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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 1,351 records · Page 75
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

WE-EF-207-03: Design and Optimization of a CBCT Head Scanner for Detection of Acute Intracranial Hemorrhage

Purpose: To design a dedicated x-ray cone-beam CT (CBCT) system suitable to deployment at the point-of-care and offering reliable detection of acute intracranial hemorrhage (ICH), traumatic brain injury (TBI), stroke, and other head and neck injuries. Methods: A comprehensive task-based image quality model was developed to guide system design and optimization of a prototype head scanner suitable to imaging of acute TBI and ICH. Previously reported models were expanded to include the effects of x-ray scatter correction necessary for detection of low contrast ICH and the contribution of bit depth (digitization noise) to imaging performance. Task-based detectablity index provided the objective function for optimization of system geometry, x-ray source, detector type, anti-scatter grid, and technique at 10–25 mGy dose. Optimal characteristics were experimentally validated using a custom head phantom with 50 HU contrast ICH inserts imaged on a CBCT imaging bench allowing variation of system geometry, focal spot size, detector, grid selection, and x-ray technique. Results: The model guided selection of system geometry with a nominal source-detector distance 1100 mm and optimal magnification of 1.50. Focal spot size ∼0.6 mm was sufficient for spatial resolution requirements in ICH detection. Imaging at 90 kVp yielded the best tradeoff between noise and contrast. The model provided quantitation of tradeoffs between flat-panel and CMOS detectors with respect to electronic noise, field of view, and readout speed required for imaging of ICH. An anti-scatter grid was shown to provide modest benefit in conjunction with post-acquisition scatter correction. Images of the head phantom demonstrate visualization of millimeter-scale simulated ICH. Conclusions: Performance consistent with acute TBI and ICH detection is feasible with model-based system design and robust artifact correction in a dedicated head CBCT system. Further improvements can be achieved with incorporation of model-based iterative reconstruction techniques also within the scope of the task-based optimization framework. David Foos and Xiaohui Wang are employees of Carestream Health.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Machine Learning to Select Experiments Driven by Fundamental Science and Applications for Targeted Nuclear Data Improvement

This work describes a blueprint for a process that accelerates progress in science by quantitatively answering the following question: What is the optimal combination of fundamental-science and application-driven experiments to maximally reduce pertinent data uncertainties? Answering this question entails solving a high-dimensional and complex optimization problem that is best solved with advanced statistic techniques often classified as machine learning. We apply this process within the framework of nuclear data with the aim to select an experiment combination that will reduce uncertainties in 239 Pu nuclear data for neutron energies between 1 and 600 keV. In this field, fundamental-physics driven data, called differential, look at one nuclear physics observable at a time. They are contrasted to application-driven, integral, data where one or few resulting values inform a broad set of nuclear data across several nuclides and energies. The candidates for integral experiments are criticality measurements that were refined by a genetic algorithm to be maximally sensitive to 239 Pu fission cross sections in the desired energy range. Twenty-three candidate differential experiments were investigated and span multiple nuclear physics observables (e.g., total, capture cross sections) for isotopes appearing in the integral experiments. The optimal combination among these candidate experiments was investigated via generalized least squares fitting, augmented with Gaussian processes to ameliorate statistical irregularities in data, and the D-optimality criterion. The latter evaluates for each pair of candidates the joint reduction in uncertainties of all 12200 nuclear data appearing in the integral experiments compared to the knowledge we have from 168 past experiments, theory, and nuclear data. We chose as differential measurements those that investigate 63 Cu and 239 Pu total cross sections, based on D-optimality rank and feasibility constraints. Two integral (criticality) experiments were selected: An experiment with Al 2 ⁢O 3 and graphite interleaved with Pu and a thick Cu reflector explores 1–30 keV, while we target the 30–600 keV range with an experiment that swaps boron in place of graphite with a different geometry.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2020

Overcoming Fundamental Limitations in Adsorbent Design: Alkene Adsorption by Non-porous Copper(I) Complexes

Purifying alkenes from alkanes requires cryogenic distillation. This consumes energy equivalent to countries of ca. 5 million people. Replacing distillation with adsorption processes would significantly increase energy efficiency. Trade-offs between kinetics, selectivity, capacity, and heat of adsorption have prevented production of an optimal adsorbent. Here, we report adsorbents that overcome these trade-offs. [Cu-Br] 3 and [Cu-H] 3 are air-stable trinuclear complexes that undergo reversible solid-state inter-molecular rearrangements to produce dinuclear [Cu-Br∙(alkene)] 2 and [Cu-H∙(alkene)] 2 . The reversible solid-state rearrangement, confirmed in situ using powder X-ray diffraction, allows adsorbent design trade-offs to be overcome, coupling low heat of adsorption (-10 to -17 kJ mol -1 alkene ), high alkene:alkane selectivity (47; 29), and uptake capacity (>2.5 molalkene mol -1 Cu3 ). Most remarkably, [Cu-H] 3 displays fast uptake and regenerates capacity within 10 minutes.

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

Hybrid power plant design for low-carbon hydrogen in the United States

In this study, we provide a nationwide techno-economic analysis of clean hydrogen production powered by a hybrid renewable energy plant for over 50,000 locations in the United States. We leverage the open-source Hybrid Optimization Performance Platform (HOPP) tool to simulate the hourly performance of an off-grid wind-solar plant integrated with a 1-GW polymer exchange membrane electrolyzer system. The levelized cost of hydrogen is calculated for varying technology costs, and tax credits to explore cost sensitivities independent of plant design, performance, and site selection. Our findings suggest that strategies for cost reduction include selecting sites with abundant wind resources, complementary wind and solar resources, and optimizing the sizing of wind and solar assets to maximize the hybrid plant capacity factor. These strategies are linked to increased hydrogen production and reduced electrolyzer stack replacements, thereby lowering the overall cost of hydrogen.

08 HYDROGEN↗
DOE OSTI2026

Selective designer hydrolysates from Miscanthus bioenergy crop for sustainable utilization

Designer enzymatic hydrolysates deliver targeted, optimized fermentable sugars while reducing the formation of inhibitory by-products. Herein, five distinct enzymatic hydrolysates (EH) were generated from Miscanthus biomass using alkaline-ethanol fractionation and hydrothermal-mechanical refining (HMR) pretreatment approaches. Noticeably, alkali-ethanol fractionated hemicellulose A (HMA) and hemicellulose B (HMB) hydrolysis resulted in xylose-rich enzymatic hydrolysates with 104.8 ± 5.6 g L −1 and 173.4 ± 4.6 g L −1 xylose, respectively. All five hydrolysates encompassing varied carbon to nitrogen ratios were evaluated for lipid production using an engineered oleaginous yeast, R. toruloides 880-ADS. Microbial fermentation of xylose-rich hydrolysates, viz. HMR-EH2, HMA-EH, and HMB-EH, resulted in lipid yields ranging from 0.05 to 0.12 g g −1 sugar. Compositional and 2D NMR analyses revealed that HMA-EH residues are highly lignin-enriched while preserving the non-condensed structure conducive to valorization. Overall, the findings highlighted a novel, selective trade-off approach for maximizing lipid titers from designer hydrolysates, corroborating complete resource recovery for establishing integrated biorefineries.

Singh, Shuchi [University of Illinois at Urbana-Ch↗
DOE OSTI2020

Multiplexed Knockouts in the Model Diatom Phaeodactylum by Episomal Delivery of a Selectable Cas9

Marine diatoms are eukaryotic microalgae that play significant ecological and biogeochemical roles in oceans. They also have significant potential as organismal platforms for exploitation to address biotechnological and industrial goals. In order to address both modes of research, sophisticated molecular and genetic tools are required. We presented here new and improved methodologies for introducing CRISPRCas9 to the model diatom Phaeodactylum tricornutum cells and a streamlined protocol for genotyping mutant cell lines with previously unknown phenotypes. First, bacterialconjugation was optimized for the delivery of Cas9 by transcriptionally fusing Cas9 to a selectable marker by the 2A peptide. An episome cloning strategy using both negative and positive selection was developed to streamline CRISPR-episome assembly. Next, cell line picking and genotyping strategies, that utilize manual sequencing curation, TIDE sequencing analysis, and a T7 endonuclease assay, were developed to shorten the time required to generate mutants. Following this new experimental pipeline, both singlegene and two-gene knockout cell lines were generated at mutagenesis efficiencies of 48% and 25%, respectively. Lastly, a protocol for precise gene insertions via CRISPRCas9 targeting was developed using particle-bombardment transformation methods. Overall, the novel Cas9 episome design and improved genotyping methods presented here allow for quick and easy genotyping and isolation of Phaeodactylum mutant cell lines (less than 3 weeks) without relying on a known phenotype to screen for mutants.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Qualitative trend analysis based on a mixed-integer representation

Shape constrained spline fitting is a useful method to impose prior knowledge onto flexible semi-parametric models during parameter estimation. Most typically, the function shape is imposed through order restrictions on the regression coefficients. The intended shape is considered known or selected based on heuristic rules. In this study, we present a method to estimate the optimal set of order restrictions to segment a univariate data series into episodes with distinct shapes. This is also known as the qualitative trend analysis (QTA) problem. The obtained solution uses a trade-off between lack-of-fit and model complexity. Further, our practical implementation takes inspiration from the generalized order restricted information criterion (GORIC) for inequality-constrained model selection. From this, one learns (a) that QTA can be formulated as a mixed-integer quadratic program (MIQP) and (b) that the newly proposed mixed order restricted information criterion (MORIC) enables optimal segmentation. This is illustrated through didactic case studies.

42 ENGINEERING↗
DOE OSTI2019

Model-based Hyperparameter Optimization of Convolutional Neural Networks for Information Extraction from Cancer Pathology Reports on HPC

Finding optimal hyperparameters is necessary to identify the best performing deep learning models but the process is costly. In this paper, we applied model-based optimization, also known as Bayesian optimization, using the CANDLE framework implemented on a High-Performance Computing environment. As a use case we selected information extraction from cancer pathology reports using a multi-task convolutional neural network, and hierarchical convolutional attention network to be optimized. We utilized a synthesized text corpus of 8,000 training cases and 2,000 validation cases with four types of clinical task labels including primary cancer site, laterality, behavior, and histological grade. We demonstrated that hyperparameter optimization using the CANDLE framework is a feasible approach with respect to both scalability and clinical task performance.

Yoon, Hong-Jun↗
DOE OSTI2016

NREL Evaluates Performance of Fast-Charge Electric Buses

This real-world performance evaluation is designed to enhance understanding of the overall usage and effectiveness of electric buses in transit operation and to provide unbiased technical information to other agencies interested in adding such vehicles to their fleets. Initial results indicate that the electric buses under study offer significant fuel and emissions savings. The final results will help Foothill Transit optimize the energy-saving potential of its transit fleet. NREL's performance evaluations help vehicle manufacturers fine-tune their designs and help fleet managers select fuel-efficient, low-emission vehicles that meet their bottom line and operational goals. help Foothill Transit optimize the energy-saving potential of its transit fleet. NREL's performance evaluations help vehicle manufacturers fine-tune their designs and help fleet managers select fuel-efficient, low-emission vehicles that meet their bottom line and operational goals.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2025

Optimizing CO 2 -Loaded Aqueous Amine Solutions for Higher Electrocatalytic CO 2 Reduction Activity

The activity of aqueous-based carbon dioxide reduction (CO 2 R) reactions is often limited by the solubility of CO 2 . The addition of amines can increase the total dissolved carbon in water through the formation of bicarbonate and carbamate species, which has been used to a great effect to capture CO 2 from dilute streams. Here, in this study, we explore the effect of 12 primary and secondary amines of varying Brønsted basicity, steric profile, and hydrogen-bonding capabilities on the aqueous CO 2 R to CO activity of a molecular Ni(cyclam)Cl 2 catalyst with a Hg electrode. Addition of some of the amines results in greater activity and selectivity for CO production compared to equivalent aqueous solutions without added amines. Under optimal conditions (0.4 M 3-amino-propionitrile), there is an over sevenfold increase in partial current density and greater selectivity for CO compared to equivalent conditions with no amine. Interestingly, the increase in activity did not correlate to any single property across the 12 amines. To elucidate the effect of the amine additives on catalysis, we used vapor–liquid equilibrium modeling (VLE), 13 C NMR spectroscopy, and computational analysis to determine the carbon speciation of the solutions. These results indicate that for amines without ethylalcohol functionalities, CO 2 R activity correlates with carbamate concentration, which is in turn governed by amine basicity and steric effects. However, this correlation does not persist for amines with ethylalcohol functionalities, which can form more stable carbamates through intramolecular-hydrogen bonding. These studies demonstrate that amine additives can enhance aqueous CO 2 R activity and selectivity and describe amine properties that lead to these higher performance metrics.

Amines↗
DOE OSTI2021

Dynamically Unveiling Metal–Nitrogen Coordination during Thermal Activation to Design High‐Efficient Atomically Dispersed CoN 4 Active Sites

Abstract We elucidate the structural evolution of CoN 4 sites during thermal activation by developing a zeolitic imidazolate framework (ZIF)‐8‐derived carbon host as an ideal model for Co 2+ ion adsorption. Subsequent in situ X‐ray absorption spectroscopy analysis can dynamically track the conversion from inactive Co−OH and Co−O species into active CoN 4 sites. The critical transition occurs at 700 °C and becomes optimal at 900 °C, generating the highest intrinsic activity and four‐electron selectivity for the oxygen reduction reaction (ORR). DFT calculations elucidate that the ORR is kinetically favored by the thermal‐induced compressive strain of Co−N bonds in CoN 4 active sites formed at 900 °C. Further, we developed a two‐step (i.e., Co ion doping and adsorption) Co‐N‐C catalyst with increased CoN 4 site density and optimized porosity for mass transport, and demonstrated its outstanding fuel cell performance and durability.

He, Yanghua↗
DOE OSTI2017

Morphology of a Ternary Blend Solar Cell Based on Small Molecule:Conjugated Polymer:Fullerene Fabricated by Blade Coating

Abstract Here, conjugated polymer is added as third component to tune the solution viscosity, morphology, and function of small molecule (SM) based bulk‐heterojunction (BHJ) solar cells, which are fabricated using blade coating. Novel information about the effect of blade coating speed on the nanoscale morphology and function of ternary blend solar cells is provided. The crystal sizes increase with an increase of coating speed for both binary and ternary blends, while the addition of the third component tends to favor smaller SM crystal grains and improves the connectivity of SM crystals. Small angle neutron scattering experiments provide the first clear experimental evidence that the addition of the third component would significantly impact the fullerene phase separation, which is crucial for bimolecular recombination and charge transport. It shows that for both binary and ternary blends, the concentration and sizes of [6,6]‐phenyl‐C61‐butyric acid methyl ester (PCBM) aggregates increase with an increase of coating speed, while addition of third component does not affect the volume fraction of PCBM aggregates but impacts the size of PCBM aggregates. It is demonstrated that the judicious selection of blade coating speed and addition of conjugated polymer optimize the morphology of SM‐BHJ, providing guidelines for high performance SM‐BHJs from roll‐to‐roll production.

Wu, Xiaomin↗
DOE OSTI2019

Phase Equilibria in the TiO{sub 2}-Rich Part of the TiO{sub 2}-CaO-SiO{sub 2}-10 Wt Pct Al{sub 2}O{sub 3}-5 Wt Pct MgO System at 1773 K

The phase equilibria of the TiO{sub 2}-CaO-SiO{sub 2}-10 wt pct Al{sub 2}O{sub 3}-5 wt pct MgO system in the TiO{sub 2}-rich part were investigated experimentally at 1773 K using the high-temperature equilibration–quenching method and the electron probe microanalyses. Through this study, the isothermal section of the above system was obtained containing one liquidus region and five solid–liquid coexistence regions. The primary phase region of anosovite was not only found, but it is the optimal composition region that is most beneficial to the anosovite selective crystallization and separation. The phase diagram of the TiO{sub 2}-CaO-SiO{sub 2}-10 wt pct Al{sub 2}O{sub 3}-5 wt pct MgO system in the TiO{sub 2}-rich corner at 1773 K has been established. The obtained phase diagram can provide reliable reference for the slag adjusting during vanadium-titanium magnetites smelting to make sure the resulting Ti-bearing slag can be upgraded using the selective crystallization and phase separation method.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Determination of Strain Rate Sensitivity of Micro-struts Manufactured Using the Selective Laser Melting Method

Micro-lattice structures manufactured using the selective laser melting (SLM) process provides the opportunity to realize optimal cellular materials for impact energy absorption. In this paper, strain rate-dependent material properties are measured for stainless steel 316L SLM micro-lattice struts in the strain rate range of 10{sup −3} to 6000 s{sup −1}. At high strain rates, a novel version of the split Hopkinson Bar has been developed. Strain rate-dependent materials data have been used in Cowper–Symonds material model, and the scope and limit of this model in the context of SLM struts have been discussed. Strain rate material data and the Cowper–Symonds model have been applied to the finite element analysis of a micro-lattice block subjected to drop weight impact loading. The model output has been compared to experimental results, and it has been shown that the increase in crush stress due to impact loading is mainly the result of strain rate material behavior. Hence, a systematic methodology has been developed to investigate the impact energy absorption of a micro-lattice structure manufactured using additive layer manufacture (SLM). This methodology can be extended to other micro-lattice materials and configurations, and to other impact conditions.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Microwave-assisted co-gasification of mixed plastics and corn stover: A synergistic approach to produce clean hydrogen

Co-gasification of waste plastic and corn stover into hydrogen is an effective way of reducing hydrogen cost and carbon footprint. However, current gasification technologies are energy intensive and cannot accommodate flexible feedstock. Microwave gasification offers several advantages including higher H 2 yield and enhanced selectivity to syngas over tars. Herein this work presents parametric optimization study of microwave co-gasification of mixed plastics and corn stover for hydrogen-rich syngas. High hydrogen yields of 30.5 mmol H2 /g feed at 700 °C were obtained using a microwave reactor, as opposed to 0.9 mmolH 2 /g feed at 700-950 °C with conventional heating. Hydrogen yields are >98% of the theoretical extractable hydrogen from feedstock. Microwaves enhanced plastic and corn stover synergy and reduced tar yields. H-poor carbon formation resulting from the dehydrogenation of tar and coke, and light olefins decomposition, triggered by the external microwave electric fields, allowed further cracking of intermediate hydrocarbons to produce additional H 2 .

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2026

Carbon dioxide pipeline network transportation cost model: evaluating economic and geographic factors for efficient carbon capture, storage, and utilization

This study presents a comprehensive pipeline network modeling framework to estimate the CO 2 delivery cost for CO 2 utilization and geologic CO 2 storage across the United States. We developed a Python-based CO 2 pipeline transportation cost model leveraging Argonne National Laboratory’s pipeline engineering expertise and detailed natural gas transmission pipeline cost data across U.S. regions. Using existing road corridors as practical routing guides, the model designs pipeline networks that aggregate CO 2 from one or multiple sources and deliver it to selected destinations. It then minimizes the total transportation cost by optimizing pipeline diameters and incorporating booster pumps. A key contribution is the incorporation of up-to-date, region-specific cost factors with itemized components for materials, labor, miscellaneous construction expenses, and right-of-way acquisition. Results emphasize that regional variation and economies of scale associated with CO 2 pipeline costs are significant and should be explicitly accounted for in screening and planning studies. By combining realistic routing constraints with regionalized cost inputs, the model provides transparent design methodology and location-specific insights into source–destination delivery costs, including the effects of routing complexity along existing road networks. We demonstrate the model with two illustrative case studies – one for CO 2 storage and one for CO 2 utilization – in which the model designs pipeline networks spanning hundreds of miles across the states, collecting CO 2 from multiple sources and delivering it to designated endpoints while minimizing levelized cost of delivery via diameter and compression optimization. The model offers a practical, scalable approach for alternative design option screening and early-stage CO 2 transportation planning.

CCS↗
DOE OSTI2024

Modeling plasticity-mediated void growth at the single crystal scale: A physics-informed machine learning approach

Modeling the evolution of voids during plastic flow as well as their effects on plastic dissipation is critical for both component manufacturing and lifetime estimation purposes. To this end, we propose a rate-dependent constitutive model to homogenize the effects of semi-randomly distributed voids on single crystal plasticity whilst capturing void interaction and plastic anisotropy. Here, this present work focuses on the case of face centered cubic crystals to introduce an anisotropic gauge function applicable within the crystal plasticity formalism. The approach combines analytical methods to describe the micromechanics of the system in combination with symbolic regression to capture analytically intractable mechanisms from data. The hybrid framework uses a physics-informed genetic programming-based symbolic regression algorithm to solve a multiform optimization problem simultaneously producing a new gauge function and a new strain rate equation. This is also a multi-objective optimization problem with many competing objectives. A new search and selection step is introduced to the genetic algorithm that promotes convergence toward a global solution that better satisfies all the objectives. Overall, the symbolic equations produced leverage data-driven methods to achieve greater accuracy than comparable alternatives on an analytically intractable problem while maintaining model transparency.

36 MATERIALS SCIENCE↗