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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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Process modelling and techno-economic analysis of NG-DRI/EAF and H2 DRI/EAF steel production providing insights into process design and break-even hydrogen prices.
To maintain and improve the verification regime that is outlined by the Preparatory Commission of the Comprehensive Nuclear Test-Ban Treaty there is a need to understand and reduce the radioxenon releases from medical isotope production facilities. In support of this objective Pacific Northwest National Laboratory (PNNL) was tasked with, modeling and evaluate the abatement process and delay bed designs for the medical isotope production facility under construction by the Korea Atomic Energy Research Institute (KAERI). This report includes this analysis and provides PNNL proposed modifications to the KAERI adsorption bed design.
This study evaluates a novel insulated cladding composed of foamed cement and vinyl siding that is designed as a retrofit product to enhance thermal performance in U.S. residential buildings. Experimental measurements showed that foamed cement exhibits a thermal conductivity of 0.0334–0.0365 W/m-K (R-value of 4.0–4.3 °F⋅ft 2 ⋅h/BTU), which is significantly higher than that of conventional fiber cement boards. Although mechanical testing confirmed the material’s brittleness and low strength, it also indicated its suitability for nonstructural insulation applications. Water vapor permeability testing demonstrated effective moisture resistance with the polymer coating. To evaluate the insulated cladding’s energy performance, we used the U.S. Department of Energy’s prototype single-family building model and simulated across 50 locations spanning 16 International Energy Conservation Code climate zones. The R-7 (R-value of 7 °F⋅ft 2 ⋅h/BTU per inch) retrofit cladding resulted in heating energy savings up to 275 therms (Fairbanks, AK) and cooling energy savings up to 1,547 kWh (Phoenix, AZ). The energy cost savings varied by region, but the highest annual cost savings ($\$$434/year) were observed in Santa Maria, CA, and the highest annual cost savings percentage (37 %) were noted in Monterey, CA. On a national scale, the insulated retrofit provided an average annual energy cost reduction of 16 %, highlighting the cladding system’s broad applicability and financial viability.
This study presents a method to obtain a heliostat size that minimizes the levelized cost of heat (LCOH) of a heliostat-based concentrating solar thermal system for applications of solar heating for industrial processes at operating temperatures from 565 to 1550°C. The method extends prior work by embedding a routine for system design that obtains near-optimal subsystem sizes, increasing the fidelity of drive cost functions, and adding an optical performance model to supplement the previously developed cost models, which we update to reflect current pricing trends. An illustrative business case is developed for Daggett, California, targeting specified annual thermal energy outputs of 50 to 400 GWh th . Optical performance is modeled using verified estimates from the literature. A surrogate heliostat cost model, derived from commercial heliostat designs and scaled for production volume, installation, and operations and maintenance costs, is used to develop cost functions. Results show that heliostat size strongly affects the LCOH, producing a characteristic U-shaped trend with a robust near-optimal window of 7-20 m 2 ; the heliostat size producing the lowest project cost in our study grows slightly as the project size increases, and is reduced as the operating temperature increases. The findings in this study are consistent with the general trend of smaller heliostats being deployed at existing projects for high-temperature industrial process heat and reflect the significant reduction in power electronics and other per-heliostat costs. The methodology we propose is general and can be tailored to revised cost curves as the technology continues to evolve.
MoO 2 has attracted much recent attention as a high capacity energy storage material. While much of the current work on MoO 2 has been focused on the high capacity four-electron reduction, this reaction is limited to slow charging process due to the large volume change and phase transitions involved. In this study we focus on one-electron insertion reactions and demonstrate that ordered mesoporous thin films of MoO 2 can show signatures of pseudocapacitive charge storage. Mesoporous MoO 2 (mp-MoO 2 ) thin films were treated at different temperatures between 350 and 700 °C to explore the role of crystallinity and nanoscale structure on charging dynamics. The porosity and pore size decreased while the crystallinity and grain size increased as the calcination temperature increased. Materials processed at 600 °C showed the best electrochemical performance due to an optimized combination of high crystallinity and small grain size. These materials could be charged and discharged in 24 s while still achieving a Li + storage capacity of 158 mAh/g. While such thin film systems do not constitute a practical energy storage device, the work here provides structural design parameters for the production of future bulk nanoporous materials.
High energy density aluminum nanoparticles (AlNP) have been at the center of attention as additives to hydrocarbon jet fuels like exo-tetrahydrodicyclopentadiene (JP-10, C 10 H 16 ) aiming at a superior performance of volume-limited air-breathing propulsion systems. However, a fundamental understanding of the ignition and combustion chemistry of JP-10 in presence of AlNPs has been elusive. Exploiting an isomer-selective comprehensive identification of the decomposition products in a newly designed high temperature chemical microreactor coupled to vacuum ultraviolet photoionization, we reveal an active low temperature heterogeneous surface chemistry commencing at 650 K involving the alumina (Al 2 O 3 ) shell. Contrary to textbook knowledge of an “inactive alumina surface”, this unconventional reactivity, where oxygen is transferred from alumina to JP-10, leads to generating cyclic, oxygenated organics like phenol (C 6 H 5 OH) and 2,4-cyclopentadiene-1-one (C 5 H 4 O) - key tracers of an alumina-mediated interfacial chemistry. Furthermore, this counterintuitive reactivity transforms our knowledge on the (catalytic) processes of alumina-coated AlNPs on the molecular level.
The use of flowing electrochemical reactors, for example, in redox flow batteries and in various electrosynthesis processes, is increasing. This technology has the potential to be of central significance in the increased deployment of renewable electricity for carbon-neutral processes. A key element of optimizing efficiency of electrochemical reactors is the combination of high solution conductivity and reagent solubility. In this work, we show a substantial rate of charge transfer for an electrochemical reaction occurring in a microemulsion containing electroactive material is loaded inside the nonpolar (toluene) subphase of the microemulsion. The measured rate constant translates to an exchange current density comparable to that in redox flow batteries. The rate could be controlled by the surfactant, which maintains partitioning of reactants and products by forming an interfacial region with ions in the aqueous phase in close proximity. The hypothesized echanism is evocative of membrane-bound enzymatic reactions. Achieving sufficient rates of electrochemical reaction is the product of an effort designed to establish a reaction condition that meets the requirements of electrochemical reactors using microemulsions to realize a separation of conducting and reactive elements of the solution, opening a door to the broad use of microemulsions to effect controlled electrochemical reactions as steps in more complex processes.
Polyacrylonitrile (PAN) is an important commercial polymer, bearing atactic stereochemistry resulting from nonselective radical polymerization. As such, an accurate, fundamental understanding of governing interactions among PAN molecular units is indispensable for advancing the design principles of final products at reduced processability costs. While ab initio molecular dynamics (AIMD) simulations can provide the necessary accuracy for treating key interactions in polar polymers, such as dipole–dipole interactions and hydrogen bonding, and analyzing their influence on the molecular orientation, their implementation is limited to small molecules only. Herein, we show that the neural network interatomic potentials (NNIPs) that are trained on the small-scale AIMD data (acquired for oligomers) can be efficiently employed to examine the structures and properties at large scales (polymers). NNIP provides critical insight into intra- and interchain hydrogen-bonding and dipolar correlations and accurately predicts the amorphous bulk PAN structure validated by modeling the experimental X-ray structure factor. Furthermore, the NNIP-predicted PAN properties, such as density and elastic modulus, are in good agreement with their experimental values. Overall, the trend in the elastic modulus is found to correlate strongly with the PAN structural orientations encoded in the Hermans orientation factor. In conclusion, this study enables the ability to predict the structure–property relations for PAN and analogues with sustainable ab initio accuracy across scales.
Abstract Through advanced mechanistic modeling and the generation of large high-quality datasets, machine learning is becoming an integral part of understanding and engineering living systems. Here we show that mechanistic and machine learning models can be combined to enable accurate genotype-to-phenotype predictions. We use a genome-scale model to pinpoint engineering targets, efficient library construction of metabolic pathway designs, and high-throughput biosensor-enabled screening for training diverse machine learning algorithms. From a single data-generation cycle, this enables successful forward engineering of complex aromatic amino acid metabolism in yeast, with the best machine learning-guided design recommendations improving tryptophan titer and productivity by up to 74 and 43%, respectively, compared to the best designs used for algorithm training. Thus, this study highlights the power of combining mechanistic and machine learning models to effectively direct metabolic engineering efforts.
Abstract Bottom-up synthetic biology aims to engineer artificial cells capable of responsive behaviors by using a minimal set of molecular components. An important challenge toward this goal is the development of programmable biomaterials that can provide active spatial organization in cell-sized compartments. Here, we demonstrate the dynamic self-assembly of nucleic acid (NA) nanotubes inside water-in-oil droplets. We develop methods to encapsulate and assemble different types of DNA nanotubes from programmable DNA monomers, and demonstrate temporal control of assembly via designed pathways of RNA production and degradation. We examine the dynamic response of encapsulated nanotube assembly and disassembly with the support of statistical analysis of droplet images. Our study provides a toolkit of methods and components to build increasingly complex and functional NA materials to mimic life-like functions in synthetic cells.
Electrochemical reduction of CO 2 is a promising route for sustainable production of fuels. A grand challenge is developing low-cost and efficient electrocatalysts that can enable rapid conversion with high product selectivity. Here we design a series of nickel phthalocyanine molecules supported on carbon nanotubes as molecularly dispersed electrocatalysts (MDEs), achieving CO 2 reduction performances that are superior to aggregated molecular catalysts in terms of stability, activity and selectivity. The optimized MDE with methoxy group functionalization solves the stability issue of the original nickel phthalocyanine catalyst and catalyses the conversion of CO 2 to CO with >99.5% selectivity at high current densities of up to -300 mA cm -2 in a gas diffusion electrode device with stable operation at -150 mA cm -2 for 40 h. The well-defined active sites of MDEs also facilitate the in-depth mechanistic understandings from in situ/operando X-ray absorption spectroscopy and theoretical calculations on structural factors that affect electrocatalytic performance.
With these guidelines, we aim to unite the lignin-first biorefining research field around best practices for performing or reporting feedstock analysis, reactor design, catalyst performance, and product yields.
During the operation of the CMS experiment at the High-Luminosity LHC the silicon sensors of the Phase-2 Outer Tracker will be exposed to radiation levels that could potentially deteriorate their performance. Previous studies had determined that planar float zone silicon with n-doped strips on a p-doped substrate was preferred over p-doped strips on an n-doped substrate. The last step in evaluating the optimal design for the mass production of about 200 m$^{2}$ of silicon sensors was to compare sensors of baseline thickness (about 300 μm) to thinned sensors (about 240 μm), which promised several benefits at high radiation levels because of the higher electric fields at the same bias voltage. This study provides a direct comparison of these two thicknesses in terms of sensor characteristics as well as charge collection and hit efficiency for fluences up to 1.5 × 10$^{15}$ n$_{eq}$/cm$^{2}$. The measurement results demonstrate that sensors with about 300 μm thickness will ensure excellent tracking performance even at the highest considered fluence levels expected for the Phase-2 Outer Tracker.
Navigating uncertainty is a critical challenge in all fields of science, especially when translating knowledge into real-world policies or management decisions. However, the wide variance in concepts and definitions of uncertainty across scientific fields hinders effective communication. As a microcosm of diverse fields within Earth Science, NASA’s Carbon Monitoring System (CMS) provides a useful crucible in which to identify cross-cutting concepts of uncertainty. The CMS convened the Uncertainty Working Group (UWG), a group of specialists across disciplines, to evaluate and synthesize efforts to characterize uncertainty in CMS projects. This paper represents efforts by the UWG to build a heuristic framework designed to evaluate data products and communicate uncertainty to both scientific and non-scientific end users. We consider four pillars of uncertainty: origins, severity, stochasticity versus incomplete knowledge, and spatial and temporal autocorrelation. Using a common vocabulary and a generalized workflow, the framework introduces a graphical heuristic accompanied by a narrative, exemplified through contrasting case studies. Envisioned as a versatile tool, this framework provides clarity in reporting uncertainty, guiding users and tempering expectations. Beyond CMS, it stands as a simple yet powerful means to communicate uncertainty across diverse scientific communities.
With the rapid expansion of large-scale photovoltaic (PV) plants, it is paramount for solar stakeholders to understand the reliability and efficiency of their plants to inform maintenance decisions, increase production, and understand the design factors that impact performance. Diagnosing underperformance in PV plants is challenging due to the relatively few monitoring points with respect to the large geographic footprint of the plant. This work introduces a cutting-edge method that transforms the analysis and management of key factors influencing PV plant performance, including performance loss rate (PLR), recoverable soiling, and major system changes. Identifying these factors is critical for deriving actionable insights. Leveraging advanced analytical techniques such as wavelet transformation, robust regression, and extreme point analysis, this approach provides a nuanced understanding of these factors. This method has been tested across two synthetic datasets and one real dataset, consistently surpassing existing benchmarks by achieving a lower median mean absolute error and reduced error variability across all comparable components.
Current allometric methods for photosynthetic-stem (cladode) plants, such as cactus pear (Opuntia spp.), require refinement to be used in field settings in which diverse accessions are grown. We analysed cladode dimensional data using 14 accessions representing four species and two hybrids to quantify statistically significant morphological differences among accessions and derived cross-accession models to approximate cladode fresh weight. A Box model using cladode dimensions (e.g., length, width, thickness and diameter) and factorial combinations of these measures (e.g., length*width*thickness*diameter vs. fresh weight) resulted in the highest coefficient of determination (R 2 = 0.95 general fit) across all accessions for estimating fresh weight along with parsimony estimates using the Schwarz–Bayes Criterion (SBC), which assesses the most consistent performance on individual accessions. A Fitting-box modelling approach used the measured cladode area captured using ImageJ (R 2 = 0.93 general fit). Lastly, an Elliptical model used an elliptical approximation for the measured area and performed well over all accessions (R 2 = 0.94 general fit) while avoiding extensive manual measurements. These models meet or exceed the performance of previously published approaches when applied across morphologically diverse accessions, providing efficient tools for nondestructive estimation of cactus pear biomass under the conditions tested.
We report the productive outcome of the design, fabrication, and testing of a new class of GaN/AlGaN-based ultrafast, sensitive photodiodes for deep-UV solar blind photodetection. Pt was employed as the interdigitated metal electrode to establish that while a Schottky contact was formed at the metal/semiconductor heterojunction, it was the quantum wells in the p-i-n vertical structure that produced the diode characteristics of the detectors. The metal–semiconductor–metal design retained its ultrafast property as expected in the p-i-n structure because the spacing between the interdigitated fingers was asymmetrically implemented to optimize the photogenerated carriers’ transit time from the quantum well to the external circuitry. Also, the vertical p-i-n structure provided its renowned efficient photocarrier generation, which was significantly enhanced by the delta quantum well architecture of the vertical epitaxial structure. The active area of the device was 300 μm × 300 μm with 5-μm finger width, and asymmetric electrode spacing of 2 μm, 3 μm, and 5 μm. Here, the best device had peak responsivity of 3.5 A/W under 262-nm illumination with full width half maximum of 337.2-ps and 37.5-ps rise times when biased with 20 V.