Halophilic archaea and their potential to generate renewable fuels and chemicals
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Biotechnology & Applied Microbiology↗
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Results for “Generators”
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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Computational Design of High-Entropy Rare Earth Disilicates as Next-Generation Thermal/Environmental Barrier Coatings
For presentation at Materials Science Technology 2023, Columbus, OH, October 1-4, 2023.
Hao, Shiqiang↗
Distinguishing Mesoscale Polar Order (Unidirectional vs Bidirectional) of Cellulose Microfibrils in Plant Cell Walls Using Sum Frequency Generation Spectroscopy
Cellulose in plant cell walls are synthesized as crystalline microfibrils with diameters of 3–4 nm and lengths of around 1–10 μm. These microfibrils are known to be the backbone of cell walls, and their multiscale three-dimensional organization plays a critical role in cell wall functions including plant growth and recalcitrance to degradation. The mesoscale organization of microfibrils over a 1–100 nm range in cell walls is challenging to resolve because most characterization techniques investigating this length scale suffer from low spatial resolution, sample preparation artifacts, or inaccessibility of specific cell types. Here, we report a sum frequency generation (SFG) study determining the mesoscale polarity of cellulose microfibrils in intact plant cell walls. SFG is a nonlinear optical spectroscopy technique sensitive to the molecular-to-mesoscale order of noncentrosymmetric domains in amorphous matrices. However, the quantitative theoretical model to unravel the effect of polarity in packing of noncentrosymmetric domains on SFG spectral features has remained unresolved. In this work, we show how the phase synchronization principle of the SFG process is used to predict the relative intensities of vibrational modes with different polar angles from the noncentrosymmetric domain axis. Applying this model calculation for the first time and employing SFG microscopy, we found that cellulose microfibrils in certain xylem cell walls are deposited unidirectionally (or biased in one direction) instead of the bidirectional polarity which was believed to be dominant in plant cell walls from volume-averaged characterizations of macroscopic samples. Here, with this advancement in SFG analysis, one can now determine the relative polarity of noncentrosymmetric domains such as crystalline biopolymers interspersed in amorphous polymer matrices, which will open opportunities to study new questions that have not been conceived in the past.
36 MATERIALS SCIENCE↗
Numerical Simulation of Vibrational Sum Frequency Generation Intensity for Non-Centrosymmetric Domains Interspersed in an Amorphous Matrix: A Case Study for Cellulose in Plant Cell Wall
Vibrational sum frequency generation (SFG) spectroscopy can specifically probe molecular species non-centrosymmetrically arranged in a centrosymmetric or isotropic medium. This capability has been extensively utilized to detect and study molecular species present at the two-dimensional (2D) interface at which the centrosymmetry or isotropy of bulk phases is naturally broken. The same principle has been demonstrated to be very effective for the selective detection of non-centrosymmetric crystalline nanodomains interspersed in three-dimensional (3D) amorphous phases. However, the full spectral interpretation of SFG features has been difficult due to the complexity associated with the theoretical calculation of SFG responses of such 3D systems. This paper describes a numerical method to predict the relative SFG intensities of non-centrosymmetric nanodomains in 3D systems as functions of their size and concentration as well as their assembly patterns, i.e., the distributions of tilt, azimuth, and rotation angles with respect to the lab coordinate. We applied the developed method to predict changes in the CH and OH stretch modes characteristic to crystalline cellulose microfibrils distributed with various orders, which are relevant to plant cell wall structures. As a result, the same algorithm can also be applied to any SFG-active nanodomains interspersed in 3D amorphous matrices.
36 MATERIALS SCIENCE↗
Electrolyte-phobic Surface for the Next-Generation Nanostructured Battery Electrodes
Nanostructured electrodes are among the most important candidates rationally designed to enable high capacity battery chemistry. Nanostructures can solve issues such as the volume change and mechanical fragmentations. However, the high surface area they usually possess would decrease the Coulombic efficiencies, since the side chemical reactions scale with surface area. Moreover, electrodes comprised of nanomaterials have significant intakes of liquid electrolytes, which reduces the overall energy density and increases the cost of the battery. Here we present a new strategy of limiting effective surface area by introducing an “electrolyte-phobic surface”. In this study, a porous Si anode functions as a model material to demonstrate this concept. Silicon boasts high theoretical capacity, but experiences large volume change during its lithiation and delithiation processes. Porous Si can address this volume change problem with the buffer effect of its inner pores. However, porous silicon shows low initial Coulombic efficiencies and high irreversible lithium loss, owing to its intrinsic high surface area. In this report, a covalently linked perfluorinated surface coating layer on porous Si particles serves as an electrolyte-phobic protection layer, minimizing the accessible surface area for the electrolytes, decreasing the side reactions between the electrolyte and Si surface, and thus significantly enhancing the initial Coulombic efficiencies, up to ~88% compared to ~60% for the pristine porous silicon. Meanwhile, the electrolyte-phobic protection layer of Si particles keeps the silicon surface compatible with the conventional polyvinylidene fluoride (PVDF) binder, which helps to stabilize the Si electrode for long-term battery cycling.
Qian, Chenxi↗
Interpretable machine learning for knowledge generation in heterogeneous catalysis
Most applications of machine learning in heterogeneous catalysis thus far have used black-box models to predict computable physical properties (descriptors), such as adsorption or formation energies, that can be related to catalytic performance (that is, activity or stability). Here, extracting meaningful physical insights from these black-box models has proved challenging, as the internal logic of these black-box models is not readily interpretable due to their high degree of complexity. Interpretable machine learning methods that merge the predictive capacity of black-box models with the physical interpretability of physics-based models offer an alternative to black-box models. In this Perspective, we discuss the various interpretable machine learning methods available to catalysis researchers, highlight the potential of interpretable machine learning to accelerate hypothesis formation and knowledge generation, and outline critical challenges and opportunities for interpretable machine learning in heterogeneous catalysis.
37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
Advancements and challenges in the production of low-carbon fuels via catalytic fast pyrolysis of biomass through refinery integration and co-product generation
Creating pathways to renewable fuels, chemicals, and materials through improved catalyst formulations and integrated process development.
Yung, Matthew M.↗
Machine-learning identification of the variability of mean velocity and turbulence intensity for wakes generated by onshore wind turbines: Cluster analysis of wind LiDAR measurements
Light detection and ranging (LiDAR) measurements of isolated wakes generated by wind turbines installed at an onshore wind farm are leveraged to characterize the variability of the wake mean velocity and turbulence intensity during typical operations, which encompass a breadth of atmospheric stability regimes and rotor thrust coefficients. The LiDAR measurements are clustered through the k-means algorithm, which enables identifying the most representative realizations of wind turbine wakes while avoiding the imposition of thresholds for the various wind and turbine parameters. Considering the large number of LiDAR samples collected to probe the wake velocity field, the dimensionality of the experimental dataset is reduced by projecting the LiDAR data on an intelligently truncated basis obtained with the proper orthogonal decomposition (POD). The coefficients of only five physics-informed POD modes are then injected in the k-means algorithm for clustering the LiDAR dataset. The analysis of the clustered LiDAR data and the associated supervisory control and data acquisition and meteorological data enables the study of the variability of the wake velocity deficit, wake extent, and wake-added turbulence intensity for different thrust coefficients of the turbine rotor and regimes of atmospheric stability. Furthermore, the cluster analysis of the LiDAR data allows for the identification of systematic off-design operations with a certain yaw misalignment of the turbine rotor with the mean wind direction.
17 WIND ENERGY↗
The LSST DESC data challenge 1: generation and analysis of synthetic images for next-generation surveys
Data Challenge 1 (DC1) is the first synthetic data set produced by the Rubin Observatory Legacy Survey of Space and Time (LSST) Dark Energy Science Collaboration (DESC). DC1 is designed to develop and validate data reduction and analysis and to study the impact of systematic effects that will affect the LSST data set. DC1 is comprised of r -band observations of 40 deg 2 to 10 yr LSST depth. In this paper, we present each stage of the simulation and analysis process: (a) generation, by synthesizing sources from cosmological N -body simulations in individual sensor-visit images with different observing conditions; (b) reduction using a development version of the LSST Science Pipelines; and (c) matching to the input cosmological catalogue for validation and testing. We verify that testable LSST requirements pass within the fidelity of DC1. We establish a selection procedure that produces a sufficiently clean extragalactic sample for clustering analyses and we discuss residual sample contamination, including contributions from inefficiency in star–galaxy separation and imperfect deblending. We compute the galaxy power spectrum on the simulated field and conclude that: (i) survey properties have an impact of 50 per cent of the statistical uncertainty for the scales and models used in DC1; (ii) a selection to eliminate artefacts in the catalogues is necessary to avoid biases in the measured clustering; and (iii) the presence of bright objects has a significant impact (2σ–6σ) in the estimated power spectra at small scales (ℓ > 1200), highlighting the impact of blending in studies at small angular scales in LSST.
46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
HDEV Depot Load Profile Generation Code (Code to Generate Heavy-Duty Electric Truck Depot Load Profiles) [SWR-21-72]
Code developed to generate heavy-duty electric truck depot load profiles for the study, "Heavy-Duty Truck Electrification and the Impacts of Depot Charging on Electricity Distribution Systems", by Borlaug et al., published in 2021. This software is provided as-is without dedicated support. The programming environment for this study may be reproduced with conda (installed via the Anaconda website): conda env create -f environment.yml To activate the environment: conda activate hdev-depot-charging-2021
Borlaug, Brennan↗
A Public Data Set of Auto-Generated Geotagged PV Site Equipment, Generated via Deep Learning
In this research, we present a data set over 100 photovoltaic (PV) sites in TX, which have been automatically geotagged via a fully autonomous deep learning (DL) pipeline. Specifically, locations of inverters, tracker/fixed tilt rows, batteries, and substations are labeled algorithmically. To ensure high data quality, all systems have been reviewed manually and any deep learning errors have been corrected. This public data set, as well as the open-sourced pipeline used to generate it, is valuable for site planning, modelling, and insurance purposes. Given time and resources, we hope to extend the data set to additional states/regions in the US.
14 SOLAR ENERGY↗
Trace material generation rate simulator, appendix B. Part B - Study of generation rate patterns Final report
Generation rate patterns and relative toxicity ratings for trace materials
CONTAMINANT↗
Thermionic converters and generators for space application. II - Solar thermionic generators.
Solar-heated thermionic generator design and fabrication evaluation, considering mirror configurations
THERMIONIC CONVERTER↗
Single stage experimental evaluation of compressor blading with slots and vortex generators. Part 2 - Data and performance for stage 5 without slots or vortex generators
Comparison performance data of compressor blading without slots or vortex generators
Brent, J. A.↗
Wind turbine generator interaction with conventional diesel generators on Block Island, Rhode Island. Volume 1: Executive summary
Primary results are summarized for a three-part study involving the effects of connecting a MOD-OA wind turbine generator to an isolated diesel power system. The MOD-OA installation considered was the third of four experimental nominal 200 kW wind turbines connected to various utilities under the Federal Wind Energy Program and was characterized by the highest wind energy penetration levels of four sites. The study analyses address: fuel displacement, dynamic interaction, and three modes of reactive power control. These analyses all have as their basis the results of the data acquisition program conducted on Block Island, Rhode Island.
Wilreker, V. F.↗
Wind turbine generator interaction with conventional diesel generators on Block Island, Rhode Island. Volume 2: Data analysis
Assessing the performance of a MOD-OA horizontal axis wind turbine connected to an isolated diesel utility, a comprehensive data measurement program was conducted on the Block Island Power Company installation on Block Island, Rhode Island. The detailed results of that program focusing on three principal areas of (1) fuel displacement (savings), (2) dynamic interaction between the diesel utility and the wind turbine, (3) effects of three models of wind turbine reactive power control are presented. The approximate two month duration of the data acquisition program conducted in the winter months (February into April 1982) revealed performance during periods of highest wind energy penetration and hence severity of operation. Even under such conditions fuel savings were significant resulting in a fuel reduction of 6.7% while the MOD-OA was generating 10.7% of the total electrical energy. Also, electrical disturbance and interactive effects were of an acceptable level.
Wilreker, V. F.↗
Crowned spur gears - Methods for generation and tooth contact analysis. I - Basic concepts, generation of the pinion tooth surface by a plane
A topology of crowned spur pinion tooth surface that reduces the level of transmission errors due to misalignment is proposed. The geometry of the deviated pinion tooth surface and regular gear tooth surface, along with tooth contact analysis is discussed. Generation of the deviated pinion tooth surface by a plane whose motion is controlled by a five-degree-of-freedom system is proposed. Numerical results are included and indicate that transmission errors remain low as the gears are misaligned.
Litvin, F. L.↗
Crowned spur gears - Methods for generation and Tooth Contact Analysis. II - Generation of the pinion tooth surface by a surface of revolution
A method for generation of crowned pinion tooth surfaces using a surface of revolution is developed. The crowned pinion meshes with a regular involute gear and has a prescribed parabolic type of transmission errors when the gears operate in the aligned mode. When the gears are misaligned the transmission error remains parabolic with the maximum level still remaining very small (less than 0.34 arc second for the numerical examples). Tooth Contact Analysis (TCA) is used to simulate the conditions of meshing, determine the transmission error, and the bearing contact.
Litvin, F. L.↗