Tracking the structure of heterogeneous catalysts under operando conditions remains a challenge due to the paucity of experimental techniques that can provide atomic-level information for catalytic metal species. Here we report on the use of X-ray absorption near edge structure (XANES) spectroscopy and supervised machine learning (SML) for refining the three-dimensional geometry of metal catalysts. SML is used to unravel the hidden relationship between the XANES features and catalyst geometry. To train our SML method, we rely on ab-initio XANES simulations. Our approach allows one to solve the structure of a metal catalyst from its experimental XANES, as demonstrated heremore by reconstructing the average size, shape and morphology of well-defined platinum nanoparticles. This method is applicable to the determination of the nanoparticle structure in operando studies and can be generalized to other nanoscale systems. In conclusion, it also allows on-the-fly XANES analysis, and is a promising approach for high-throughput and time-dependent studies. less
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Computer vision techniques which have the potential for use on the space station and related applications are assessed. A knowledge-based vision system (expert vision system) and the development of a demonstration system for it are described. This system implements some of the capabilities that would be necessary in a machine vision system for the robot arm of the laboratory module in the space station. A Perceptics 9200e image processor, on a host VAXstation, was used to develop the demonstration system. In order to use realistic test images, photographs of actual space shuttle simulator panels were used. The system's capabilities of scene identification and scene matching are discussed.
NASA Langley Research Center is working on a continuous wave (CW) laser based remote sensing scheme for the detection of CO2 and O2 from space based platforms suitable for ACTIVE SENSING OF CO2 EMISSIONS OVER NIGHTS, DAYS, AND SEASONS (ASCENDS) mission. ASCENDS is a future space-based mission to determine the global distribution of sources and sinks of atmospheric carbon dioxide (CO2). A unique, multi-frequency, intensity modulated CW (IMCW) laser absorption spectrometer (LAS) operating at 1.57 micron for CO2 sensing has been developed. Effective aerosol and cloud discrimination techniques are being investigated in order to determine concentration values with accuracies less than 0.3%. In this paper, we discuss the demonstration of a pseudo noise (PN) code based technique for cloud and aerosol discrimination applications. The possibility of using maximum length (ML)-sequences for range and absorption measurements is investigated. A simple model for accomplishing this objective is formulated, Proof-of-concept experiments carried out using SONAR based LIDAR simulator that was built using simple audio hardware provided promising results for extension into optical wavelengths. 2ff7e9595c
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