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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

Renewable Energy Materials Properties Database: Summary

Renewable energy is providing a growing share of electricity generation in the United States, with generation from utility scale wind and solar increasing by 12% and 29%, respectively, in 2020 (EIA 2021). Installation of new renewable energy facilities requires material inputs that range from common construction materials to specialized, high-performance materials that may have limited availability. This report summarizes the Department of Energy (DOE) Wind Energy Technologies Office (WETO) and DOE Solar Energy Technologies Office (SETO) database of materials used in domestic wind and solar installations. The Energy Act of 2020 directs WETO and SETO to deliver a "comprehensive physical property database of materials for use in [wind and solar] energy technologies, which shall identify the type, quantity, country of origin, source, significant uses, projected availability, and physical properties of materials used in [wind and solar] energy technologies" by no later than September 1, 2022. This report is the summary and directions for use of the Renewable Energy Materials Property Database (REMPD), in response to that direction. The related full text of the Energy Act of 2020 is provided in Appendix A. The focus of the REMPD and this accompanying report is on quantifying the raw and processed materials used in renewable energy technologies. The database contains information on the amount of each material that goes into wind and solar power plants, descriptions of the relevant material properties, and the primary countries of origin for each material. Some materials go through several stages of processing and/or are incorporated into subcomponents that make up the completed energy generation facilities. This report does not analyze supply chains for the production of renewable energy plant components. The Department of Energy has carried out related analysis in response to Executive Order 14017, which directed production of "America's Supply Chains" reports for Wind and Solar. The supply chain reports can be found at https://www.energy.gov/sites/default/files/2022-02/Wind%20Supply%20Chain%20Report%20-%20Final%202.25.22.pdf (Wind) and https://www.energy.gov/sites/default/files/2022-02/Solar%20Energy%20Supply%20Chain%20Report%20-%20Final.pdf (Solar). Additional reports in this series that are relevant to wind, solar and hybrid plants include: Energy Storage at https://www.energy.gov/sites/default/files/2022-02/Energy%20Storage%20Supply%20Chain%20Report%20-%20final.pdf and Neodymium Magnets at https://www.energy.gov/sites/default/files/2022-02/Neodymium%20Magnets%20Supply%20Chain%20Report%20-%20Final.pdf.

14 SOLAR ENERGY↗
DOE OSTI2020

The energy footprint of automotive electronic sensors

We discuss how Information and communication technologies (ICT) have emerged as one of the leading technologies to reduce global emissions, particularly in the mobility sector. Automotive electronics, such as sensors/actuators and microcontrollers (commonly known as electronic control units (ECU) which control one or more of the electrical systems or subsystems in a vehicle), play a key role in ICT. Sensors/actuators are key in electronic ICT devices, starting with the data collection and data communication with the internet. The latest two big trends of electrification and automation in vehicles, are projected to increase the use of worldwide automotive sensors from 7.5 billion units in 2017 to 11.0 billion units by the year 2024. A representative state-of-the art automotive sensor system, (i.e., an ultrasonic backup system), including the ECUs controlling the vehicle electrical systems/subsystems, has been considered to estimate the energy footprint in terms of manufacturing and operational energy of global automotive sensors use. A widely used life cycle energy assessment method (i.e., cumulative energy demand) was used as both direct and indirect (including the extraction, manufacturing, and disposal of the raw and auxiliary materials) energy use can be considered for the energy footprint estimation. The embodied manufacturing energy impacts of the system was estimated to be 559 MJ/system, compared to the 417 MJ/system for lifetime system power and additional gasoline use. The share of purchased energy to the embodied energy where the upstream energy isn't included in the former case, is less than 10% and ~ 85% for the component manufacturing and vehicle operation energy uses, respectively. As the purpose of this ultrasonic backup system is to prevent rear crashes, an estimated 1.0 MJ/system is avoided from reduced lifetime vehicular repairs (from an estimated 11% chance of requiring a rear bumper replacement). While all of this is small compared to the overall automotive manufacturing and use energy, the 11 billion automotive sensors expected to be produced in 2024 could require 1540 PJ for manufacturing and those sensors would require an additional 780–1150 PJ for lifetime energy use.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Recent Advances in the Chemistry of Hydrometallurgical Methods

Producing pure substances has been the obsession of scientists for centuries and has defined chemistry as we know it. Nowadays, most of our economy still relies on the chemist ability to extract and purify metallic elements from raw or recycled materials, which, in most cases, is done partly or entirely via hydrometallurgical methods. In this work, the authors present the critical aspects of this relatively overlooked, yet critical, chemistry discipline. This review article highlights the importance of hydrometallurgical processes for strategic industrial sectors, put in perspective traditional separation methods with emerging ones (new metal-binding chelators, ionic liquids, deep eutectic solvents, microfluidic devices, biomaterials, etc.), critically reviews progress made on separation methods over the past two decades, and highlights the challenges lying ahead for the hydrometallurgists in academia and the industry.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Recycling Wind Energy Systems in the United States Part 1: Providing a Baseline for America's Wind Energy Recycling Infrastructure for Wind Turbines and Systems

The U.S. investments in building this new wind energy capacity will not only mobilize millions of tons of raw and processed materials in existing supply chains, some of which are critical materials, but also create new types and large volumes of end-of-life (EOL) waste streams. Building efficient, cost-effective, and environmentally responsible EOL management infrastructure of wind energy system components is pivotal in diverting upcoming volumes of waste stream from landfills, recovery of critical materials and reducing life cycle emissions from production of primary commodity materials . The primary goal of this report is to organize and communicate findings from this assessment on how alternate materials, designs and manufacturing processes could enable more efficient, cost-effective, and environmentally responsible disassembly and resource recovery from wind energy technologies. The findings of this assessment could inform prioritization of RD&D investment spending to meet Energy Act of 2020 directions. This assessment focused on key RD&D recommendations for three main temporal phases: Short-term (2023-2026), medium-term (2026 through 2035) and long-term (beyond 2035).

17 WIND ENERGY↗
DOE OSTI2024

Organizational Influence on Supply Chain for Digital Energy Infrastructure: Business Models, and Policy Landscape

Significant investment is driving the essential modernization and digitization of U.S. energy infrastructure, but the United States faces a key challenge in this grid transformation: our renewable and clean energy supply chains have limited capacity to source necessary digital assets through U.S. or allied sources. Batteries and their associated power electronic interfaces are key components to delivering clean and more resilient energy delivery, providing much-needed fast ramping, emergency discharge, generation, and operations support to the electric grid. These services have grown to be invaluable over the past 10 years and will soon be an irreplaceable component of energy delivery. While there have been significant strides to move supply chains for raw and critical materials to U.S. and allied nations, the control and power electronic industry has lagged, in part because of lower cost margins. For example, the United States now has growing capacity to manufacture solar photovoltaic (PV) panels, but 90% of the inverters—which are essential to the conversion of DC to AC for grid connection and controls—are made in or source parts from the People’s Republic of China (PRC). While a global supply chain is beneficial for many economic and supply diversification reasons, presence of foreign entities of concern (FEOC) in a dominant role in this supply chain brings additional concerns for national security of infrastructure. The BESS relationship and new clean energy have features that warrant discussions and additional focus on impact for technology security, given their critical role. This review considers the impact and solutions for complex business and policy landscapes in clean energy supply chain, and the benefit trade-offs, focusing on the Battery Energy Storage System case study, and its complex organizational relationships across both the US, Allied countries and Foreign Entities of Concern. While many examples in this paper are presented on PRC focused manufacture, this could apply to any potentially adversarial relationship

25 - ENERGY STORAGE↗
DOE OSTI2023

Exploration of fibers produced from petroleum based-mesophase pitch and pet blends for carbon fiber production

Petroleum-derived mesophase pitch and polyethylene terephthalate (PET) were blended and melt-extruded to create precursor fibers. The raw blends, heat treated bulk materials, and fibers were all examined to determine compatibility between petroleum-derived mesophase pitch and PET. Thermal investigation of the raw blends showed the presence of both the pitch and PET in separate phases suggesting no interactions had occurred during mixing. Here, the heat-treated bulk material also showed multiple phases under polarized light while Fourier-transform infrared spectroscopy indicated that the two materials did not fully separate during extrusion. Elemental scans of the precursor fibers showed areas of high oxygen concentrations averaging 11.7% as compared to a 2% oxygen concentration across the majority of the cross-section. Tensile tests of the precursor fibers showed a decrease in average modulus as compared to pure pitch fibers; however, the depressed modulus of the blended fibers was found to be independent of the PET weight percentage. Fibers created from 5 wt.% PET had a strength value within 10% of pure pitch and a 200% increase in modulus and were therefore found to be the most promising for carbon fiber production. Pure pitch and the 5 wt.% PET fibers were stabilized and carbonized to 1000 °C. Both types of fibers were tensile tested to determine their strength and stiffness. Pure pitch carbon fibers maintained a higher strength while the carbon fibers produced from 5 wt.% PET possessed a stiffer modulus which can be attributed to their radial microstructure.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Metabolism of Scenedesmus obliquus cultivated with raw plant substrates

The potential benefits of adding raw, non-food, lignocellulosic plant material as a carbon source for mixotrophic growth of microalgae have previously been demonstrated. This approach has advantages over using traditional carbon sources like glucose or acetate due to wide-spread plant biomass availability and substrate recalcitrance to bacterial contamination. Here, we report the overall growth characteristics and explore the metabolic patterns of Scenedesmus obliquus cultured in the presence raw plant substrate. An initial screen of plant substrate candidates showed an increase in specific growth rate and biomass accumulation when S. obliquus was cultured in the presence of switchgrass or yard waste compared to media alone. We observed a near doubling of microalgal dry weight when S. obliquus was grown with 0.2% ( w/v ) switchgrass under ambient CO 2 . Scanning electron microscopy (SEM) of corn stem after S. obliquus cultivation exhibited substantial phloem degradation. Transcriptomic analyses of S. obliquus during mid- and late-log phase growth revealed a dynamic metabolic landscape within many KEGG pathways. Notably, differential expression was observed for several potential glycosyl hydrolases. We also investigated the influence of switchgrass on the growth of S. obliquus at 50 L volume in mini raceway ponds to determine the scalability of this approach.

Schambach, Jenna Y.↗
DOE OSTI2021

Hybrid Machine Learning for Scanning Near-Field Optical Spectroscopy

The underlying physics behind an experimental observation often lacks a simple analytical description. This is especially the case for scanning probe microscopy techniques, where the interaction between the probe and the sample is nontrivial. Realistic modeling to include the details of the probe is always exponentially more difficult than its "spherical cow" counterparts. On the other hand, a well-trained artificial neural network based on real data can grasp the hidden correlation between the signal and sample properties. In this work, we show that, via a combination of model calculation and experimental data acquisition, a physics-infused hybrid neural network can predict the tip-sample interaction in the widely used scattering-type scanning near-field optical microscope. This hybrid network provides a long-sought solution for accurate extraction of material properties from tip-specific raw data. The methodology can be extended to other scanning probe microscopy techniques as well as other data-oriented physical problems in general.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Bridging the Gap between the X-ray Absorption Spectroscopy and the Computational Catalysis Communities in Heterogeneous Catalysis: A Perspective on the Current and Future Research Directions

X-ray absorption spectroscopy (XAS) [extended X-ray absorption fine structure (EXAFS) and X-ray absorption near-edge structure (XANES)] is a key technique within the heterogeneous catalysis community to probe the structure and properties of the active site(s) for a diverse range of catalytic materials. Furthermore, the interpretation of the raw experimental data to derive an atomistic picture of the catalyst requires modeling and analysis; the EXAFS data are compared to a model, and a goodness of fit parameter is used to judge the best fit. This EXAFS modeling can often be nontrivial and time-consuming; overcoming or improving these limitations remains a central challenge for the community. Considering these limitations, this Perspective highlights how recent developments in analysis software, increased availability of reliable computational models, and application of data science tools can be used to improve the speed, accuracy, and reliability of EXAFS interpretation. In particular, we emphasize the advantages of combining theory and EXAFS as a unified technique that should be treated as a standard (when applicable) to identify catalytic sites and not two separate complementary methods. Building on the recent trends in the computational catalysis community, we also present a community-driven approach to adopt FAIR Guiding Principles for the collection, analysis, dissemination, and storage of XAS data. Written with both the experimental and theory audience in mind, we provide a unified roadmap to foster collaborations between the two communities.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Unsupervised learning-enabled pulsed infrared thermographic microscopy of subsurface defects in stainless steel

Metallic structures produced with laser powder bed fusion (LPBF) additive manufacturing method (AM) frequently contain microscopic porosity defects, with typical approximate size distribution from one to 100 microns. Presence of such defects could lead to premature failure of the structure. In principle, structural integrity assessment of LPBF metals can be accomplished with nondestructive evaluation (NDE). Pulsed infrared thermography (PIT) is a non-contact, one-sided NDE method that allows for imaging of internal defects in arbitrary size and shape metallic structures using heat transfer. PIT imaging is performed using compact instrumentation consisting of a flash lamp for deposition of a heat pulse, and a fast frame infrared (IR) camera for measuring surface temperature transients. However, limitations of imaging resolution with PIT include blurring due to heat diffusion, sensitivity limit of the IR camera. We demonstrate enhancement of PIT imaging capability with unsupervised learning (UL), which enables PIT microscopy of subsurface defects in high strength corrosion resistant stainless steel 316 alloy. PIT images were processed with UL spatial–temporal separation-based clustering segmentation (STSCS) algorithm, refined by morphology image processing methods to enhance visibility of defects. The STSCS algorithm starts with wavelet decomposition to spatially de-noise thermograms, followed by UL principal component analysis (PCA), fine-tuning optimization, and neural learning-based independent component analysis (ICA) algorithms to temporally compress de-noised thermograms. The compressed thermograms were further processed with UL-based graph thresholding K-means clustering algorithm for defects segmentation. The STSCS algorithm also includes online learning feature for efficient re-training of the model with new data. For this study, metallic specimens with calibrated microscopic flat bottom hole defects, with diameters in the range from 203 to 76 µm, were produced using electro discharge machining (EDM) drilling. While the raw thermograms do not show any material defects, using STSCS algorithm to process PIT images reveals defects as small as 101 µm in diameter. To the best of our knowledge, this is the smallest reported size of a sub-surface defect in a metal imaged with PIT, which demonstrates the PIT capability of detecting defects in the size range relevant to quality control requirements of LPBF-printed high-strength metals.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Defect identification in simulated Bragg coherent diffraction imaging by automated AI

X-ray Bragg coherent diffraction imaging is a powerful technique for operando and in situ materials characterization and provides a unique means of quantifying the influence of one-dimensional (1D) and two-dimensional (2D) material defects on material response. However, obtaining full images from raw x-ray diffraction data is nontrivial and computationally intensive, precluding real-time experimental feedback. Here, we present a machine learning approach to identify the presence of crystalline line defects (edge and screw) in samples from the raw, 2D, coherent diffraction data without the need for image reconstruction through iterative phase retrieval. Further, we compare different approaches to designing neural networks for this application and demonstrate the potential of automated ML (autoML) approaches.

36 MATERIALS SCIENCE↗
DOE OSTI2026

INL CRMO XRF Special Report No. 3: INL CRMO XRF Calibration Methods for the Analysis of Ceramics and Brick

This report details the analytical methods used by the Idaho National Laboratory Cultural Resource Management Office to characterize ceramic and brick artifacts using X-ray fluorescence spectrometry. More specifically, this report provides details on instrumentation, analysis times, and matrix-specific calibration protocols for ceramic materials. Calibration curves were developed to adjust raw instrumental output for 13 elements using a suite of reference standards made from historic brick borrowed from the Yale Peabody Museum. Calibration curves were validated by plotting predicted values against published reference values for the calibration set as well as a certified soil reference standard (SRM 2711a).

36 MATERIALS SCIENCE↗
DOE OSTI2023

Measurement of the low-energy antitriton inelastic cross section

In this Letter, the first measurement of the inelastic cross section for antitriton–nucleus interactions is reported, covering the momentum range of 0.8 ≤ p < 2.4 GeV/c. The measurement is carried out using data recorded with the ALICE detector in pp and Pb–Pb collisions at a centre-of-mass energy per nucleon of 13 TeV and 5.02 TeV, respectively. The detector material serves as an absorber for antitriton nuclei. The raw yield of (anti)triton nuclei measured with the ALICE apparatus is compared to the results from detailed ALICE simulations based on the GEANT4 toolkit for the propagation of (anti)particles through matter, allowing one to quantify the inelastic interaction probability in the detector material. This analysis complements the measurement of the inelastic cross section of antinuclei up to A = 3 carried out by the ALICE Collaboration, and demonstrates the feasibility of the study of the isospin dependence of inelastic interaction cross section with the analysis techniques presented in this Letter.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗
DOE OSTI2022

Accelerating the discovery of battery electrode materials through data mining and deep learning models

The availability of crystalline materials databases allows for building accurate machine learning (ML) models that can accelerate the exploration of materials chemical space for energy storage applications. In this work, we screen all inorganic materials included in the Materials Project and AFLOW databases as potential metal-ion battery electrodes. We develop an efficient protocol to mine and screen raw data in current databases and provide a new database of electrode materials by considering pairs of charged and discharged electrodes. This effort leads to a new database with over 190,000 instances, in contrast to the original battery database which contains about 5000. The expanded battery data set is then used to build regression-based deep neural network models for predicting average voltages and percentage volume changes upon charging and discharging, which present improvements of at least 28% for target properties with respect to previous models, and are now able to predict anode electrodes (low voltage region) as well as electrodes that will not work in electrochemical cells (negative voltages), overcoming the challenges identified in previous ML models for battery electrodes. Additionally, a further screening of the expanded database itself allowed us to identify 35 novel electrode candidates with excellent battery performance metrics.

25 ENERGY STORAGE↗
DOE OSTI2020

Leaf isoprene and monoterpene emission data-set across hyperdominant tree genera in the Amazon basin

This data package contains the analyzed data used to support the publication "Leaf isoprene and monoterpene emission distribution across hyperdominant tree genera in the Amazon basin," Phytochemistry, in-press. Data was collected from 162 trees across four sites in the Amazon. For all leaf samples studied for volatile isoprenoid emissions, branch cuttings were conducted in the upper canopy with sun exposed leaves with the assistance of a tree climber utilizing a pole pruner or directly accessed from flux towers. Large branches were removed from the upper canopy (up to 0.5-1.0 meter in length) and rapidly recut on the ground under water to maintain the transpiration stream. Net photosynthesis and isoprene and monoterpene emission rates were quantified from leaves during controlled changes in photosynthetically active radiation (PAR) using a commercial leaf photosynthesis system (LI-6400XT, LI-COR Inc., USA) interfaced with a gas chromatograph-mass spectrometer (GC-MS, 5975C series, Agilent Technologies, USA). For detailed methods and raw data, refer to the publication and related material cited in Dataset References.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Raw leaf gas exchange data in the Amazon basin, 2014-2016

This data package contains raw leaf gas exchange data used to support the publication "Leaf isoprene and monoterpene emission distribution across hyperdominant tree genera in the Amazon basin," Phytochemistry, in-press. Data was collected from 162 individuals, distributed among 25 botanical families and 113 species, from four sites in the Amazon. The data was collected using a modified portable photosynthesis system for environmental control coupled to a thermal desorption tube for volatile isoprenoid collections. Collected TD tube samples were analysed in Manaus, Brazil, using thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS). The dataset includes both photosynthesis and isoprene/monoterpene emission rates from leaves under standard environmental conditions as well as controlled light response curves. For detailed methods and raw data, refer to the publication and related material cited in Dataset References.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

High-Throughput Analysis of Materials for Chemical Looping Processes

Chemical looping is a promising approach for improving the energy efficiency of many industrial chemical processes. However, a major limitation of modern chemical looping technologies is the lack of suitable active materials to mediate the involved subreactions. Identification of suitable materials has been historically limited by the scarcity of high-temperature (>600 °C) thermochemical data to evaluate candidate materials. An accuratethermodynamic approach is demonstrated here to rapidly identify active materials which is applicable to a wide variety of chemical looping chemistries. Application of this analysis to chemical looping combustion correctly classifies 17/17 experimentally studied redox materials by their viability and identifies over 1300 promising yet previously unstudied active materials. This approach is further demonstrated by analyzing redox pairs for mediating a novel chemical looping process for producing pure SO2 from raw sulfur and air which could provide a more efficient and lower emission route to sulfuric acid. 12 promising redox materials for this process are identified, two of which are supported by previous experimental studies of their individual oxidation and reduction reactions. This approach provides the necessary foundation for connecting process design with high-throughput material discovery to accelerate the innovation and development of a wide range of chemical looping technologies.

chemical looping↗
DOE OSTI2020

Open Source Visualization and Analysis Platform for 3D Reconstructions of Materials by Transmission Electron Microscopy

Three-dimensional characterization of materials at the nano- and meso-scale has become possible with transmission and scanning transmission electron microscopes (S/TEM). Its importance has extended to a wide class of nanomaterials such as hydrogen fuel cells, solar cells, industrial catalysts, new battery materials and semiconductor devices, as well as spanning high-tech industry, universities, and national labs. While capable instrumentation is abundant, this rapidly expanding demand for high-resolution tomography is bottlenecked by software that is instead tailored for lower-dose, biological applications and not optimized for higher-resolution materials applications. Existing tomography fails to utilize the chemical information provided by S/TEM spectrometers. To address this problem, this project delivered a scalable, fully functional, freely-distributable, open Source materials tomography package with a modern user interface that enables automated acquisition, alignment, and real-time reconstruction of raw tomography data, and provides advanced segmentation, three-dimensional chemical visualization and analysis optimized for materials applications. It has established an extendable framework capable of automation for high-throughput for the tomography of materials from data acquisition to visualization. Phase I and II delivered a full-featured, cross-platform, clean and integrated application for S/TEM materials tomography. It can read projection data from the microscope, with graphical tools for alignment of data, tomographic reconstruction, segmentation, and visualization of the reconstructed 3D volume. The entire pipeline can be saved to an XML state file, enabling fully reproducible data processing and analysis with a full Python environment for the development of custom algorithms, processing, and analysis requirements. Phase IIB extended the application to provide real-time tomographic reconstruction, "live updates" as data is processed, analysis of big data, and multi-channel tomography. Here, quantitative assessment of nanomaterials can occur as data is being recorded on a 3D visualization platform that accommodates additional information from multiple channels. We provide unmatched high-throughput tomography, where the complete tomographic pipeline from measurement to 3D visualization can occur rapidly. The project was extended to offer capabilities for micro-CT, X-ray, neutron, focused ion beam, atomic electron tomography and atom probe tomography. With over 600 transmission electron microscopes worldwide and approximately 50 coming online each year, the demand and impact of an open-source tomography tool is large. Significant opportunities exist in high-tech industry, universities, and national labs to enable or enhance three-dimensional imaging at the nanoscale and bring automated high-throughput approaches that will accelerate progress in materials characterization and metrology. The project supports a service based business model by enabling lab-specific acquisition and processing customization and integration-support and development that will be provided into Phase III and beyond.

36 MATERIALS SCIENCE↗