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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 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Amorphous Iridium Oxide-Integrated Anode Electrodes with Ultrahigh Material Utilization for Hydrogen Production at Industrial Current Densities

Herein, ionomer-free amorphous iridium oxide (IrO x ) thin electrodes are first developed as highly active anodes for proton exchange membrane electrolyzer cells (PEMECs) via low-cost, environmentally friendly, and easily scalable electrodeposition at room temperature. Combined with a Nafion 117 membrane, the IrO x -integrated electrode with an ultralow loading of 0.075 mg cm –2 delivers a high cell efficiency of about 90%, achieving more than 96% catalyst savings and 42-fold higher catalyst utilization compared to commercial catalyst-coated membrane (2 mg cm –2 ). Additionally, the IrO x electrode demonstrates superior performance, higher catalyst utilization and significantly simplified fabrication with easy scalability compared with the most previously reported anodes. Notably, the remarkable performance could be mainly due to the amorphous phase property, sufficient Ir 3+ content, and rich surface hydroxide groups in catalysts. Overall, due to the high activity, high cell efficiency, an economical, greatly simplified and easily scalable fabrication process, and ultrahigh material utilization, the IrO x electrode shows great potential to be applied in industry and accelerates the commercialization of PEMECs and renewable energy evolution.

08 HYDROGEN↗
DOE OSTI2020

Prediction of condition-specific regulatory genes using machine learning

Recent advances in genomic technologies have generated data on large-scale protein–DNA interactions and open chromatin regions for many eukaryotic species. How to identify condition-specific functions of transcription factors using these data has become a major challenge in genomic research. To solve this problem, we have developed a method called ConSReg, which provides a novel approach to integrate regulatory genomic data into predictive machine learning models of key regulatory genes. Using Arabidopsis as a model system, we tested our approach to identify regulatory genes in data sets from single cell gene expression and from abiotic stress treatments. Our results showed that ConSReg accurately predicted transcription factors that regulate differentially expressed genes with an average auROC of 0.84, which is 23.5–25% better than enrichment-based approaches. To further validate the performance of ConSReg, we analyzed an independent data set related to plant nitrogen responses. ConSReg provided better rankings of the correct transcription factors in 61.7% of cases, which is three times better than other plant tools. We applied ConSReg to Arabidopsis single cell RNA-seq data, successfully identifying candidate regulatory genes that control cell wall formation. Our methods provide a new approach to define candidate regulatory genes using integrated genomic data in plants.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1979

Fermi liquid viscosity in a finite geometry

Forced flow of a Fermi liquid is studied for a cell geometry consisting of two planes with a separation on the order of the mean free path. An approximate transport equation is used to derive an integral equation for the velocity profile, which is solved numerically. Results for the total flux through the cell, which determines the dissipation, are given as a function of the Knudsen number kappa (ratio of cell thickness to mean free path). Effects of specular reflection at the boundaries are considered. It is found that the dissipation has a minimum at kappa of 1/2, and behaves linearly for kappa not less than 3. Implications for present experimentation are discussed.

Jaffe, J. E.↗
DOE OSTI2022

ARIES/Flatirons Facility - Hydrogen System Capability Buildout

Under the "Advanced Research on Integrated Energy Systems" (ARIES) initiative, hydrogen system capabilities including a MW-scale electrolyzer, storage system, and MW-scale fuel cell generator will be designed and commissioned at NREL's Flatirons Campus. This hydrogen infrastructure will support H2@Scale goals by enabling integrated systems R&D to study the science of scaling for hydrogen energy systems. The system is designed with flexibility to provide a testbed to demonstrate systems integration, grid services, energy storage, direct renewable hydrogen production, and innovative end use applications (e.g. HD transportation, natural gas blending, etc.).

DIRECT ENERGY CONVERSION,HYDROGEN↗
DOE OSTI2022

A performance comparison between GaInP-on-Si and GaAs-on-Si 3-terminal tandem solar cells

The pursuit of ever-higher solar cell efficiencies has focused heavily on multijunction technologies. In tandem cells, subcells are typically either contacted via two terminals (2T) or four terminals (4T). Simulations show that the less-common three-terminal (3T) design may be comparable to 4T tandem cells in its compatibility with a range of materials, operating conditions, and methods for subcell integration, yet the 3T design circumvents shading losses of the 4T intermediate conductive layers. This study analyzes the performance of two superstrate 3T III-V-on-Si (III-V//Si) tandem cells: One has slightly greater current contribution from the Si bottom cell (GaInP//Si) and the other has substantially greater current contribution from the GaAs top cell (GaAs//Si). Our results show that both tandem cells exhibit the same efficiency (21.3%), thereby demonstrating that the third terminal allows for flexibility in the selection of the top cell material, similar to the 4T design.

14 SOLAR ENERGY↗
DOE OSTI2023

Optimization of nutrient utilization efficiency and productivity for algal cultures under light and dark cycles using genome-scale model process control

Abstract Algal cultivations are strongly influenced by light and dark cycles. In this study, genome-scale metabolic models were applied to optimize nutrient supply during alternating light and dark cycles of Chlorella vulgaris . This approach lowered the glucose requirement by 75% and nitrate requirement by 23%, respectively, while maintaining high final biomass densities that were more than 80% of glucose-fed heterotrophic culture. Furthermore, by strictly controlling glucose feeding during the alternating cycles based on model-input, yields of biomass, lutein, and fatty acids per gram of glucose were more than threefold higher with cycling compared to heterotrophic cultivation. Next, the model was incorporated into open-loop and closed-loop control systems and compared with traditional fed-batch systems. Closed-loop systems which incorporated a feed-optimizing algorithm increased biomass yield on glucose more than twofold compared to standard fed-batch cultures for cycling cultures. Finally, the performance was compared to conventional proportional-integral-derivative (PID) controllers. Both simulation and experimental results exhibited superior performance for genome-scale model process control (GMPC) compared to traditional PID systems, reducing the overall measured value and setpoint error by 80% over 8 h. Overall, this approach provides researchers with the capability to enhance nutrient utilization and productivity of cell factories systematically by combining genome-scale models and controllers into an integrated platform with superior performance to conventional fed-batch and PID methodologies.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Verification of an improved equation-free projective integration method for neoclassical plasma-profile evolution in tokamak geometry

A brute-force, long-time gyrokinetic simulation of plasma profile evolution in magnetic fusion devices is not desirable due to large computational resource requirements and a possible accumulation of numerical error. The equation-free projective integration method of Keverekidis et al. [Commun. Math. Sci. 1(4), 715–762 (2003)] is one of the outstanding candidates in projecting micro-scale simulations to a longer timescale. However, its application to tokamak plasma has not been fruitful due to the appearance of spurious transient oscillations in the lifting process, which are present when the kinetic simulations are initialized with a simplified model distribution function and which make the kinetic simulations to deviate from the desired paths. In this work, a kinetically informed lifting algorithm is added to the equation-free projective integration method, which is then verified in the electrostatic gyrokinetic particle-in-cell code XGCa [R. Hager and C. S. Chang, Phys. Plasmas 23, 042503 (2016)] for a neoclassical ion heat transport problem with adiabatic electrons. This new lifting operator is demonstrated to control spurious transients, enabling an over four-times reduction in the overall computing time in the time-evolution of the ion temperature profile in an axisymmetric toroidal plasma. Further reduction in the computing time is found to be limited due to the stability properties of the linear least squares projective integrator.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2026

N -Terminal Octylated Peptoid Hydrogels as 3D-Printable Cell Scaffolds and Proteolytically Robust Cargo Depots

Supramolecular hydrogels that mimic the extracellular matrix (ECM) represent promising materials for tissue engineering and drug delivery. However, conventional hydrogels formed via the self-assembly of natural or synthetic building blocks often face a trade-off between biological functionality and biochemical stability, limiting their utility in long-term or protease-rich environments. Peptoids, a class of peptide-inspired, sequence-defined polymers, offer a compelling alternative due to their exceptional proteolytic resistance and bioactivity. Despite this potential, the development of supramolecular peptoid hydrogels has been hindered by the absence of backbone hydrogen bond donors, which limits long-range ordering necessary for efficient hydrogel formation. This work describes a short peptoid functionalized at the N-terminus with an octyl chain that readily self-assembles into hydrogels. Hydrophobic interactions among pendant octyl groups promote directional peptoid packing into highly ordered nanosheets, which interconnect to form a porous hydrogel network. These hydrogels exhibit tunable viscoelasticity, shear-thinning, and self-healing properties, enabling their use as inks for extrusion-based 3D printing. They support NIH-3T3 fibroblast adhesion, spreading, and proliferation, maintaining greater than 95% cell viability over 4 days. Moreover, the hydrogels retain their macroscopic integrity under protease-rich conditions, enabling sustained cargo release and uniform cellular uptake. Together, this study demonstrates a class of supramolecular peptoid hydrogelators that integrate biocompatibility, 3D printability, and proteolytic stability, providing a versatile platform for ECMmimetic scaffolds in regenerative medicine and long-term therapeutic delivery.

cargo delivery↗
DOE OSTI2021

Reactor cell neutron dose for the molten salt breeder reactor conceptual design

The private sector’s interest in the active development of molten salt reactors has led to the need to develop and test advanced modeling and simulation tools to analyze various advanced reactor types under numerous conditions. This paper discusses the effort undertaken to model the Oak Ridge National Laboratory (ORNL) Molten Salt Breeder Reactor (MSBR) design using ORNL’s Shift Monte Carlo code. The MSBR model integrates a Monte Carlo N-Particle (MCNP) MSBR core model with an MCNP model that was generated from a CAD model of the external components and the reactor building, which was subsequently run in Shift. This paper focuses on development of the fully integrated model and its use in performing neutron transport calculations in the reactor cell area. This model is intended to aid in understanding radiological dose conditions during operation, as well as the iron dpa rates in the reactor vessel. The neutron biological dose rates and flux calculated in the reactor cell are much higher in the MSBR than in typical light-water reactors. The implications of these results and future work are also discussed in this paper.

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
DOE OSTI2024

Machine learning-guided design of direct methanol fuel cells with a platinum group metal-free cathode

Direct methanol fuel cells (DMFCs) offer a promising solution for clean electricity generation, particularly in small electronics and remote auxiliary power units. However, optimizing their efficiency and performance is challenging due to the complex interactions between various factors. Here, we present a novel approach that integrates experiments with machine learning to model and predict the performance of these fuel cells using atomically dispersed platinum group metal (PGM)-free catalysts at the cathode. Further, our machine learning models, trained on diverse input parameters, allow for the comprehensive optimization of DMFC performance prior to fabrication and testing. Through extensive experimental validation, we demonstrate that this data-driven approach accurately predicts key performance metrics, such as maximum power output and polarization curves. By combining our models with interpretable game-theory methods, we provide deep insights into the factors governing fuel cell performance, ultimately paving the way for the design of scalable and efficient DMFC technologies.

25 ENERGY STORAGE↗
DOE OSTI2024

Hydrothermal conditioning of oleaginous yeast cells to enable recovery of lipids as potential drop-in fuel precursors

Abstract Background Lipids produced using oleaginous yeast cells are an emerging feedstock to manufacture commercially valuable oleochemicals ranging from pharmaceuticals to lipid-derived biofuels. Production of biofuels using oleaginous yeast is a multistep procedure that requires yeast cultivation and harvesting, lipid recovery, and conversion of the lipids to biofuels. The quantitative recovery of the total intracellular lipid from the yeast cells is a critical step during the development of a bioprocess. Their rigid cell walls often make them resistant to lysis. The existing methods include mechanical, chemical, biological and thermochemical lysis of yeast cell walls followed by solvent extraction. In this study, an aqueous thermal pretreatment was explored as a method for lysing the cell wall of the oleaginous yeast Rhodotorula toruloides for lipid recovery. Results Hydrothermal pretreatment for 60 min at 121 °C with a dry cell weight of 7% (w/v) in the yeast slurry led to a recovery of 84.6 ± 3.2% (w/w) of the total lipids when extracted with organic solvents. The conventional sonication and acid-assisted thermal cell lysis led to a lipid recovery yield of 99.8 ± 0.03% (w/w) and 109.5 ± 1.9% (w/w), respectively. The fatty acid profiles of the hydrothermally pretreated cells and freeze-dried control were similar, suggesting that the thermal lysis of the cells did not degrade the lipids. Conclusion This work demonstrates that hydrothermal pretreatment of yeast cell slurry at 121 °C for 60 min is a robust and sustainable method for cell conditioning to extract intracellular microbial lipids for biofuel production and provides a baseline for further scale-up and process integration. Graphical abstract

09 BIOMASS FUELS↗
NASA NTRS1977

Integral glass encapsulation for solar arrays

Electrostatic bonding has been used to join silicon solar cells to borosilicate glass without the aid of any organic binders or adhesives. The results of this investigation have been to demonstrate, without question, the feasibility of this process as an encapsulation technique. The potential of ESB for terrestrial solar arrays was clearly shown. The process is fast, reproducible, and produces a permanent bond between glass and silicon that is stronger than the silicon itself. Since this process is a glass sealing technique requiring no organics it makes moisture tight sealing of solar cells possible.

Young, P. R.↗
NASA NTRS1987

Software for integrated manufacturing systems, part 1

For several years, a broad, unified approach to programming manufacturing cells, factory floors, and other manufacturing systems has been developed. It is based on a blending of distributed Ada, software components, generics and formal models. Among other things the machines and devices which make up the components, and the entire manufacturing cell is viewed as an assembly of software components. The purpose of this project is to reduce cost, increase the reliability and increase the flexibility of manufacturing software. An overview is given of the approach and an experimental generic factory floor controller that was developed using the approach is described. The controller is generic in the sense that it can control any one of a large class of factory floors making an arbitrary mix of parts.

Naylor, A. W.↗
DOE OSTI2025

Chromatin structures from integrated AI and polymer physics model

The physical organization of the genome in three-dimensional space regulates many biological processes, including gene expression and cell differentiation. Three-dimensional characterization of genome structure is critical to understanding these biological processes. Direct experimental measurements of genome structure are challenging; computational models of chromatin structure are therefore necessary. We develop an approach that combines a particle-based chromatin polymer model, molecular simulation, and machine learning to efficiently and accurately estimate chromatin structure fromindirectmeasures of genome structure. More specifically, we introduce a new approach where the interaction parameters of the polymer model are extracted from experimental Hi-C data using a graph neural network (GNN). We train the GNN on simulated data from the underlying polymer model, avoiding the need for large quantities of experimental data. The resulting approach accurately estimates chromatin structures across all chromosomes and across several experimental cell lines despite being trained almost exclusively on simulated data. The proposed approach can be viewed as a general framework for combining physical modeling with machine learning, and it could be extended to integrate additional biological data modalities. Ultimately, we achieve accurate and high-throughput estimations of chromatin structure from Hi-C data, which will be necessary as experimental methodologies, such as single-cell Hi-C, improve.

Biochemistry & Molecular Biology↗
NASA NTRS1994

Cell Libraries

A NASA contract led to the development of faster and more energy efficient semiconductor materials for digital integrated circuits. Gallium arsenide (GaAs) conducts electrons 4-6 times faster than silicon and uses less power at frequencies above 100-150 megahertz. However, the material is expensive, brittle, fragile and has lacked computer automated engineering tools to solve this problem. Systems & Processes Engineering Corporation (SPEC) developed a series of GaAs cell libraries for cell layout, design rule checking, logic synthesis, placement and routing, simulation and chip assembly. The system is marketed by Compare Design Automation.

Source record↗
DOE OSTI2023

Low Impedance Cathode/Electrolyte Interfaces for High Energy Density Solid-State Batteries

All-solid-state batteries (ASSBs) using a ceramic fast Li-ion conductor as a solid-state electrolyte (SSE) have been proposed as a promising strategy to significantly increase the energy density of lithium batteries. Due to their high ion conductivity and excellent stability, Li-stuffed garnets exhibit the most promising physical and chemical properties for SSEs. However, the typical microstructure, thick (>100 μm) bulk electrolyte and simple planar electrode/electrolyte interfaces, combined with poor electrode wetting of the garnet result in excessively high area specific resistances (ASRs) that severely limit achievable current density and cell energy density. In this project, the team is building on their demonstrated expertise with garnet electrolytes and ASSBs to accomplish the following: (1) engineer interfaces to overcome high NMC/LLZ interfacial impedance and interfacial degradation; (2) develop processing and fabrication techniques to achieve high loading NMC/LLZ composite cathodes with low resistance and high cyclability; and (3) integrate the NMC/LLZ cathodes into all-solid-state Li-metal/LLZ cells to achieve high-energy-density batteries.

25 ENERGY STORAGE↗
NASA NTRS2012

Thermally Resilient, Broadband Optical Absorber from UV to IR Derived from Carbon Nanostructures

Optical absorber coatings have been developed from carbon-based paints, metal blacks, or glassy carbon. However, such materials are not truly black and have poor absorption characteristics at longer wavelengths. The blackness of such coatings is important to increase the accuracy of calibration targets used in radiometric imaging spectrometers since blackbody cavities are prohibitively large in size. Such coatings are also useful potentially for thermal detectors, where a broadband absorber is desired. Au-black has been a commonly used broadband optical absorber, but it is very fragile and can easily be damaged by heat and mechanical vibration. An optically efficient, thermally rugged absorber could also be beneficial for thermal solar cell applications for energy harnessing, particularly in the 350-2,500 nm spectral window. It has been demonstrated that arrays of vertically oriented carbon nanotubes (CNTs), specifically multi-walled-carbon- nanotubes (MWCNTs), are an exceptional optical absorber over a broad range of wavelengths well into the infrared (IR). The reflectance of such arrays is 100x lower compared to conventional black materials, such as Au black in the spectral window of 350-2,500 nm. Total hemispherical measurements revealed a reflectance of approximately equal to 1.7% at lambda approximately equal to 1 micrometer, and at longer wavelengths into the infrared (IR), the specular reflectance was approximately equal to 2.4% at lambda approximately equal to 7 micrometers. The previously synthesized CNTs for optical absorber applications were formed using water-assisted thermal chemical vapor deposition (CVD), which yields CNT lengths in excess of 100's of microns. Vertical alignment, deemed to be a critical feature in enabling the high optical absorption from CNT arrays, occurs primarily via the crowding effect with thermal CVD synthesized CNTs, which is generally not effective in aligning CNTs with lengths less than 10 m. Here it has been shown that the electric field inherent in a plasma yields vertically aligned CNTs at small length scales (less than 10 m), which still exhibit broadband, and high-efficiency optical absorption characteristics from the ultraviolet (UV) to IR. A thin and yet highly absorbing coating is extremely valuable for detector applications for radiometry in order to enhance sensitivity. A plasma-based process also increases the potential of forming the optical absorbers at lower synthesis temperatures in the future, increasing the prospects of integrating the absorbers with flexible substrates for low-cost solar cell applications, for example.

Kaul, Anupama B.↗
DOE OSTI2022

Analysis of ionizing radiation induced DNA damage response in human adult stem cells and differentiated neurons

Findings of neurodegenerative features associated with human radiosensitive syndromes such as Ataxia telangiectasia suggest that DNA repair efficiency is crucial for maintaining the functional integrity of central nervous system. To gain a better understanding of ionizing radiation (IR) induced DNA damage response in undifferentiated and differentiated neural cell types and to evaluate the role of ATM in DNA double strand break (DSB) repair, an in vitro human neural cell differentiation model system was utilized in this study. As compared to adult stem cells, differentiated neurons displayed an attenuated DSB repair response (as judged by the persistence of 53BP1 foci) after IR exposure and the attenuation was even more pronounced in stem cells and neurons after suppression of ATM (Ataxia Telangiectasia Mutated) gene product suggesting the importance of ATM for an optimal DSB repair efficiency in human neural cell types. In corroboration with an attenuated DNA damage response, a sharp decline in the expression levels of several DSB repair genes was observed in neurons. Furthermore, our results suggest that cellular differentiation modulates the expression of several genes thereby compromising the DSB repair fidelity in post mitotic neurons. Further studies are required to verify whether or not ATM mediated exacerbation of DNA repair deficiency in differentiated neurons leads to neurodegeneration.

53 binding protein 1↗