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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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Improved Primary Reference Cell Calibrations for Higher Accuracy Photovoltaic Cell and Module Performance Measurements

The adoption of photovoltaic (PV) modules for clean electricity relies on accurate measurements of their performance, which are essential for estimating their energy production potential. Herein, the calibration chain of PV cells and modules, with particular emphasis on primary reference cell calibrations, is discussed. Also, herein, the direct sunlight method the group has developed for these calibrations is presented and critical improvements and upgrades that lead to calibration uncertainty as low as 0.45% are discussed. The ultimate motivation behind this work is to provide low‐uncertainty performance measurements of PV modules, and lowering the calibration uncertainty of primary reference cells is a key first step toward achieving this goal. As the use of solar electricity continues to grow, the demand for primary reference cell calibrations inevitably increases beyond what the small handful of primary calibration laboratories can provide today. Therefore, this work can serve as a useful guide for implementing primary PV reference cell calibrations using the outdoor method, as well as outlining the critical elements required to make these calibrations highly accurate.

Osterwald, Carl R.↗
DOE OSTI2023

cTULIP: application of a human-based RNA-seq primary tumor classification tool for cross-species primary tumor classification in canine

The domestic dog, Canis familiaris, is quickly gaining traction as an advantageous model for use in the study of cancer, one of the leading causes of death worldwide. Naturally occurring canine cancers share clinical, histological, and molecular characteristics with the corresponding human diseases. In this study, we take a deep-learning approach to test how similar the gene expression profile of canine glioma and bladder cancer (BLCA) tumors are to the corresponding human tumors. We likewise develop a tool for identifying misclassified or outlier samples in large canine oncological datasets, analogous to that which was developed for human datasets. We test a number of machine learning algorithms and found that a convolutional neural network outperformed logistic regression and random forest approaches. We use a recently developed RNA-seq-based convolutional neural network, TULIP, to test the robustness of a human-data-trained primary tumor classification tool on cross-species primary tumor prediction. Our study ultimately highlights the molecular similarities between canine and human BLCA and glioma tumors, showing that protein-coding one-to-one homologs shared between humans and canines, are sufficient to distinguish between BLCA and gliomas. The results of this study indicate that using protein-coding one-to-one homologs as the features in the input layer of TULIP performs good primary tumor prediction in both humans and canines. Furthermore, our analysis shows that our selected features also contain the majority of features with known clinical relevance in BLCA and gliomas. Our success in using a human-data-trained model for cross-species primary tumor prediction also sheds light on the conservation of oncological pathways in humans and canines, further underscoring the importance of the canine model system in the study of human disease.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Neutron Spin–Echo Studies of the Structural Relaxation of Network Liquid ZnCl 2 at the Structure Factor Primary Peak and Prepeak

Using neutron spin-echo spectroscopy, we studied the microscopic structural relaxation of a prototypical network ionic liquid ZnCl 2 at the structure factor primary peak and pre-peak. The results show that the relaxation at the primary peak is faster than the pre-peak and the activation energy is ≈33% higher. Stretched exponential relaxation is observed even at temperatures well above the melting point T m . Surprisingly, the stretching exponent shows a rapid increase upon cooling, especially at the primary peak, where it changes from stretched exponential to simple exponential on approaching T m . Furthermore, these results suggest that the appearance of glassy dynamics typical of the supercooled state even in the equilibrium liquid state of ZnCl 2 as well as the difference of activation energy at the two investigated length scales are related to the formation of network structure on cooling.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Isotopic Constraints on the Nature of Primary Precipitates in Archean–Early Paleoproterozoic Iron Formations from Determinations of the Iron Phonon Density of States of Greenalite and 2L- and 6L-Ferrihydrite

Iron formations (IFs) are chemical sedimentary rocks that were widely deposited before the Great Oxidation Event (GOE) around 2.4- 2.2 Ga. It is generally thought that IFs precipitated as hydrated Fe 3+ oxides (HFOs) such as ferrihydrite following surface oxidation of Fe 2+ -rich, anoxic deep waters. This model often implicates biological oxidation and underpins reconstructions of marine nutrient concentrations. However, nanoscale petrography indicates that an Fe 2+ silicate, greenalite, is a common primary mineral in well-preserved IFs, motivating an alternative depositional model of anoxic ferrous silicate precipitation. It is unclear, however, if Fe 2+ -rich silicates can produce the Fe isotopic variations in IFs that are well explained by Fe 2+ oxidation. To address this question, we constrain the equilibrium Fe isotopic ( 56 Fe/ 54 Fe) fractionation of greenalite and ferrihydrite by determining the iron phonon densities of states for those minerals. Here, we use ab initio density functional theory (DFT + U) calculations and nuclear resonant inelastic X-ray scattering spectroscopy to show that ferrous greenalite should be isotopically lighter than ferrihydrite by similar to 1-1.2 parts per thousand at equilibrium, and fractionation should scale linearly with increasing Fe 3+ content in greenalite. By anchoring ferrihydrite-greenalite mineral pair fractionations to published experimental Fe isotopic fractionations between HFOs and aqueous Fe 2+ , we show that ferrous greenalite may produce all but the heaviest pre-GOE Fe isotopic compositions and mixed valence greenalites can produce the entire record. Our results suggest that heavy Fe isotope enrichments alone are not diagnostic of primary IF mineralogies, and ferrihydrite and partially oxidized or even purely ferrous greenalite are all viable primary IF mineralogies.

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

Direct Oxidation of Primary Alcohols to Carboxylic Acids

Oxidation of primary alcohols to carboxylic acids is a fundamental transformation in organic chemistry, yet despite its simplicity, extensive use, and relationship to pH, it remains a subject of active research for synthetic organic chemists. Since 2013, a great number of new methods have emerged that utilize transition-metal compounds as catalysts for acceptorless dehydrogenation of alcohols to carboxylates. The interest in this reaction is explained by its atom economy, which is in accord with the principles of sustainability and green chemistry. Furthermore, the methods for the direct synthesis of carboxylic acids from alcohols is ripe for a modern survey, which we provide in this review.

acceptorless dehydrogenation↗
DOE OSTI2025

Maps of growing season gross primary production and net ecosystem exchange for Council Road Mile Marker 71, Seward Peninsula, Alaska, [2017-2023]

This data archive is in support of the Next-Generation Ecosystem Experiments in the Arctic (NGEE Arctic) publication "Integrating Characteristic Arctic Vegetation in a Land Surface Model Improves Representation of Carbon Dynamics Across a Tundra Landscape", by Murphy et al. (2025a). Murphy et al. (2025a) evaluated whether incorporating observed Arctic vegetation heterogeneity into ELM, the land model of the Department of Energy’s Energy Exascale Earth System Model (E3SM), improved simulations of tundra carbon cycling. The associated model archive can be found at Murphy et al. (2025b). The study focused on the spatial patterns and net landscape-level growing season productivity and carbon uptake. As part of this evaluation, observationally derived maps of average growing season (June–August) net ecosystem exchange (NEE) and gross primary production (GPP) were developed for the same domain. These maps, which form the dataset described here, integrate eddy covariance flux tower, remote sensing, and vegetation community data to provide spatially explicit benchmarks for model evaluation. The maps provide spatially explicit estimates of average growing season NEE and GPP across 13 tundra vegetation communities within the study domain. By combining flux tower observations with Airborne Visible-Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) hyperspectral imagery and drone-based normalized difference vegetation index (NDVI), these maps capture the heterogeneity of carbon fluxes associated with different Arctic vegetation types. While they represent average seasonal conditions rather than interannual variability, the maps provide a unique dataset for evaluating model performance, comparing vegetation community contributions to landscape-scale carbon cycling, and supporting regional analyses of Arctic carbon dynamics. This data archive contains 5 m resolution maps of vegetation communities, vegetation community average growing season GPP, and vegetation community average growing season NEE (three *.tif files), a User’s Guide (*pdf file), and Table 1 of the User’s Guide displaying vegetation community coverage and average growing season NEE and GPP values (*.csv file).

Murphy, Bailey [ORNL] (ORCID:0000000203995221)↗
DOE OSTI2021

Observed Global Increases in Tropical Cyclone-Induced Ocean Cooling and Primary Production

Tropical cyclones (TC) provide an important source of mixing for the upper ocean, bringing cold, nutrient-rich water to the surface and triggering phytoplankton blooms. TCs are changing in response to global warming, yet the impacts of those changes on the upper ocean are poorly known. Here we show a significant increasing trend in global TC-induced sea surface temperature (SST) cooling of 0.054oC per decade since 1982. The enhanced cooling has been driven primarily by an increase in the intensity of strong TCs (maximum wind speed > 33 m s-1) and associated strengthening of TC-induced mixing. Consistent with the enhanced mixing and SST cooling, we find a significant increase in TC-induced surface chlorophyll-a concentration of 3.7 x 10-3 mg m-3 per decade in oligotrophic areas. This enhancement of oceanic primary production by TCs is expected to partially offset its projected global decline under climate change.

Dac Da, Nguyen↗
DOE OSTI2021

A Novel State-Constrained Primary Control for Grid-Forming Inverters

The problem of operating an AC microgrid with purely inverter-based generating units is considered, and a novel vector control for the constrained grid-forming (GFM) inverters is proposed. The design aims to achieve the primary control objectives of the GFM inverters: voltage establishment, synchronization, and voltage/frequency regulation. It is novel that the proposed control also ensures all operational variables constrained both real-time and for all the time except for low voltage during the period of grid forming. The constraints include frequency, output AC voltage, input DC power, and active/reactive power injections to the grid. Here, in this paper, outcomes of a detailed simulation are presented to demonstrated that the above design objectives are met and that the overall performance is superior due to its unique ability of simultaneously controlling the whole vectors of the inverter state and output.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2025

Data for Attributing Differences of Solar-Induced Chlorophyll Fluorescence (SIF)-Gross Primary Production (GPP) Relationships between Two C4 Crops, Corn and Miscanthus

Information to characterize the solar-induced chlorophyll fluorescence (SIF)-gross primary production (GPP) relationship in C4 cropping systems remains limited. The annual C4 crop corn and perennial C4 crop miscanthus differ in phenology, canopy structure and leaf physiology. Investigating the SIF-GPP relationships in these species could deepen our understanding of SIF-GPP relationships within C4 crops. Using in situ canopy SIF and GPP measurements for both species along with leaf-level measurements, we found considerable differences in the SIF-GPP relationships between corn and miscanthus, with a stronger SIF-GPP relationship and higher slope of SIF-GPP observed in corn compared to miscanthus. These differences were mainly caused by leaf physiology. For miscanthus, high non-photochemical quenching (NPQ) under high light, temperature and water vapor deficit (VPD) conditions caused a large decline of fluorescence yield (ΦF), which further led to a SIF midday depression and weakened the SIF-GPP relationship. The larger slope in corn than miscanthus was mainly due to its higher GPP in mid-summer, largely attributed to the higher leaf photosynthesis and less NPQ. Our results demonstrated variation of the SIF-GPP relationship within C4 crops and highlighted the importance of leaf physiology in determining canopy SIF behaviors and SIF-GPP relationships.

Feedstock Production↗
DOE OSTI2022

Energy Prediction Impact of the Space Level Occupancy Schedule for a Primary School

By using the same occupant schedule for all spaces, a building level occupancy schedule in building energy modelling can reduce the cost and time of data collection, especially for large-scale simulations or when detailed occupancy data cannot be obtained. However, by describing the unique occupancy status in each space, a space level schedule can better reflect real-world scenarios. This research investigates the energy prediction impact of a space level occupancy schedule for primary school modelling in 16 ASHRAE climate zones. The results show that when switched from a building level to a space level occupancy schedule, the energy prediction difference is between -1.0% and 0.8%. Generally, the predicted building energy consumption using a space level occupancy schedule is higher than when using a building level occupancy schedule in hot and warm climate zones, but lower in other climate zones.

building energy model↗
DOE OSTI2021

Bioresorbable Primary Battery Anodes Built on Core–Double-Shell Zinc Microparticle Networks

Bioresorbable implantable electronics require power sources that are also bioresorbable with controllable electrical output and lifetime. In this paper, we report a bioresorbable zinc primary battery anode filament based on a zinc microparticle (MP) network coated with chitosan and Al 2 O 3 double shells. When discharged in 0.9% NaCl saline, a Zn MP filament with a 0.17 × 2 mm 2 cross-sectional area exhibited a stable voltage output of 0.55 V at a current of 0.01 mA. Covered by chitosan and Al 2 O 3 double shells, the zinc MP filament exhibited a directional dissolution behavior with a tunable lifetime approximately linear to its length. A stable 200 h discharging time was achieved with a 15 mm Zn MP filament. The maximum output power was found to be 12 μW at 0.03 mA for one filament. The linearity relationship between the current output and the filament cross-sectional area suggested a facile strategy to raise the power output at constant discharging voltage. The filaments could also be connected in series and in parallel to boost its overall voltage and current output, demonstrating their excellent integration capability. Furthermore, this work presents a promising pathway toward bioresorbable transient batteries with controllable lifetime and power output, demonstrating a great potential for powering transient implantable biomedical devices.

25 ENERGY STORAGE↗
DOE OSTI2021

Controlling Amphiphilic Polymer Folding beyond the Primary Structure with Protein-Mimetic Di(Phenylalanine)

While methods for polymer synthesis have proliferated, their functionality pales in comparison to natural biopolymers–strategies are limited for building the intricate network of noncovalent interactions necessary to elicit complex, protein-like functions. Using a bioinspired di(phenylalanine) acrylamide (FF) monomer, we explored the impact of various noncovalent interactions in generating ordered assembled structures. Amphiphilic copolymers were synthesized that exhibit β-sheet-like local structure upon collapsing into single-chain assemblies in aqueous environments. Here, the systematic analysis of a series of amphiphilic copolymers illustrated that the global collapse is primarily driven by hydrophobic forces. Hydrogen-bonding and aromatic interactions stabilize local structure, as β-sheet-like interactions were identified via circular dichroism and thioflavin T fluorescence. Similar analysis of phenylalanine (F) and alanine-phenylalanine acrylamide (AF) copolymers found that distancing the aromatic residue from the polymer backbone is sufficient to induce β-sheet-like local structure akin to the FF copolymers; however, the interactions between AF subunits are less stable than those formed by FF. Further, hydrogen-bond donating hydrophilic monomers disrupt internal structure formed by FF within collapsed assemblies. Collectively, these results illuminate design principles for the facile incorporation of multiple facets of protein-mimetic, higher-order structure within folded synthetic polymers.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

TULIP: An RNA-seq-based Primary Tumor Type Prediction Tool Using Convolutional Neural Networks

Background: With cancer as one of the leading causes of death worldwide, accurate primary tumor type prediction is critical in identifying genetic factors that can inhibit or slow tumor progression. There have been efforts to categorize primary tumor types with gene expression data using machine learning, and more recently with deep learning, in the last several years. Methods In this paper, we developed four 1-dimensional (1D) Convolutional Neural Network (CNN) models to classify RNA-seq count data as one of 17 highly represented primary tumor types or 32 primary tumor types regardless of imbalanced representation. Additionally, we adapted the models to take as input either all Ensembl genes (60,483) or protein coding genes only (19,758). Unlike previous work, we avoided selection bias by not filtering genes based on expression values. RNA-seq count data expressed as FPKM-UQ of 9,025 and 10,940 samples from The Cancer Genome Atlas (TCGA) were downloaded from the Genomic Data Commons (GDC) corresponding to 17 and 32 primary tumor types respectively for training and validating the models. Results: All 4 1D-CNN models had an overall accuracy of 94.7% to 97.6% on the test dataset. Further evaluation indicates that the models with protein coding genes only as features performed with better accuracy compared to the models with all Ensembl genes for both 17 and 32 primary tumor types. For all models, the accuracy by primary tumor type was above 80% for most primary tumor types. Conclusions: We packaged all 4 models as a Python-based deep learning classification tool called TULIP (TUmor CLassIfication Predictor) for performing quality control on primary tumor samples and characterizing cancer samples of unknown tumor type. Further optimization of the models is needed to improve the accuracy of certain primary tumor types.

Jones, Sara↗
DOE OSTI2020

Double-Shell Tank Primary Bottom Visual Inspection Results - 20025

A pressing need for the Double-Shell Tank Integrity Program at Hanford is of the ability to inspect the bottom of the tanks. Though the DSTs' annulus spaces (the space between the internal and external tank walls) have been inspected visually since 1992 and with Ultrasonic Testing (UT) since 1996, inspections of the tank bottoms have been able to be conducted due to limitations of available technology, omitting about 40 percent of the tank surface below the maximum fill level from routine inspection. Visual inspections of the tank annulus have been conducted up to five times in some DSTs and portions of the annulus space for all of the DSTs has been inspected twice with UT. This region includes the walls of the primary tank and secondary liner along with accessible portions of the secondary liner bottom. Due to this lack of technology, the degradation of the primary tank bottom in Tank AY-102 was undetected until the tank leaked into its annulus space, confirmed in 2012. Since that time, WRPS has been working with the nondestructive examination community and the Pacific Northwest National Laboratory, to evaluate potential technologies to conduct these challenging remote inspections. Recent work has included the development, testing, and deployment of several remote visual inspection technologies to observe the condition of the primary tank bottom of the DSTs by leveraging air distribution slots in the refractory pad on which it rests. Robotic solutions to conduct this inspection were developed with a detailed understanding of the physical configuration and operational logistics. Understanding and subsequent communication of these details to vendors within the robotics and nondestructive examination community was a critical component to the successful production of these new tools. Throughout the FY16-FY18 years, vendors from nondestructive examination community were solicited for ideas to meet the tank bottom inspection challenge. Two tank bottom visual inspection solutions were then pursued to meet that challenge. Both tools were successfully deployed at Tank AP-107, and provided valuable visuals, never before obtained from the under tank environment of a double-shell tank. While there are minor opportunities for improvement identified with each system, general outcomes were overwhelmingly positive. Both systems were successfully able to navigate the refractory air slot pattern from the outer edge to the tank center and record video footage. The micro-crawler provided high quality and very stable footage through the air slot pattern, but took longer to deploy into the slots and traverse through them than the alternative system. The push-pull tethered camera was able to navigate all seven of its target air slots in a matter of a few hours, which included deployment, movement between slot entrances, and complete removal of the system from the annulus environment. Through these air slots, new information was gained about the tank bottom and refractory pad condition of tank AP-107, AP-106, AP-108, and AN-102. While some debris was noted in the air slots, including sand, chipped refractory, and construction materials, the refractory was found to be structurally stable and adequately supporting the primary tank bottom through the full diameter of the tank. Primary tank bottom steel exterior was found to be in good condition, free of ongoing or aggressive corrosion conditions. Looking toward the future, robotic access to and visual inspection of refractory air slots and the primary tank bottom steel has now proven viable. Incorporating periodic air slot inspections as a regular practice within the Double-Shell Tank Integrity Program is the next step, allowing trending of any changes in condition. As ultrasonic testing infrastructure was leveraged to complete the initial system deployments described in this report, it makes sense to adopt that same strategy for ongoing periodic examinations and deploy these primary tank bottom inspection tools alongside ultrasonic testing operations. Development of visual inspection tools to evaluate the primary tank bottom region of the double-shell tanks was planned as the first stage of inspection. Continued development of volumetric sensors and incorporation of these inspection tools into Hanford's ongoing Double-Shell Tank Integrity Program are the next steps, seeking to continuously expand understanding of asset integrity and remaining useful life. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗