Manganese Oxide Overlayers Promote CO Oxidation on Pt
Not Available
SEARCH · Search NASA
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.
Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.
Not Available
CO 2 hydrogenation to methanol is of great environmental and economic interest due to its potential to reduce carbon emissions and produce valuable chemicals in one single reaction. Compared with the unmodified traditional Cu/ZnO/Al 2 O 3 catalyst, an indium oxide (In 2 O 3 )-based catalyst can double the methanol selectivity from 30–50 to 60–100%. It is worth noting that over catalysts involving various active metals dispersed on indium oxide (M/In 2 O 3 , M = Pd, Ni, Au, etc.), although the methanol yield is boosted, the selectivity remains similar to that of plain In 2 O 3 despite the distinct chemical properties of the added metals. Here, to investigate the phenomena behind this behavior, we used RuO 2 /In 2 O 3 as a test catalyst. The results of ambient pressure photoelectron spectroscopy, in situ X-ray absorption fine structure, and time-resolved X-ray diffraction indicate that the structure of the RuO 2 /In 2 O 3 catalyst is highly dynamic in the presence of a reactive environment. Specifically, under CO 2 hydrogenation conditions, Ru clusters facilitate the reduction of In 2 O 3 to generate In 2 O 3–x aggregates, which encapsulate the Ru systems in a migration driven by thermodynamics. In this way, the Ru O sites for CH 4 production are blocked while creating RuO x –In 2 O 3–x interfacial sites with tunable metal–oxide interactions for selective methanol production. In an inverse oxide/metal configuration, indium oxide has properties not seen in its bulk phase that are useful for the binding and conversion of CO 2 . This work reveals the dynamic nature of In 2 O 3 -based catalysts, providing insights for a rational design of materials for the selective synthesis of methanol.
Ordering of different chalcogens, S, Se, and Te, on Au(111) exhibit broad similarities but also some distinct features, which must reflect subtle differences in relative values of the long-range pair and many-body lateral interactions between adatoms. We develop lattice-gas (LG) models within a cluster expansion framework, which includes about 50 interaction parameters. These LG models are developed based on density functional theory (DFT) analysis of the energetics of key adlayer configurations in combination with the Monte Carlo (MC) simulation of the LG models to identify statistically relevant adlayer motifs, i.e., model development is based entirely on theoretical considerations. The MC simulation guides additional DFT analysis and iterative model refinement. Given their complexity, development of optimal models is also aided by strategies from supervised machine learning. The model for S successfully captures ordering motifs over a broader range of coverage than achieved by previous models, and models for Se and Te capture the features of ordering, which are distinct from those for S. More specifically, the modeling for all three chalcogens successfully explains the linear adatom rows (also subtle differences between them) observed at low coverages of ~0.1 monolayer. The model for S also leads to a new possible explanation for the experimentally observed phase with a (5 × 5)-type low energy electron diffraction (LEED) pattern at 0.28 ML and to predictions for LEED patterns that would be observed with Se and Te at this coverage.
Not provided.
Abstract not provided.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
Explore the source record for details and available documents.
MicroBooNE samples are provided for collaborative development in two different formats: HDF5, targeting the broadest audience, and artroot, targeting users that are familiar with the software infrastructure of Fermilab neutrino experiments and more in general of HEP experiments. The HDF5 files are stored on Zenodo, together with a list of artroot files accessible with xrootd.This sample includes simulated interactions of neutrinos from the Booster Neutrino Beam (BNB), overlaid on top of cosmic ray data. The sample is restricted to charged-current electron neutrino interactions within the argon active volume of the time projection chamber.The HDF5 files in this sample do not include the information at the wire waveform level ("NoWire" label), allowing for larger number of events to be included in the data set.More documentation, including detailed description of content, recipes, and example usage, at https://github.com/uboone/OpenSamples.Suggested text for acknowledgment is the following:We acknowledge the MicroBooNE Collaboration for making publicly available the data sets [data set DOIs] employed in this work. These data sets consist of simulated neutrino interactions from the Booster Neutrino Beamline overlaid on top of cosmic data collected with the MicroBooNE detector [2017 JINST 12 P02017].In addition, we request that software products resulting from the usage of the datasets are also made publicly available.
MicroBooNE samples are provided for collaborative development in two different formats: HDF5, targeting the broadest audience, and artroot, targeting users that are familiar with the software infrastructure of Fermilab neutrino experiments and more in general of HEP experiments. The HDF5 files are stored on Zenodo, together with a list of artroot files accessible with xrootd.This sample includes simulated interactions of neutrinos from the Booster Neutrino Beam (BNB), overlaid on top of cosmic ray data. The sample is inclusive, i.e. it includes all types of neutrinos and interactions, with relative abundance matching our nominal flux and cross section models. Interactions are simulated in in the whole cryostat volume.The HDF5 files in this sample include the information at the wire waveform level (after deconvolution and finding of regions of interest). As this information significantly increases the file size, this sample contains about 17% of the events of the corresponding sample without wire information.More documentation, including detailed description of content, recipes, and example usage, at https://github.com/uboone/OpenSamples.Suggested text for acknowledgment is the following:We acknowledge the MicroBooNE Collaboration for making publicly available the data sets [data set DOIs] employed in this work. These data sets consist of simulated neutrino interactions from the Booster Neutrino Beamline overlaid on top of cosmic data collected with the MicroBooNE detector [2017 JINST 12 P02017].In addition, we request that software products resulting from the usage of the datasets are also made publicly available.
MicroBooNE samples are provided for collaborative development in two different formats: HDF5, targeting the broadest audience, and artroot, targeting users that are familiar with the software infrastructure of Fermilab neutrino experiments and more in general of HEP experiments. The HDF5 files are stored on Zenodo, together with a list of artroot files accessible with xrootd.This sample includes simulated interactions of neutrinos from the Booster Neutrino Beam (BNB), overlaid on top of cosmic ray data. The sample is restricted to charged-current electron neutrino interactions within the argon active volume of the time projection chamber.The HDF5 files in this sample include the information at the wire waveform level (after deconvolution and finding of regions of interest). As this information significantly increases the file size, this sample contains about 20% of the events of the corresponding sample without wire information.More documentation, including detailed description of content, recipes, and example usage, at https://github.com/uboone/OpenSamples.Suggested text for acknowledgment is the following:We acknowledge the MicroBooNE Collaboration for making publicly available the data sets [data set DOIs] employed in this work. These data sets consist of simulated neutrino interactions from the Booster Neutrino Beamline overlaid on top of cosmic data collected with the MicroBooNE detector [2017 JINST 12 P02017].In addition, we request that software products resulting from the usage of the datasets are also made publicly available.
MicroBooNE samples are provided for collaborative development in two different formats: HDF5, targeting the broadest audience, and artroot, targeting users that are familiar with the software infrastructure of Fermilab neutrino experiments and more in general of HEP experiments. The HDF5 files are stored on Zenodo, together with a list of artroot files accessible with xrootd.This sample includes simulated interactions of neutrinos from the Booster Neutrino Beam (BNB), overlaid on top of cosmic ray data. The sample is inclusive, i.e. it includes all types of neutrinos and interactions, with relative abundance matching our nominal flux and cross section models. Interactions are simulated in in the whole cryostat volume.The HDF5 files in this sample do not include the information at the wire waveform level ("NoWire" label), allowing for larger number of events to be included in the data set.More documentation, including detailed description of content, recipes, and example usage, at https://github.com/uboone/OpenSamples.Suggested text for acknowledgment is the following:We acknowledge the MicroBooNE Collaboration for making publicly available the data sets [data set DOIs] employed in this work. These data sets consist of simulated neutrino interactions from the Booster Neutrino Beamline overlaid on top of cosmic data collected with the MicroBooNE detector [2017 JINST 12 P02017].In addition, we request that software products resulting from the usage of the datasets are also made publicly available.
Lunar crater photographs obtained by Ranger VIII AND IX compared with those of laboratory craters overlain with granular material, for depth interpretation
Board that fits over the control panel decreases errors and time loss. Device has cutouts for controls and indicators, bears all appropriate labeling information, can be removed with the changing computer assignments, and guards against accidental changes in control settings.
The machine processing is discussed of spatially variant multitemporal data such as imagery obtained at different times which requires that these data be in geometrical registration so that the analysis processor may obtain the datum for a specified ground resolution element in each of the sets of imagery being utilized for analysis. Misregistration between corresponding subsets of imagery contains both a displacement and a geometrical distortion component. Search techniques utilizing the moduli of the Fourier Transforms are developed for estimating the coefficients of geometrical distortion components of this model. Following the correction of these distortion components, the displacement is located by the crosscorrelation of a template obtained from one set of data, termed the reference, with the second, or background data. This template, derived for the optimum discrimination of the reference data embedded in the background, is determined by the solution of a system of equations involving the reference data and the covariance matrix of these data.
When one image (the signal) is to be registered with a second image (the signal plus noise) of the same scene, one would like to know the accuracy possible for this registration. This paper derives an estimate of the variance of the registration error that can be expected via two approaches. The solution in each instance is found to be a function of the effective bandwidth of the signal and the noise, and the signal-to-noise ratio. Application of these results to LANDSAT-1 data indicates that for most cases, registration variances will be significantly less than the diameter of one picture element.
Software has been written in FORTRAN IV for a Varian 73 computer which reformats LANDSAT-data-derived surface classifications and pictorial representations into a digital array which corresponds to the Universal Transverse Mercator Grid.