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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Wyoming CarbonSAFE: Advancing Commercialization of Low-Carbon Energy Technologies in Fossil-Rich Wyoming

Wyoming’s Powder River Basin (PRB) is the most prolific coal producer and exporter in the United States and the State of Wyoming’s largest oil-producing basin. In addition to being a leading energy producer, the PRB is the site of research programs whose aim is to develop and integrate low-carbon technologies into existing fossil fuel energy industries. Much of the PRB’s low-carbon research is focused around Dry Fork Station (DFS), which is the newest coal-fired power station in the western US. Currently, DFS hosts five carbon capture projects, including pilot-scale capture projects that utilize different capture technologies and a full front-end engineering and design (FEED) study, a commercial CO 2 pipeline for nearby CO 2 -enhanced oil recovery industry, and is also co-located with the Wyoming Integrated Test Center, which is a host facility for unconventional carbon utilization research (i.e. flue gas carbon-to-products innovations). DFS is also the host site for the Wyoming CarbonSAFE project. Wyoming CarbonSAFE, funded by the Department of Energy/National Energy Technology Laboratory, is a multi-phased program (currently in Phase II) whose core objective is developing and validating storage sites within a complex capable of storing 50 million metric tonnes of CO 2 over a 30 year project period using carbon capture, utilization and storage (CCUS). This paper will provide an overview of the project to-date, showing that CarbonSAFE goals are achievable with respect to geologic, environmental, regulatory, CO 2 source and economic conditions, and why the State of Wyoming offers one of the more favorable environments to advance the commercialization studies. In 2019, the project team designed and completed a ~10,000 foot stratigraphic test well just south of DFS. From this well, the team collected over 600 feet of core, fluid samples from target injection zones, and a full petrophysical log suite. In addition, legacy 2D seismic lines were acquired and a 3D seismic survey was acquired in the fall of 2020. The objective of these field activities has been to identify and characterize target storage reservoirs and associated caprock. This complex has several reservoirs that could meet commercial injection goals, and over 4000 feet of associated caprock. Simulations of site performance suggest that stacked injection provides the most effective storage strategy, and would necessitate several sites within the greater complex to meet final project injection goals. Other work within this project includes the development of a business-case around proximal fields with CO 2 -enhanced oil recovery potential and existing tax credits, assessment of regulatory conditions, including pore space ownership and Class VI injection well permitting requirements, implementation of a robust public outreach Electronic copy available at: https://ssrn.com/abstract=3821220 GHGT-15 McLaughlin 2 program, and surface site characterization activities that have focused on environmental factors. In the PRB and at DFS, the State of Wyoming and other entities have made carbon management a priority of its future energy industry by providing a regulatory and business framework that is favorable to advancing these technologies. These endeavors will become more realistic with the successful implementation of Wyoming CarbonSAFE, and its ability to secure and validate commercial-scale CCUS at the center of Wyoming’s premier low-carbon research efforts.

McLaughlin, Fred↗
DOE OSTI2025

Age hardening response of Cu-30Ni alloys: The role of Si microalloying additions

Cu-30Ni alloys offer high strength and excellent corrosion resistance for maritime applications. While primarily a solid solution system, industrial alloys typically include microalloying additions of Fe, Mn, Nb and/or Si to enable age hardening. However, an understanding of the microstructural evolution during age hardening remains incomplete. Here, we conduct systematic ageing treatments and report the resulting hardness and microstructures. The Cu-30Ni alloy with Si aged at 650 °C for 6 h demonstrates significantly enhanced Vickers hardness, reaching ∼201 HV 1 compared to 103 HV 1 for the as-homogenised sample. Si-rich clusters and γ′ precipitates are identified, with their composition, size, and volume fraction quantified to determine their strengthening contributions. First-principles atomistic simulations elucidate the underlying formation mechanisms of these clusters and precipitates, highlighting the critical role of Si in driving their nucleation and growth. This study advances the design of high-strength, Cu alloys with the potential for corrosion resistance in demanding maritime environments.

Age hardening↗
DOE OSTI2026

Development of a Flow-through Cell for Ultrasonic Extraction (UE)─Single Particle (SP)─ICP-MS─an Approach for Nano/Microparticle Elemental and Isotopic Analysis

Nanotechnology is a salient part of the scientific landscape, and analytical approaches are rapidly evolving to enable small-scale characterization of nano- and microparticle compositions and impurities. Here, a flow-through sonicating cell was developed for direct particle extraction from a silicon wafer and integrated with an inductively coupled plasma–mass spectrometer (ICP-MS) for subsequent elemental and isotopic characterization of the released particles. Ultrasonic extraction (UE)─single particle (SP)─ICP-MS offers controlled particle mobilization from solid substrates and allows for increased sample throughput by eliminating the need for pre-extraction of particles and decreasing sample preparation steps. Coupling this device to an ICP-MS with a time-of-flight (TOF) mass analyzer, it is possible to distinguish unique isotopic compositions of the particles. Both tungsten and nickel isotopically tagged particles, which were deposited on Si wafers, are presented here with analysis via UE─SP─ICP-MS. This approach could support efforts in the fields of particle synthesis, nuclear safeguards and forensics, environmental monitoring, and semiconductor industries in which the detection of particles from substrates and wafers is critical.

Paul, Molly [ORNL] (ORCID:0009000009672055)↗
DOE OSTI2024

Single-nuclei transcriptome analysis of channel catfish spleen provides insight into the immunome of an aquaculture-relevant species

The catfish industry is the largest sector of U.S. aquaculture production. Given its role in food production, the catfish immune response to industry-relevant pathogens has been extensively studied and has provided crucial information on innate and adaptive immune function during disease progression. To further examine the channel catfish immune system, we performed single-cell RNA sequencing on nuclei isolated from whole spleens, a major lymphoid organ in teleost fish. Libraries were prepared using the 10X Genomics Chromium X with the Next GEM Single Cell 3’ reagents and sequenced on an Illumina sequencer. Each demultiplexed sample was aligned to the Coco_2.0 channel catfish reference assembly, filtered, and counted to generate feature-barcode matrices. From whole spleen samples, outputs were analyzed both individually and as an integrated dataset. The three splenic transcriptome libraries generated an average of 278,717,872 reads from a mean 8,157 cells. The integrated data included 19,613 cells, counts for 20,121 genes, with a median 665 genes/cell. Cluster analysis of all cells identified 17 clusters which were classified as erythroid, hematopoietic stem cells, B cells, T cells, myeloid cells, and endothelial cells. Subcluster analysis was carried out on the immune cell populations. Here, distinct subclusters such as immature B cells, mature B cells, plasma cells, γδ T cells, dendritic cells, and macrophages were further identified. Differential gene expression analyses allowed for the identification of the most highly expressed genes for each cluster and subcluster. This dataset is a rich cellular gene expression resource for investigation of the channel catfish and teleost splenic immunome.

Science & Technology - Other Topics↗
DOE OSTI2023

Investigating Marine Environmental Degradation of Additive Manufacturing Materials for Renewable Energy Applications: Preprint

Marine renewable energy is a relatively young industry where there is a great need for rapid prototyping in design-build-test campaigns to quickly mature groundbreaking technologies. Additive manufacturing has an important role to play in the industry; however, little information is available to marine energy developers to help inform them on which additive manufacturing materials are appropriate for harsh marine environments. This paper presents an initial study on the mechanical characterization of polymeric additive manufacturing materials and the degradation effects of marine environments. Ultem 9085, acrylonitrile styrene acrylate, and chopped carbon-filled nylon, as well as continuous carbon and glass fiber-reinforced nylon were chosen for this study. Samples were manufactured to perform a variety of tension, shear, and compression mechanical characterization tests on the materials. Half of the samples were conditioned in Pacific Ocean water for approximately 6 months at Pacific Northwest National Laboratory's Marine Sciences Laboratory before being returned for mechanical characterization. The mechanical testing results showed that the Ultem 9085 and acrylonitrile styrene acrylate materials experienced little to no degradation in stiffness or strength after exposure to the marine environment. On the other hand, the nylon-based materials suffered significant stiffness and strength degradation (over 50% in some cases) after environmental conditioning. Ultimately, these data sets should serve as starting points to allow marine renewable energy developers to make informed additive manufacturing material choices for their prototype deployments.

additive manufacturing↗
DOE OSTI2025

Enhanced boron doping for diamond growth in cylindrical CVD reactors using a Faraday cage

We report on the use of 3D-printed titanium Faraday cages in a cylindrical resonant cavity CVD reactor for the growth of heavily boron-doped single-crystalline superconducting diamond. We show that the cage enhances boron doping uniformity and provides control of the doping efficiency and growth rate by varying the distance between the plasma and the diamond surface. Using finite element modelling, we demonstrate that the Faraday cage focuses the electric field at the top of the cage whilst also suppressing the electric field inside the cage, thereby pinning the plasma at a fixed height above the sample. This eliminates plasma “hot spots” at the edges of the sample and creates a more uniform plasma electron and hydrogen radical distribution over the sample, resulting in a more uniform boron incorporation.

Boron doped diamond↗
DOE OSTI2024

A model to separate conduction and radiation in high temperature laser flash measurements for semi-transparent materials

Laser flash analysis (LFA) is a widely used technique in the scientific and industrial communities for measuring the thermal diffusivity and conductivity of materials. However, traditional fitting methods based solely on heat conduction do not account for radiation heat transfer in transparent and semi-transparent materials, which becomes significant at high temperatures. In this study, we have developed a coupled radiation-conduction model that considers the optical properties of materials to accurately extract thermal diffusivity from LFA measurements. We conducted LFA measurements on fused silica glass samples of varying thicknesses and within the temperature range of 25 to 1000 °C and fitted the data with the model. Since the coupled model requires optical properties as input parameters, which are often unavailable for new materials, we have also proposed a modified transparent model. Compared to the coupled model, results from the modified transparent model generally overestimate thermal conductivity, but the errors are acceptable in silica glass with small thickness. Here, we also investigated the applicability of the modified transparent model with materials of different optical properties.

14 SOLAR ENERGY↗
DOE OSTI2024

Survey of Space Professionals’ Perception of Satellite Cybersecurity from 2012 to 2022: Decision-Makers’ Thoughts on Satellite Cybersecurity Evolving

Cyberattacks on space assets are often portrayed in vague terms of doubt and mystery. Several claims depict satellites being compromised or attacked, but little corroboration has been published or made publicly available. As the commercial space industry is growing, commercial satellite decision makers will need to analyze the unacknowledged risk of cyberattacks against satellites. This paper identifies and characterizes what a cybersecurity risk to a space asset could look like and why space professionals might not prioritize cybersecurity. Additional information was captured from a decadal survey of space professionals in 2012 and 2022. Comparing the decadal results shows a rise in the perceived risk of satellites to cybersecurity threats from a sample of space professionals. This growing notable shift of perspective is not fully defined or agreed upon.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Investigating Marine Environmental Degradation Of Additive Manufacturing Materials For Renewable Energy Applications

Marine renewable energy is a relatively young industry where there is a great need for rapid prototyping in design-build-test campaigns to quickly mature ground-breaking technologies. Additive manufacturing has an important role to play in the industry; however, little information is available to marine energy developers to help inform them on which additive manufacturing materials are appropriate for harsh marine environments. This paper presents an initial study on the mechanical characterization of polymeric additive manufacturing materials and degradation effects due to the marine environment. Ultem 9085, acrylonitrile styrene acrylate (ASA), and chopped carbon-filled Nylon, as well as continuous carbon and glass fiber reinforced Nylon were chosen for this study. Samples were manufactured to perform a variety of tension, shear, and compression mechanical characterization tests on the materials. Half of the samples were conditioned in Pacific Ocean water for approximately 6 months at the Pacific Northwest National Laboratory’s Marine and Coastal Research Laboratory before being returned for mechanical characterization. The mechanical testing results showed that the Ultem 9085 and ASA materials experienced little to no degradation in stiffness or strength after exposure to the marine environment. On the other hand, the Nylon-based materials suffered significant stiffness and strength degradation (over 50% in some cases) after environmental conditioning. Ultimately, these results should serve as starting points to allow marine renewable energy developers to make informed additive manufacturing material choices for their prototype deployments.

Murdy, Paul↗
DOE OSTI2022

Optimal Control of Biomass Feedstock Processing System Under Uncertainty in Biomass Quality

Planning of biorefinery operations is complicated by the stochastic nature of physical and chemical characteristics of biomass feedstock, such as, moisture level and carbohydrate content. Biomass characteristics affect the performance of the equipment which feed the reactor and the efficiency of the conversion process in a biorefinery. We propose a stochastic optimization model to identify a blend of feedstocks, inventory levels, and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor while meeting the requirements of the biochemical conversion process. We propose a sample average approximation (SAA) of the model, and develop an efficient algorithm to solve the SAA model. A feedstock preprocessing process consists of two-stage grinding and pelleting is used to develop a case study. Extensive numerical analysis are conducted which lead to a number of observations. Our main observation is that sequencing bales based on moisture level and carbohydrate content leads to robust solutions that improve processing time and processing rate of the reactor. We provide a number of managerial insights that facilitate the implementation of the model proposed. Note to Practitioners—This paper is motivated by the challenges faced in the bioenergy industry. The focus of this paper is on plants which use the biochemical conversion process to generate liquid fuels. It has been observed that variations in biomass characteristics, such as moisture content, cause variations in feeding of the system which lead to under-utilization of equipment. A requirement of biochemical conversion process is to maintain the carbohydrate content of biomass processed by the reactor, larger than a threshold. We propose a model that identifies the inventory levels and operating conditions of equipment to ensure a continuous flowing of biomass to the reactor. The goal is to improve equipment utilization while satisfying the requirements of the conversion process. The model is tested using real-life data. We found out that by sequencing bales based on moisture level and carbohydrate content, a plant can reduce variability in the system leading to improved system reliability, higher processing rates of the reactor, and higher throughput.

09 BIOMASS FUELS↗
DOE OSTI2021

Magnetic Nanoparticle Extraction of Lithium from Produced Waters: CRADA 483 [Abstract only]

In this project, we will synthesize, evaluate, and screen a set of new sorbents that have high capacity and selectivity for lithium. Sorbent performance will be evaluated by conducting Li extraction tests with produced water samples supplied by ConocoPhillips Company, Moselle Technologies, and Cascade Natural Resources. The best performing of these sorbents based on Li uptake capacity and selectivity will be produced as a magnetic nanoparticle and subjected to extended cycle testing in our laboratory bench-scale magnetic separator system. Moselle has acquired an exclusive license to the background IP associated with this magnetic nanoparticle mineral extraction technology and wishes to foster implementation of the technology in the oil & gas industry through this CRADA. Moselle will support PNNL in the design of a commercial-scale magnetic separator system tailored for lithium production. The goal is to collect sufficient information to support a decision by our industry partners to invest in a subsequent field demonstration at one of our partner’s field sites as a prerequisite to advancing this technology towards commercialization.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Response of soil bacteria to PUREX chemicals suggests biomarker utility and bioremediation potential

Chemicals involved in plutonium uranium reduction extraction (PUREX) can be released from nuclear reprocessing facilities and accumulate in the environment. We exposed chemically diverse soils to a range of concentrations of key chemicals used in the PUREX process. The responses of soil microbial communities are dependent on soil type, and tributyl phosphate exposure generates the most reproducible changes in microbial communities. We reconstructed the genomes of key bacteria and find several phosphotriesterase genes found only in Rhizobiaceae. The abundance of phosphotriesterase genes is significantly higher in samples exposed to tributyl phosphate. In conclusion, these phosphotriesterase genes may be involved in breakdown of tributyl phosphate, and a means of accessing phosphate for these bacteria.

Bioremediation↗
DOE OSTI2022

Machine learning-assisted upscaling analysis of reservoir rock core properties based on micro-computed tomography imagery

Optimum solutions for geologic modeling and reservoir simulation in industries such as oil and gas recovery and carbon capture and storage require accurate characterization of reservoir properties, which are often heterogeneous. In this study, high-quality micro-computed tomography (CT) images (1.475-μm/pixel resolution) of a sandstone core acquired from the Bell Creek oil field, USA, were used to provide nondestructive analysis of pore- and core-scale heterogeneity across measurement scales of 94–566 μm. In addition to characterizing the as-received sample, the core sample was flooded with brine to evaluate the capacity of the core sample to receive injected fluids. The micro-CT images were systematically segmented into pore spaces and grains via machine learning (ML) steps including image preprocessing, label creation using a traditional ML method based on limited manual image annotation, and finally U-Net segmentation. The segmented image stacks were reconstructed into digital cubes of various scales of voxel lengths. The 3D porosity values were calculated for all the digital cubes, and the fractal dimensions of the cubes were estimated using a box-counting method. The results showed that smaller cubes had greater heterogeneity and that the porosity values could be accurately estimated by fractal dimension and voxel lengths using ML models. For the core sample with brine flooding, the ratio of pores filled by brine to the total pore space was related to the porosity and could also be accurately estimated by porosity, fractal dimension, and voxel lengths using ML models. In conclusion, the results of this study demonstrate that the concept of fractal dimension can be a useful vector to perform upscaling analysis of sandstone rock heterogeneity from the pore to core scale and that fractal dimensions can be used to estimate porosity values and pore space-filling capacity across those scales.

58 GEOSCIENCES↗
DOE OSTI2023

A combinatorial droplet microfluidic device integrated with mass spectrometry for enzyme screening

Mass spectrometry (MS) enables detection of different chemical species with a very high specificity; however, it can be limited by its throughput. Integrating MS with microfluidics has a tremendous potential to improve throughput and accelerate biochemical research. In this work, we introduce Drop-NIMS, a combination of a passive droplet loading microfluidic device and a matrix-free MS laser desorption ionization technique called nanostructure-initiator mass spectrometry (NIMS). This platform combines different droplets at random to generate a combinatorial library of enzymatic reactions that are deposited directly on the NIMS surface without requiring additional sample handling. The enzyme reaction products are then detected with MS. Drop-NIMS was used to rapidly screen enzymatic reactions containing low (on the order of nL) volumes of glycoside reactants and glycoside hydrolase enzymes per reaction. MS “barcodes” (small compounds with unique masses) were added to the droplets to identify different combinations of substrates and enzymes created by the device. We assigned xylanase activities to several putative glycoside hydrolases, making them relevant to food and biofuel industrial applications. Overall, Drop-NIMS is simple to fabricate, assemble, and operate and it has potential to be used with many other small molecule metabolites.

47 OTHER INSTRUMENTATION↗
DOE OSTI2024

Sub-millisecond keyhole pore detection in laser powder bed fusion using sound and light sensors and machine learning

Laser powder bed fusion is a mainstream additive manufacturing technology widely used to manufacture complex parts in prominent sectors, including aerospace, biomedical, and automotive industries. However, during the printing process, the presence of an unstable vapor depression can lead to a type of defect called keyhole porosity, which is detrimental to the part quality. In this study, we developed an effective approach to locally detect the generation of keyhole pores during the printing process by leveraging machine learning and a suite of optical and acoustic sensors. Simultaneous synchrotron x-ray imaging allows the direct visualization of pore generation events inside the sample, offering high-fidelity ground truth. A neural network model adopting SqueezeNet architecture using single-sensor data was developed to evaluate the fidelity of each sensor for capturing keyhole pore generation events. Our comparative study shows that the near infrared images gave the highest prediction accuracy, followed by 100 kHz and 20 kHz microphones, and the photodiode sensitive to processing laser wavelength had the lowest accuracy. Using a single sensor, over 90% prediction accuracy can be achieved with a temporal resolution as short as 0.1 ms. A data fusion scheme was also developed with features extracted using SqueezeNet neural network architecture and classification using different machine learning algorithms. Our work demonstrates the correlation between the characteristic optical and acoustic emissions and the keyhole oscillation behavior, and thereby provides strong physics support for the machine learning approach.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Al-Ce Alloy-Based Compact Heat Exchanger for Refrigerant Charge Reduction and Unprecedented Durability (CRADA NFE-21-08888 Final Report)

Oak Ridge National Laboratory and Eck Industries produced and characterized durable and corrosion resistant Al-Ce-Mg alloy-based heat exchanger. Eck Industries successfully casted Al-Ce-Mg heat exchanger. ORNL characterized reaction bonding between Al-2Ce-6Mg alloy and stainless-steel tube in a heat exchanger header. Metallurgical bonds were achieved between stainless steel tubes and Al-Ce-Mg alloy cast headers. ORNL performed corrosion testing on Al-Ce-Mg alloy/stainless steel tube reactive bond interface. In most cases no significant changes to the reactive bond morphology or compositional distribution were observed in the samples after exposure to acid for 267h acid.

36 MATERIALS SCIENCE↗
DOE OSTI2024

Al-Ce Alloy-Based Compact Heat Exchanger for Refrigerant Charge Reduction and Unprecedented Durability

• Oak Ridge National Laboratory and Eck Industries produced and characterized durable and corrosion resistant Al-Ce-Mg alloy-based heat exchanger. • Eck Industries successfully casted Al-Ce-Mg heat exchanger. • ORNL characterized reaction bonding between Al-2Ce-6Mg alloy and stainless-steel tube in a heat exchanger header. • Metallurgical bonds were achieved between stainless steel tubes and Al-Ce-Mg alloy cast headers. • ORNL performed corrosion testing on Al-Ce-Mg alloy/stainless steel tube reactive bond interface. • In most cases no significant changes to the reactive bond morphology or compositional distribution were observed in the samples after exposure to acid for 267h acid.

36 MATERIALS SCIENCE↗
DOE OSTI2021

On the Limits of EM Based Detection of Control Logic Injection Attacks In Noisy Environments

The difficulty in applying traditional security mechanisms in Industrial Control System (ICS) environments makes a large portion of these mission-critical assets vulnerable to cyber attacks. Therefore, there is a dire need for the development of novel security mechanisms specifically designed to protect such critical systems. Recently a lot of attention has been given to mechanisms that exploit the EM emanations of devices for defense purposes. Such practices may lead to the development of robust external and non-intrusive anomaly detection systems. Nevertheless, the majority of current work in the area neglects to consider the implications of real-life environments, particularly environmental noise. In this work, we explore the limits of EM-based anomaly detection towards identifying injection attacks in control logic software in noisy environments. Our study conducted upon both synthetically generated and real signals identified that indeed environmental noise might significantly degrade the accuracy of the anomaly detection process. Experiments done upon synthetic data indicated that assuming that signals are captured with high sampling rates, even minor code injections can be detected with above-90% accuracy in noisy environments where SNR is up to -2dB. This is true even if naive detection methods are considered. Moreover, experiments done using a real-life testbed attest that even single-instruction injections can be detected with near-perfect accuracy in relatively clean environments. Finally, noise-elimination techniques can drastically improve the reliability of the detection mechanism even in noisy environments.

97 MATHEMATICS AND COMPUTING↗