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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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Time-series, PV performance field data from 2019-2020 for the bifacial tracking research field at NREL. Dataset also contains meterological data for site.
Albedo data are essential for accurate prediction of bifacial PV module performance. However, spectral response mismatch between PV modules and irradiance sensors used in albedometers can limit the accuracy of performance predictions. In order to provide quantitative assessment of this effect, we investigate via simulation the differences in spectrally responsive albedo measured with thermopile pyranometers and crystallinesilicon PV reference cells in comparison to a representative crystalline-silicon bifacial PV module for nine different representative ground surface materials. Calculations are performed using simulated solar spectra together with catalogued spectral reflectivity data distributed with the SMARTS simulation software. For the specific materials considered, the results show that albedo measurement using thermopile pyranometers could over or under-estimate the ground-reflected radiation usable by a bifacial PV module by up to 10%, versus only approximately 4% total range of variation for a PV reference cell.
This work presents one year results for a side-by-side comparison of 5 different bifacial technologies, deployed in a 75-kW single-axis tracked field at Golden, CO. Four PERC (multi and mono) and 1 Silicon Heterojunction manufacturers are represented. Reference strings of equivalent monofacial PV modules are also installed for a direct technology comparison and calculation of bifacial gain. Analysis indicates performance within expectation, with a cumulative model mean error within +/- 2% for both bifacial and monofacial models and cumulative bifacial energy gain between 6-9%. High accuracy string-level DC monitoring and module-level measurements are also presented, showing effects of snow and edge effects in the order of +/- 3% for non-uniform irradiance across the row. Data from a custom-module to measure shading loss from the torque-tube is installed, showing up to 6% irradiance non-uniformity for the month of January.
The performance of bifacial PV systems depends greatly on the installed conditions. Previous simulations and results have shown very high bifacial gain improvement, but this may not be the case for all conditions, particularly for large-scale systems with self-shading, lower-cost PV modules (PERC) which might have lower bifaciality coefficient, and field deployments over natural ground cover. But not to worry! Financial models indicate that even with these lower performance conditions, and with bifacial gain of 4%-7%, bifacial modules can still provide improved LCOE.
Despite significant interest toward solid-state electrolytes owing to their superior safety in comparison to liquid-based electrolytes, sluggish ion diffusion and high interfacial resistance limit their application in durable and high-power density batteries. Here, a novel quasi-solid Li + ion conductive nanocomposite polymer electrolyte containing black phosphorous (BP) nanosheets is reported. The developed electrolyte is successfully cycled against Li metal (over 550 h cycling) at 1 mA cm -2 at room temperature. The cycling overpotential is dropped by 75% in comparison to BP-free polymer composite electrolyte indicating lower interfacial resistance at the electrode/electrolyte interfaces. Molecular dynamics simulations reveal that the coordination number of Li + ions around (trifluoromethanesulfonyl)imide (TFSI - ) pairs and ethylene-oxide chains decreases at the Li metal/electrolyte interface, which facilitates the Li + transport through the polymer host. Here, density functional theory calculations confirm that the adsorption of the LiTFSI molecules at the BP surface leads to the weakening of N and Li atomic bonding and enhances the dissociation of Li + ions. This work offers a new potential mechanism to tune the bulk and interfacial ionic conductivity of solid-state electrolytes that may lead to a new generation of lithium polymer batteries with high ionic conduction kinetics and stable long-life cycling.
Abstract Photodetectors operating across the short‐, mid‐, and long‐wave infrared (SWIR–LWIR, λ = 1–14 µm) underpin modern science, technology, and society in profound ways. Narrow bandgap semiconductors that form the basis for these devices require complex manufacturing, high costs, cooling, and lack compatibility with silicon electronics, attributes that remain prohibitive for their widespread usage and the development of emerging technologies. Here, a photoconductive detector, fabricated using a solution‐processed narrow bandgap conjugated polymer is demonstrated that enables charge carrier generation in the infrared and ultrasensitive SWIR–LWIR photodetection at room temperature. Devices demonstrate an ultralow electronic noise that enables outstanding performance from a simple, monolithic device enabling a high detectivity ( D *, the figure of merit for detector sensitivity) >2.44 × 10 9 Jones (cm Hz 1/2 W −1 ) using the ultralow flux of a blackbody that mirrors the background emission of objects. These attributes, ease of fabrication, low dark current characteristics, and highly sensitive operation overcome major limitations inherent within modern narrow–bandgap semiconductors, demonstrate practical utility, and suggest that uncooled detectivities superior to many inorganic devices can be achieved at high operating temperatures.
In this work, the electroreduction of carbon dioxide (CO 2 ) to oxalate is enabled by incorporating trace metallic lead (Pb) on carbon‐based supports (CBS) with polymer overlayers. These composite materials serve as an efficient electrocatalytic system for the facile conversion and storage of CO 2 , a pernicious atmospheric pollutant. Results from controlled potential electrolysis experiments indicate that 1) trace metallic Pb on the ppb scale is active toward the reductive coupling of CO 2 to oxalate at comparable Faradaic efficiencies to bulk metallic Pb and 2) polymer encapsulation of this trace metallic Pb leads to promotion of CO 2 reduction (CO 2 R) selectively to metal oxalates over other products such as CO. Importantly, metal oxalates are important alternative cementitious materials and precursors for other materials’ synthesis applications. The solid products undergo rigorous spectroscopic characterization, including 13 CO 2 labeling experiments, to ensure the metal oxalates are in fact produced from CO 2 R. These findings serve as a model for leveraging microenvironment effects to enhance activity and selectivity for CO 2 R using trace‐metal catalysts for carbon utilization and storage technologies.
Sodium layered oxides Na x MO 2 (x ≤ 1 and M = transition metal ions) have gained significant interest as sodium-ion battery (NIB) cathodes owing to their high operating voltages and potential for higher energy density compared with polyanion and Prussian blue–type cathodes. However, their practical applications are often hindered by the irreversible structural transitions leading to capacity fading during cycling. The nature and substitution of transition metal ions define the material properties and electrochemical performance. In this study, through comprehensive electrochemical characterization combined with multi-scale structural and spectroscopical analyses, we demonstrate the synergistic impacts of Lithium and Titanium doping, which not only increases overall capacity by boosting cation and anion cooperative redox contributions but also improves the rate capability and cycling stability. Specifically, Li + doping enhances the available sodium inventory for extraction, while Ti 4+ disrupts Na + /vacancy ordering at lower voltages (< 4 V) and mitigates the detrimental P2→OP4/O2 phase transition during cycling. The combined effect of Lithium and Titanium doping promotes more charge localization on Oxygen, which activates reversible lattice oxygen redox reactions at elevated voltages, contributing additional capacity beyond conventional cationic redox. This work provides crucial insights into the design of high-performance, high-capacity P2-type layered cathode materials for sodium-ion batteries.
Abstract With the growing amount of renewable energy sources, the grid has become responsible for accounting for intermittency and the flexibility needed to utilize dynamic sources. Expensive peaking plants and energy storage systems have been proposed as ways to mitigate those problems. There is a large group of energy consumers that can respond to grid conditions. Historically, these consumers have been residential and commercial users, but with modern innovations and practices, industrial consumers have the potential to become a major player in this space. Grid‐responsive smart manufacturing can be used to utilize modern tools in manufacturing innovation as enablers for grid response. These modern tools already exist but are not widely used for industrial grid‐side energy management. This article defines grid‐responsive smart manufacturing, identifies five major barriers to its widespread implementation, and portrays the path to getting industrial users to be key players in grid stability and flexibility.
For occupational medical screening programs focused on long-term health surveillance, participant attrition is a significant barrier to success. Here we investigate demographic, medical history, and clinical data from National Supplemental Screening Program (NSSP) examinees for association with likelihood of return for a second exam (rescreening). A total of 15,733 individuals completed at least one NSSP exam before December 31, 2016; of those, 4832 also completed a second exam on or before December 31, 2019. Stepwise logistic regression models were used to identify variables associated with whether a participant was rescreened in the NSSP. Individuals were less likely to return for rescreening if they had a history of any cancer; cardiovascular problems; diabetes or kidney disease; or if they used insulin. Age at time of first exam and job site category significantly influenced likelihood of return. Workers categorized as “guests” were more likely to return. Participants were less likely to return if they had an abnormal urinalysis, abnormal pulmonary function, pneumoconiosis, aortic atherosclerosis, or hearing loss at their initial exam. Participants who received a chest X-ray at their initial screening were more likely to return. The presence of health problems is strongly linked to screening program attrition. Participants who are older at the time of their initial screening exam are less likely to return. The discovery of several strong demographic, medical, and job associations reveals the importance for medical screening programs to understand and address factors that influence participant retention and, consequently, the effectiveness of long-term health surveillance activities.
Soil organic carbon (SOC) regulates terrestrial ecosystem functioning, provides diverse energy sources for soil microorganisms, governs soil structure, and regulates the availability of organically bound nutrients. Investigators in increasingly diverse disciplines recognize how quantifying SOC attributes can provide insight about ecological states and processes. Today, multiple research networks collect and provide SOC data, and robust, new technologies are available for managing, sharing, and analyzing large data sets. Here, we advocate that the scientific community capitalize on these developments to augment SOC data sets via standardized protocols. We describe why such efforts are important and the breadth of disciplines for which it will be helpful, and outline a tiered approach for standardized sampling of SOC and ancillary variables that ranges from simple to more complex. We target scientists ranging from those with little to no background in soil science to those with more soil-related expertise, and offer examples of the ways in which the resulting data can be organized, shared, and discoverable.
Ants play multiple roles in ecosystems, but their ability to affect decomposition processes in temperate grasslands is relatively unknown. We investigated whether the suppression of ant populations influenced litter decomposition in grasslands via predation of some decomposers (e.g., mites and springtails) and/or microbial activity and composition. We performed two successful ant suppression treatments (seven weeks, 37% suppression, year 1, 10 weeks, 70% suppression, year 2) over the course of a 59-week experiment. We then assayed the effects of ant suppression using coarse- and fine-mesh litterbags and evaluated litter chemistry, microbial and arthropod communities, and microbial enzyme activity. Ant suppression efforts reduced ant abundance and altered ant, arthropod decomposer, and non-ant predator community composition. However, ant suppression did not affect decomposer arthropod abundance, litter mass loss, microbial composition, or enzyme activity in litterbags. Litterbag mesh size did not alter microbial composition, perhaps due to a failure to exclude decomposers, as mites and springtails were more or equally abundant in fine-mesh bags. Nevertheless, mesh size did change litter chemistry, suggesting that mesh size-mediated microenvironments affect decomposition environment regardless of invertebrate exclusion. Coarse-mesh litterbags had higher concentrations of microbial sugars, lignin, and N and lower concentrations of litter C and crystalline cellulose than fine-mesh litterbags. Litterbag mesh size may alter decomposition processes irrespective of invertebrate abundance. We found no evidence that ant predation was an important driver of decomposer populations or decomposition in these systems, and we suspect redundancy at the top of the detrital food web dilutes the role of ants.
In this study, isotopic information from 81 snowpits was collected over a 5-year period in a large, Colorado watershed. Data spans gradients in elevation, aspect, vegetation, and seasonal climate. They are combined with overlapping campaigns for water isotopes in precipitation and snowmelt, and a land-surface model for detailed estimates of snowfall and climate at sample locations. Snowfall isotopic inputs, describe the majority of δ18O snowpack variability. Aspect is a secondary control, with slightly more enriched conditions on east and north facing slopes. This is attributed to preservation of seasonally enriched snowfall and vapour loss in the early winter. Sublimation, expressed by decreases in snowpack d-excess in comparison to snowfall contributions, increases at low elevation and when seasonal temperature and solar radiation are high. At peak snow accumulation, post-depositional fractionation appears to occur in the top 25 ± 14% of the snowpack due to melt-freeze redistribution of lighter isotopes deeper into the snowpack and vapour loss to the atmosphere during intermittent periods of low relative humidity and high windspeed. Relative depth of fractionation increases when winter daytime temperatures are high and winter precipitation is low. Once isothermal, snowpack isotopic homogenization and enrichment was observed with initial snowmelt isotopically depleted in comparison to snowpack and enriching over time. The rate of δ18O increase (d-excess decrease) in snowmelt was 0.02‰ per day per 100-m elevation loss. Isotopic data suggests elevation dictates snowpack and snowmelt evolution by controlling early snow persistence (or absence), isotopic lapse rates in precipitation and the ratio of energy to snow availability. Hydrologic tracer studies using stable water isotopes in basins of large topographic relief will require adjustment for these elevational controls to properly constrain stream water sourcing from snowmelt.
The Photovoltaic (PV) Performance Modeling Collaborative (PVPMC) organized a blind PV performance modeling intercomparison to allow PV modelers to blindly test their models and modeling ability against real system data. Measured weather and irradiance data were provided along with detailed descriptions of PV systems from two locations (Albuquerque, New Mexico, USA, and Roskilde, Denmark). Participants were asked to simulate the plane-of-array irradiance, module temperature, and DC power output from six systems and submit their results to Sandia for processing. The results showed overall median mean bias (i.e., the average error per participant) of 0.6% in annual irradiation and –3.3% in annual energy yield. While most PV performance modeling results seem to exhibit higher precision and accuracy as compared to an earlier blind PV modeling study in 2010, human errors, modeling skills, and derates were found to still cause significant errors in the estimates.
Critical Assessment of Structure Prediction (CASP) is an organization aimed at advancing the state of the art in computing protein structure from sequence. In the spring of 2020, CASP launched a community project to compute the structures of the most structurally challenging proteins coded for in the SARS-CoV-2 genome. Forty-seven research groups submitted over 3000 three-dimensional models and 700 sets of accuracy estimates on 10 proteins. The resulting models were released to the public. CASP community members also worked together to provide estimates of local and global accuracy and identify structure-based domain boundaries for some proteins. Subsequently, two of these structures (ORF3a and ORF8) have been solved experimentally, allowing assessment of both model quality and the accuracy estimates. Models from the AlphaFold2 group were found to have good agreement with the experimental structures, with main chain GDT_TS accuracy scores ranging from 63 (a correct topology) to 87 (competitive with experiment).
We present a novel self-assembled monolayer (SAM)-based technique to make nanopinhole-enabled passivated contacts on silicon solar cells by tuning the SAM coverage area and etch selectivity. We deposit trimethyl-silyl Si(CH 3 ) 3 groups using hexamethyldisilazane (HMDS) as the precursor over passivating dielectric layers and their stacks (SiO 2 , SiN x , SiO 2 /SiN x ) and interrupt the HMDS attachment chemistry shortly before a full monolayer is formed on its surface. Subsequent etching in dilute HF produces pinholes through the dielectric layers due to the higher etch resistance of the SAM to HF etching. The pinhole areal density (10 4 –10 8 /cm 2 ) and size (10–1000 nm) can be tuned both by duration of HMDS attachment and HF etch time. Pinholes were characterized by atomic force microscopy, tetramethylammonium hydroxide (TMAH) selective etch, and Ag decoration by electroless plating. Polysilicon (poly-Si) passivated contacts enabled by pinholes were formed by subsequent deposition of doped amorphous silicon (a-Si:H) followed by thermal crystallization and dopant drive-in. At optimal areal pinhole density ≈10 7 /cm 2 , contacts exhibit both passivation and carrier transport via pinholes as evidenced by electron beam induced current, transmission line measurements, and carrier lifetime measurements. Solar cells based with these pinhole contacts show V oc = 723 mV and FF = 80.3%. The remaining SAM layer does not affect device performance.
Performance of data reduction workflows at the High Flux Isotope Reactor (HFIR) and the Spallation Neutron Source (SNS) at Oak Ridge National Laboratory (ORNL) is mainly determined by the time spent loading raw measurement events stored in large and sparse datasets. This paper describes: (1) our long-term view to leverage SNS and HFIR data management needs with our experience at ORNL’s world-class high performance computing (HPC) facilities, and (2) our short-term efforts to speed up current workflows using Mantid, a data analysis and reduction community framework used across several neutron scattering facilities. We show that minimally invasive short-term improvements in metadata management have a moderate impact in speeding up current production workflows. We propose a more disruptive domain-specific solution: the No Cost Input Output (NCIO) framework, we provide an overview, the risks and challenges in NCIO’s adoption by HFIR and SNS stakeholders.
Asynchronous evolutionary algorithms are becoming increasingly popular as a means of making full use of many processors while solving computationally expensive search and optimization problems. These algorithms excel at keeping large clusters fully utilized, but may sometimes inefficiently sample an excess of fast-evaluating solutions at the expense of higher-quality, slow-evaluating ones. We introduce a steady-state parent selection strategy, SWEET (“Selection whilE EvaluaTing”), that sometimes selects individuals that are still being evaluated and allows them to reproduce early. This gives slow-evaluating individuals that have higher fitnesses an increased ability to multiply in the population. We find that SWEET appears effective in simulated take-over time analysis, but that its benefit is confined mostly to early in the run, and our preliminary study on an autonomous vehicle controller problem that involves tuning a spiking neural network proves inconclusive.