Publications

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Tomographic spectral imaging is a powerful technique for the 3D analysis of materials. The present work describes the application of this technique to the analysis of localized corrosion of a connector pin. Implemented via serial sectioning in a focused ion-beam/scanning electron microscope, electron-excited x-ray spectra were acquired from each voxel in a 3D array. The resultant tomographic spectral image was analyzed in its entirety with Sandia's Automated eXpert Spectral Image Analysis multivariate statistical analysis software. The result of the analysis is a small number of chemical components which describe the 3D phase distribution in the volume of material sampled. © 2012 IEEE.

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Tomographic spectral imaging is a powerful technique for the three-dimensional (3-D) analysis of materials. Using a focused ion-beam/scanning electron microscope equipped with an x-ray spectrometer, 3-D microanalysis can be performed on individual regions of a sample, such as defects, with microanalytical spatial resolution of better than 300 nm typically. The focused ion-beam can serially section at comparable thicknesses to sequentially reveal new analytical surfaces within the specimen. After each slice a full 2-spatial dimension spectral image, consisting of a complete spectrum at each point in the 2-D array, is acquired with the scanning electron microscope/energy-dispersive x-ray spectrometer on the same platform. The process is repeated multiple times to result in a 3-D or tomographic spectral image. The challenge is to effectively and efficiently analyze the tomographic spectral image to extract chemical phase distributions. Therefore, automated multivariate statistical analysis methods were developed and applied to these images. Sandia's Automated eXpert Spectral Image Analysis multivariate statistical analysis software requires no a priori information to find even very weak signals hidden in the data sets. The result of the analysis is a small number of chemical components which describe the 3-D phase distribution in the volume of material sampled. These 3-D phases can then be effectively visualized with off-the-shelf 3-D rendering software. © 2011 TMS.

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A reliability and availability model has been developed for a portion of the 4.6 megawatt (MWdc) photovoltaic system operated by Tucson Electric Power (TEP) at Springerville, Arizona using a commercially available software tool, GoldSim{trademark}. This reliability model has been populated with life distributions and repair distributions derived from data accumulated during five years of operation of this system. This reliability and availability model was incorporated into another model that simulated daily and seasonal solar irradiance and photovoltaic module performance. The resulting combined model allows prediction of kilowatt hour (kWh) energy output of the system based on availability of components of the system, solar irradiance, and module and inverter performance. This model was then used to study the sensitivity of energy output as a function of photovoltaic (PV) module degradation at different rates and the effect of location (solar irradiance). Plots of cumulative energy output versus time for a 30 year period are provided for each of these cases.

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A series of thin electrodeposited Cu foils and Cu foil/Kapton flex circuits were tested in bending fatigue according to ASTM E796 and IPC-TM-650. The fatigue behavior was analyzed in terms of strain vs. number of cycles to failure, using a Coffin-Manson approach. The effects of Cu foil thickness and Cu trace width are discussed. The Cu foils performed as expected and the Cu foil/Kapton® (E.I. du Pont de Nemours and Company, Wilmington, DE) composites showed significant improvement in fatigue lifetime due to the composite strengthening effect of the Kapton layers. However, the flex circuits showed more scatter in fatigue life based on electrical continuity. The effect of the Kapton layers manifests itself by significantly more widespread microcracking in the Cu traces and the extent of microcracking depended on the strain level. *Sandia is a multiprogram laboratory operated by Sandia Corporation, a Lockheed Martin Company, for the United States Department of Energy's National Nuclear Security Administration under contract DE-AC04-94AL85000. © 2008 MS&T'08 ®.

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The goal of this study is to model the electrical response of gold plated copper electrical contacts exposed to a mixed flowing gas stream consisting of air containing 10 ppb H{sub 2}S at 30 C and a relative humidity of 70%. This environment accelerates the attack normally observed in a light industrial environment (essentially a simplified version of the Battelle Class 2 environment). Corrosion rates were quantified by measuring the corrosion site density, size distribution, and the macroscopic electrical resistance of the aged surface as a function of exposure time. A pore corrosion numerical model was used to predict both the growth of copper sulfide corrosion product which blooms through defects in the gold layer and the resulting electrical contact resistance of the aged surface. Assumptions about the distribution of defects in the noble metal plating and the mechanism for how corrosion blooms affect electrical contact resistance were needed to complete the numerical model. Comparisons are made to the experimentally observed number density of corrosion sites, the size distribution of corrosion product blooms, and the cumulative probability distribution of the electrical contact resistance. Experimentally, the bloom site density increases as a function of time, whereas the bloom size distribution remains relatively independent of time. These two effects are included in the numerical model by adding a corrosion initiation probability proportional to the surface area along with a probability for bloom-growth extinction proportional to the corrosion product bloom volume. The cumulative probability distribution of electrical resistance becomes skewed as exposure time increases. While the electrical contact resistance increases as a function of time for a fraction of the bloom population, the median value remains relatively unchanged. In order to model this behavior, the resistance calculated for large blooms has been weighted more heavily.

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