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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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Port wine, an established product a new market: A comparative analysis of perceptions of firms and consumers
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Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale
The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by 13 C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.
Comparing Sensor Fusion and Multimodal Chemometric Models for Monitoring U(VI) in Complex Environments Representative of Irradiated Nuclear Fuel
Optical sensors and chemometric models were leveraged for the quantification of uranium(VI) (0–100 μg mL –1 ), europium (0–150 μg mL –1 ), samarium (0–250 μg mL –1 ), praseodymium (0–350 μg mL –1 ), neodymium (0–1000 μg mL –1 ), and HNO 3 (2–4 M) with varying corrosion product (iron, nickel, and chromium) levels using laser fluorescence, Raman scattering, and ultraviolet–visible–near-infrared absorption spectra. In this paper, an efficient approach to developing and evaluating tens of thousands of partial least-squares regression (PLSR) models, built from fused optical spectra or multimodal acquisitions, is discussed. Each PLSR model was optimized with unique preprocessing combinations, and features were selected using genetic algorithm filters. The 7-factor D-optimal design training set contained just 55 samples to minimize the number of samples. The performance of PLSR models was evaluated by using an automated latent variable selection script. PLS1 regression models tailored to each species outperformed a global PLS2 model. PLS1 models built using fused spectra data and a multimodal (i.e., analyzed separately) approach yielded similar information, resulting in percent root-mean-square error of prediction values of 0.9–5.7% for the seven factors. Further, the optical techniques and data processing strategies established in this study allow for the direct analysis of numerous species without measuring luminescence lifetimes or relying on a standard addition approach, making it optimal for near-real-time, in situ measurements. Nuclear reactor modeling helped bound training set conditions and identified elemental ratios of lanthanide fission products to characterize the burnup of irradiated nuclear fuel. Leveraging fluorescence, spectrophotometry, experimental design, and chemometrics can enable the remote quantification and characterization of complex systems with numerous species, monitor system performance, help identify the source of materials, and enable rapid high-throughput experiments in a variety of industrial processes and fundamental studies.
Probing Crystallization Effects when Processing Bulk-Heterojunction Active Layers: Comparing Fullerene and Nonfullerene Acceptors
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Comparative Electronic Structures of the Chiral Helimagnets Cr 1/3 NbS 2 and Cr 1/3 TaS 2
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Enhancing Properties with Distortion: A Comparative Study of Two Iron Phosphide Fe 2 P Polymorphs
Iron phosphide (Fe 2 P) crystallizes in its own hexagonal crystal structure type (h-Fe 2 P). As found in meteorites, orthorhombic polymorph (o-Fe 2 P) was originally reported as a high-temperature and high-pressure phase. Recently, o-Fe 2 P was described as being stable at ambient pressure, yet no synthetic methods were developed for single-crystal growth or single-phase bulk powder synthesis. Here, we report a successful method for growing o-Fe 2 P single crystals and synthesizing phase-pure polycrystalline samples using tin-flux. In situ powder X-ray diffraction studies showed that the phase transition from o-Fe 2 P to h-Fe 2 P occurs at about 873 K, and below that temperature, the formation of the o-Fe 2 P phase is favored thermodynamically rather than kinetically. Systematic comparison of transport, magnetic, and electrocatalytic properties of both h-Fe 2 P and o-Fe 2 P phases showed a substantial impact of the crystal structure on properties. The orthorhombic structural distortion resulted in considerable changes in magnetic properties, with the o-Fe 2 P phase exhibiting a 60% lower Fe magnetic moment and a substantially higher ferromagnetic Curie temperature than h-Fe 2 P. Further, electrochemical measurements toward the hydrogen evolution reaction in acidic media showed that the o-Fe 2 P phase requires an 80 mV lower overpotential than the h-Fe 2 P phase to generate a current density of -10 mA/cm 2 , and their electronic structures suggest that the higher density of states at the Fermi energy is the origin of superior catalytic activity in o-Fe 2 P.
Experimental Open Air Burning of Vegetation Enhances Organic Matter Chemical Heterogeneity Compared to Laboratory Burns
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Liquid Alkaline Water Electrolyzers: Comparing Performance across Design, Operation, and End-of-Life Scenarios
Liquid alkaline water electrolysis (LAWE) is a demonstrated technology for hydrogen production, yet a comprehensive life cycle assessment (LCA) of their deployment is lacking. Research leading to improvements to the core component, the electrochemical stack, along with auxiliary system materials and dynamic operation of stacks from variable electricity supply offers new data that allows for detailed modeling and evaluation. Here, we present an LCA of two facility designs based on the current state-of-the-art stack and an advanced stack with zero-gap between electrodes, and capture dynamic electricity use from solar, wind, and hybrid sources and stack recycling strategies. We present life cycle impact factors characterizing the production of 1 kg of hydrogen across 12 environmental, human health, and resource impact categories (TRACI and ReCiPe) in the contiguous United States. As expected, the source of electricity will drive impacts (e.g., 83-94% of carbon intensity); however, we find that operating using wind electricity can lower hydrogen leakage and the overall carbon intensity (1.03 kgCO 2 e/kgH 2 ) relative to solar electricity (2.57 kgCO 2 e/kgH 2 ) at matched 1:1 capacity between LAWE and the electricity source. The deployment of the advanced design and stack recycling lowers impacts across all life cycle stages. We highlight opportunities to further reduce potential impacts, including the balance of plant materials and operation cycles associated with the use of variable wind and solar electricity that result in hydrogen leakage.
Comparative Assessment of Pose Prediction Accuracy in RNA–Ligand Docking
Structure-based virtual high-throughput screening is used in early-stage drug discovery. Over the years, docking protocols and scoring functions for protein–ligand complexes have evolved to improve the accuracy in the computation of binding strengths and poses. In the past decade, RNA has also emerged as a target class for new small-molecule drugs. However, most ligand docking programs have been validated and tested for proteins and not RNA. Here, we test the docking power (pose prediction accuracy) of three state-of-the-art docking protocols on 173 RNA–small molecule crystal structures. The programs are AutoDock4 (AD4) and AutoDock Vina (Vina), which were designed for protein targets, and rDock, which was designed for both protein and nucleic acid targets. AD4 performed relatively poorly. For RNA targets for which a crystal structure of a bound ligand used to limit the docking search space is available and for which the goal is to identify new molecules for the same pocket, rDock performs slightly better than Vina, with success rates of 48% and 63%, respectively. However, in the more common type of early-stage drug discovery setting, in which no structure of a ligand–target complex is known and for which a larger search space is defined, rDock performed similarly to Vina, with a low success rate of ~27%. Further, Vina was found to have bias for ligands with certain physicochemical properties, whereas rDock performs similarly for all ligand properties. Thus, for projects where no ligand–protein structure already exists, Vina and rDock are both applicable. However, the relatively poor performance of all methods relative to protein–target docking illustrates a need for further methods refinement.