies related to its geographical distribution, population structure, and phenotypic diversity. However, seven additional species from the same genus have been less thoroughly studied, which has limited our understanding of the macroevolutionary events leading to the diversification of this genus over the last 20 million years. Here, we show the geographies, hosts, substrates, and phylogenetic relationships for approximately 1,800 Saccharomyces strains, covering the complete genus with unprecedented breadth and depth. We generated and analyzed complete genome sequences of 163 strains and phenotyped 128 phylogenetically diverse strains. This dataset provides insights about genetic and phenotypic diversity within and between species and populations, quantifies reticulation and incomplete lineage sorting, and demonstrates how gene flow and selection have affected traits, such as galactose metabolism. These findings elevate the genus Saccharomyces as a model to understand biodiversity and evolution in microbial eukaryotes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Modeling the potential effects of rooftop solar on household energy burden in the United States

Policymakers at the federal and state level have begun to incorporate energy burden into equity goals and program evaluations, aiming to reduce energy burden below a high level of 6% for lower income households in the United States. Pairing an empirical household-level dataset spanning United States geographies together with modeled hourly energy demand curves, we show that rooftop solar reduces energy burden across a majority of adopters during our study period from a median of 3.3% to 2.6%. For low- and moderate-income adopters (at or below 80% and 120% of area median income, respectively), solar reduces median 2021 energy burden from 7.7% to 6.2%, and 4.1% to 3.3%, respectively. Importantly, solar reduces the rate of high or severe energy burden from 67% of all low-income households before adoption to 52% of households following adoption, and correspondingly from 21% to 13% for moderate-income households. Here, we show rooftop solar can support policy goals to reduce energy burden along with strategies such as weatherization and bill assistance.

14 SOLAR ENERGY↗
DOE OSTI2018

Phylogenetic patterns recover known HIV epidemiological relationships and reveal common transmission of multiple variants

The growth of human immunodeficiency virus (HIV) sequence databases resulting from drug resistance testing has motivated efforts using phylogenetic methods to assess how HIV spreads. Such inference is potentially both powerful and useful for tracking the epidemiology of HIV and the allocation of resources to prevention campaigns. We recently used simulation and a small number of illustrative cases to show that certain phylogenetic patterns are associated with different types of epidemiological linkage. Our original approach was later generalized for large next-generation sequencing datasets and implemented as a free computational pipeline. Previous work has claimed that direction and directness of transmission could not be established from phylogeny because one could not be sure that there were no intervening or missing links involved. We address this issue by investigating phylogenetic patterns from 272 previously identified HIV transmission chains with 955 transmission pairs representing diverse geography, risk groups, subtypes, and genomic regions. These HIV transmissions had known linkage based on epidemiological information such as partner studies, mother-to-child transmission, pairs identified by contact tracing, and criminal cases. Here, we show that the resulting phylogeny inferred from real HIV genetic sequences indeed reveals distinct patterns associated with direct transmission contra transmissions from a common source. Thus, our results establish how to interpret phylogenetic trees based on HIV sequences when tracking who-infected-whom, when and how genetic information can be used for improved tracking of HIV spread. We also investigate limitations that stem from limited sampling and genetic time-trends in the donor and recipient HIV populations.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Leaving in the lead: Priorities for perovskite photovoltaics

The need for moving electricity generation to a sustainable model requires the development of low cost ubiquitous photovoltaics (PVs) to harvest the planet’s primary energy source, the Sun. Building upon the successes of Si-based and CdTe-based PV technologies, PVs with lower-embodied energy and requiring lower carbon dioxide equivalent to produce will be required to meet long-term sustainability goals. In particular, thin-film technologies, such as high-efficiency metal halide perovskite (MHP) PV modules, provide avenues to reduced embodied energy, lower energy payback times, and enabling energy-dense tandems [H. M. Wikoff et al., Joule 6(7), 1710–1725 (2022) and V. Fthenakis, Renewable Sustainable Energy Rev. 13(9), 2746–2750 (2009)]. The ability to improve efficiency and lower energy payback time of next generation thin-film PV modules is a critical foundation for green H2 and electrification more broadly. In this regard, Pb-based MHP-PVs have separated themselves as a result of the high-efficiencies that can be realized across a range of electronic gaps. Questions regarding Pb-based MHP-PVs that are often asked, as the challenges of efficiency and reliability are met, revolve around the “problem” of the Pb content. Specifically, “does Pb toxicity preclude MHP-PV modules from being deployed at the TW scale?” To provide this sense of scale, in 2021, the United States burned 10.5 quads of coal, with 90% of that used for electricity generation. Given the energy content of coal of 29 MJ/kg and a residual lead content in that coal of 30 mg/kg, electricity generation from coal resulted in more lead emitted into the atmosphere than what would be required to produce over 2 TW of MHP-PV name plate capacity (assuming a 20% module efficiency and an ∼700 nm active layer). This amounts to more PV power than has been deployed across all PV technologies and geographies to date. This only includes US coal consumption; the rest of the world would be much larger. This example illustrates the scale of the material usage relative to the energy production. Imagine a power-generation technology that offsets these Pb emissions from coal and essentially sequesters this Pb content between two sheets of impermeable glass. Why should we let Pb’s history of misuse prevent it from being included in next generation PV modules that can enable a sustainable energy future?

14 SOLAR ENERGY↗
DOE OSTI2016

On the universal structure of human lexical semantics

How universal is human conceptual structure? The way concepts are organized in the human brain may reflect distinct features of cultural, historical, and environmental background in addition to properties universal to human cognition. Semantics, or meaning expressed through language, provides indirect access to the underlying conceptual structure, but meaning is notoriously difficult to measure, let alone parameterize. Here, we provide an empirical measure of semantic proximity between concepts using cross-linguistic dictionaries to translate words to and from languages carefully selected to be representative of worldwide diversity. These translations reveal cases where a particular language uses a single “polysemous” word to express multiple concepts that another language represents using distinct words. We use the frequency of such polysemies linking two concepts as a measure of their semantic proximity and represent the pattern of these linkages by a weighted network. This network is highly structured: Certain concepts are far more prone to polysemy than others, and naturally interpretable clusters of closely related concepts emerge. Statistical analysis of the polysemies observed in a subset of the basic vocabulary shows that these structural properties are consistent across different language groups, and largely independent of geography, environment, and the presence or absence of a literary tradition. As a result, the methods developed here can be applied to any semantic domain to reveal the extent to which its conceptual structure is, similarly, a universal attribute of human cognition and language use.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

A coupled human–natural system analysis of freshwater security under climate and population change

Limited water availability, population growth, and climate change have resulted in freshwater crises in many countries. Jordan’s situation is emblematic, compounded by conflict-induced population shocks. Integrating knowledge across hydrology, climatology, agriculture, political science, geography, and economics, we present the Jordan Water Model, a nationwide coupled human–natural-engineered systems model that is used to evaluate Jordan’s freshwater security under climate and socioeconomic changes. The complex systems model simulates the trajectory of Jordan’s water system, representing dynamic interactions between a hierarchy of actors and the natural and engineered water environment. A multiagent modeling approach enables the quantification of impacts at the level of thousands of representative agents across sectors, allowing for the evaluation of both systemwide and distributional outcomes translated into a suite of water-security metrics (vulnerability, equity, shortage duration, and economic well-being). Model results indicate severe, potentially destabilizing, declines in freshwater security. Per capita water availability decreases by approximately 50% by the end of the century. Without intervening measures, >90% of the low-income household population experiences critical insecurity by the end of the century, receiving <40 L per capita per day. Widening disparity in freshwater use, lengthening shortage durations, and declining economic welfare are prevalent across narratives. To gain a foothold on its freshwater future, Jordan must enact a sweeping portfolio of ambitious interventions that include large-scale desalinization and comprehensive water sector reform, with model results revealing exponential improvements in water security through the coordination of supply- and demand-side measures.

42 ENGINEERING↗
DOE OSTI2024

Extreme elevational migration spurred cryptic speciation in giant hummingbirds

The ecoevolutionary drivers of species niche expansion or contraction are critical for biodiversity but challenging to infer. Niche expansion may be promoted by local adaptation or constrained by physiological performance trade-offs. For birds, evolutionary shifts in migratory behavior permit the broadening of the climatic niche by expansion into varied, seasonal environments. Broader niches can be short-lived if diversifying selection and geography promote speciation and niche subdivision across climatic gradients. To illuminate niche breadth dynamics, we can ask how “outlier” species defy constraints. Of the 363 hummingbird species, the giant hummingbird (Patagona gigas) has the broadest climatic niche by a large margin. To test the roles of migratory behavior, performance trade-offs, and genetic structure in maintaining its exceptional niche breadth, we studied its movements, respiratory traits, and population genomics. Satellite and light-level geolocator tracks revealed an >8,300-km loop migration over the Central Andean Plateau. This migration included a 3-wk, ~4,100-m ascent punctuated by upward bursts and pauses, resembling the acclimatization routines of human mountain climbers, and accompanied by surging blood-hemoglobin concentrations. Extreme migration was accompanied by deep genomic divergence from high-elevation resident populations, with decisive postzygotic barriers to gene flow. The two forms occur side-by-side but differ almost imperceptibly in size, plumage, and respiratory traits. The high-elevation resident taxon is the world’s largest hummingbird, a previously undiscovered species that we describe and name here. The giant hummingbirds demonstrate evolutionary limits on niche breadth: when the ancestral niche expanded due to evolution (or loss) of an extreme migratory behavior, speciation followed.

Science & Technology - Other Topics↗
DOE OSTI2025

Population structure limits the use of genomic data for predicting phenotypes and managing genetic resources in forest trees

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.

Science & Technology - Other Topics↗
DOE OSTI2019

Toward adaptive decision support for assessing infrastructure system resilience using hidden performance measures

The understanding of resilience is an emerging topic within the study of risks affecting distributed transportation infrastructure systems. Although recent studies have explored the quantification of system resilience, there has been limited research aimed at understanding the role of multiple performance measures, geographies, and modeling uncertainties within the assessment of resilience and associated decision-making. Under real-world conditions, there is an increased burden on analysts for translating observed system data (from human and electronic sensor observations) into the estimation of system performance that may not be directly observable. This paper addresses these issues using a scenario-based risk modeling approach to understand: 1) resilience of complex systems, often in cases of hidden (not readily observable) measures of performance, 2) resilience sensitivity to modeling uncertainties in event and system characteristics, and 3) resilience sensitivity to the measurement of performance across multiple dimensions. The methods in this paper integrate uncertainty-driven risk and probabilistic modeling within a multi-state Markov approach. This study contributes to the state-of-the-art by developing methodologies for assessing community perceptions of infrastructure system resilience using observable factors and inferring possibly hidden performance measures for facilitating adaptive decision-support. The methods are demonstrated with hypothetical spatiotemporal data for the Washington, District of Columbia and Baltimore, Maryland urban regions in the United States against evacuation-causing and shelter-in-place disruptive scenarios across multiple system performance dimensions. The analysis results may be useful for risk managers, supply chain analysts, emergency decision-makers, and the general public.

Infrastructure System Resilience, Risk Management,↗
DOE OSTI2020

Applying the index of watershed integrity to the Matanuska–Susitna basin

The Matanuska-Susitna Borough is the fastest growing region in the State of Alaska and is impacted by a number of human activities. We conducted a multiscale assessment of the stressors facing the borough by developing and mapping the Index of Watershed Integrity (IWI) and Index of Catchment Integrity (the latter considers stressors in areas surrounding individual stream segments exclusive of upstream areas). The assessment coincided with the borough’s stormwater management planning. We adapted the list of anthropogenic stressors used in the original conterminous United States IWI application to reflect the borough’s geography, human activity, and data availability. This analysis also represents an early application of the NHDPlus High Resolution geospatial framework and the first use of the framework in an IWI study. We also explored how remediation of one important stressor, culverts, could impact watershed integrity at the catchment and watershed scales. Overall, we found that the integrity scores for the Matanuska– Susitna basin were high compared to the conterminous United States. Low integrity scores did occur in the rapidly developing Wasilla–Palmer core area. We also found that culvert remediation had a larger proportional impact in catchments with fewer stressors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

Supervised classification of electric power transmission line nominal voltage from high-resolution aerial imagery

For many researchers, government agencies, and emergency responders, access to the geospatial data of US electric power infrastructure is invaluable for analysis, planning, and disaster recovery. Historically, however, access to high quality geospatial energy data has been limited to few agencies because of commercial licenses restrictions, and those resources which are widely accessible have been of poor quality, particularly with respect to reliability. Recent efforts to develop a highly reliable and publicly accessible alternative to the existing datasets were met with numerous challenges – not the least of which was filling the gaps in power transmission line voltage ratings. To address the line voltage rating problem, we developed and tested a basic methodology that fuses knowledge and techniques from power systems, geography, and machine learning domains. Specifically, we identified predictors of nominal voltage that could be extracted from aerial imagery and developed a tree-based classifier to classify nominal line voltage ratings. Overall, we found that line support height, support span, and conductor spacing are the best predictors of voltage ratings, and that the classifier built with these predictors had a reliable predictive accuracy (that is, within one voltage class for four out of the five classes sampled). In conclusion, we applied our approach to a study area in Minnesota.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

The CanBikeCO Full Pilot: Long-Term Results and Analysis From an E-Bike Program in Colorado, USA

Personal micromobility devices like bicycles, e-bikes, and scooters are low- or zero-energy alternatives to single-occupancy vehicles. However, a lack of data has led to a dearth of data-driven research on personally owned e-bike usage. We present longitudinal findings from the CanBikeCO program, focused on e-bike adoption and use across demographics, trip characteristics, and geographies in the state of Colorado. CanBikeCO recorded travel survey data from low-income individuals provided with personal e-bikes by the Colorado Energy Office in six communities across Colorado from July 2021 to December 2022. The data were collected using a custom instance of the National Renewable Energy Laboratory OpenPATH platform, which combines passive data collection with semantic information such as trip mode and purpose labels. To our knowledge, there are no prior travel survey data on personally owned e-bikes with this range and scope. Insights from this unique dataset include: (i) work trips were 17% more likely than average trips to be taken on an e-bike, (ii) e-bikes were most often reported to replace cars (34% of e-bike trips) and other personal micromobility devices (22%), and (iii) participants favored walking for trips less than 1 mile, e-bikes for trips of 1-3 miles, and e-bikes, cars, or shared rides for trips of 3-20 miles. The data used to generate these results have been made available in the Transportation Secure Data Center. We find e-bike use is appealing across age groups and may be related to characteristics of land use, urban form, occupation, income, and car ownership. We conclude for this population that the energy demand added by e-bike use (induced demand and replacing non-motorized modes) is outweighed by the reduction in energy demand from replacement of single-occupancy vehicle trips with e-bike trips. Our findings suggest considerable potential for energy savings from personal e-bike ownership.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

SPATIALLY RESOLVED SPECTROSCOPY OF EUROPA: THE DISTINCT SPECTRUM OF LARGE-SCALE CHAOS

We present a comprehensive analysis of spatially resolved moderate spectral resolution near-infrared spectra obtained with the adaptive optics system at the Keck Observatory. We identify three compositionally distinct end member regions: the trailing hemisphere bullseye, the leading hemisphere upper latitudes, and a third component associated with leading hemisphere chaos units. We interpret the composition of the three end member regions to be dominated by irradiation products, water ice, and evaporite deposits or salt brines, respectively. The third component is associated with geological features and distinct from the geography of irradiation, suggesting an endogenous identity. Identifying the endogenous composition is of particular interest for revealing the subsurface composition. However, its spectrum is not consistent with linear mixtures of the salt minerals previously considered relevant to Europa. The spectrum of this component is distinguished by distorted hydration features rather than distinct spectral features, indicating hydrated minerals but making unique identification difficult. In particular, it lacks features common to hydrated sulfate minerals, challenging the traditional view of an endogenous salty component dominated by Mg-sulfates. Chloride evaporite deposits are one possible alternative.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2026

The biogeography of soil and airborne fungi in the Southwestern USA in relation to climate and vegetation

To assess how fungal dispersal might respond to climate change, we examined how climate and geography influence the regional distribution of fungi in soil and air. Specifically, we hypothesized that neighboring fungal communities should be more similar than distant communities (i.e. spatially autocorrelated) and that fungal dispersal should be more limited in soil than in air. We collected soil and air samples from 60 sites across five states in the Southwestern USA. Then, we sequenced the ITS2 region to identify fungal taxa in each sample. Next, we used distance-based redundancy analysis to partition variation in fungal community composition between climate variables and spatial structure. Fungi were indeed spatially autocorrelated. Moreover, precipitation, maximum vapor pressure deficit, and soil moisture were significantly related to fungal community composition in soils. In comparison, only precipitation was significantly related to community composition in the air. After accounting for climate, the strength of spatial autocorrelation did not differ significantly in soilborne versus airborne fungi. Dispersal limitation was evident in soilborne fungi at short distances (<100 km) and was not observed at any distance in airborne fungi. Altogether, climate may influence which fungal taxa are present in soil and air, and fungi could feasibly wind disperse over regional scales.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Co-location Pattern Mining of Geosocial Data to Characterize Urban Functional Spaces

Spatial Co-location Pattern (SCP) mining continues to play a critical role in understanding the morphology of urban functional spaces of world cities. It requires a large amount of fine-granular data and computing efficiency to handle the combinatorial explosion of co-location patterns. To this end, this work has two main contributions - i) We showcase a novel approach to perform SCP mining to characterize intra-city scale structure of urban functionality or co-located activity patterns using geosocial Points-of-Interest (POI) vector data. ii) We present a generalized and optimized parallel/distributed SCP mining algorithm implemented on a Hadoop MapReduce system and demonstrate the utility of our approach using the city of Berlin (Germany) as an example. The SCPs tend to vary across Berlin's municipal boroughs and at different spatial scales. Our findings on Berlin's functional structure conform to existing urban geography models. Such a data-driven exploration of massive urban POIs using distributed computing is first of its kind and can help better understand the changing dynamics of urban functionality, as well as physical, and social network structure around the world.

Masrur, Arif↗
DOE OSTI2025

HexWeather: Hexagonal Spatial Data Aggregation for Weather-Driven Grid Resilience Analysis

Extreme weather accounts for over 8 0 % of major U.S. power outages since 2000, highlighting the need for spatial tools that align weather data with the irregular boundaries of electric infrastructure. This paper introduces HexWeather, a modular, resolution-aware framework for aggregating historical and forecasted weather data using Uber's H3 hexagonal spatial indexing system. Unlike traditional methods that rely on state or county-level grids, HexWeather enables weather analysis across custom geographies such as utility service areas where public datasets are often unavailable or misaligned. Using Open-Meteo data, we evaluate how H3 resolution affects anomaly detection, spatial variability, and forecast uncertainty across three scales: state, county, and utility. Results show that while coarse resolutions suffice for broad trend tracking, finer resolutions are essential for identifying localized variability and operational risks. By applying metrics like Z-score standard deviation and interquartile range, HexWeather quantifies the spatial spread of both historical anomalies and forecasted conditions, allowing users to assess resolution adequacy for each analysis. This framework supports rapid weather data reuse, reproducible anomaly detection, and predictive modeling for infrastructure resilience. By bridging spatial misalignment in traditional datasets and enabling retrospective and forward-looking analysis within the same pipeline, HexWeather lays the groundwork for better post event analysis, outage prediction, and resilience planning.

Morris, Jacob [ORNL]↗
DOE OSTI2018

Local Variability in PV Soiling Rate

We examine the correlation between monthly PV system soiling rates over a multi-year period within two groups of First Solar soiling stations in separate regions of California as a function of site separation. The results demonstrate large seasonal variations in monthly soiling rates, with seasonal patterns that differ between sites. Monthly patterns at nearby sites were more highly correlated, suggesting that soiling rates can be strongly affected by local geography and weather. For the two regions studied, soiling rate pattern correlation diminished beyond a distance on the order of 50 km.

14 SOLAR ENERGY↗
DOE OSTI2019

Biomass yield in a genetically diverse Miscanthus sinensis germplasm panel evaluated at five locations revealed individuals with exceptional potential

Abstract To breed improved biomass cultivars of Miscanthus ×giganteus, it will be necessary to select the highest‐yielding and best‐adapted genotypes of its parental species, Miscanthus sinensis and Miscanthus sacchariflorus . We phenotyped a diverse clonally propagated panel of 569 M. sinensis and nine natural diploid M . × giganteus at one subtropical (Zhuji, China) and five temperate locations (Sapporo, Japan; Leamington, Ontario, Canada; Fort Collins, CO; Urbana, IL; and Chuncheon, Korea) for dry biomass yield and 14 yield‐component traits, in trials grown for 3 years. Notably, dry biomass yield of four Miscanthus accessions exceeded 80 Mg/ha in Zhuji, China, approaching the highest observed for any land plant. Additionally, six M. sinensis in Sapporo, Japan and one in Leamington, Canada also yielded more than the triploid M . × giganteus ‘1993‐1780’ control, with values exceeding 20 Mg/ha. Diploid M . × giganteus was the best‐yielding group at the northern sites. Genotype‐by‐environment interactions were modest among the five northern trial sites but large between Zhuji, and the northern sites. M. sinensis accessions typically yielded best at trial sites with latitudes similar to collection sites, although broad adaptation was observed for accessions from southern Japan. Genotypic heritabilities for third year yields ranged from 0.71 to 0.88 within locations. Compressed circumference was the best predictor of yield. These results establish a baseline of data for initiating selection to improve biomass yield of M. sinensis and M . × giganteus in a diverse set of relevant geographies.

09 BIOMASS FUELS↗