Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Obtaining particulate compositional maps from scanned PIXE (proton-induced X-ray emission) measurements is extremely difficult due to the complexity of analyzing spectroscopic data collected with low signal-to-noise at each scan point (pixel). Multivariate spectral analysis has the potential to analyze such data sets by reducing the PIXE data to a limited number of physically realizable and easily interpretable components (that include both spectral and image information). We have adapted the AXSIA (automated expert spectral image analysis) program, originally developed by Sandia National Laboratories to quantify electron-excited X-ray spectroscopy data, for this purpose. Samples consisting of particulates with known compositions and sizes were loaded onto Mylar and paper filter substrates and analyzed by scanned micro-PIXE. The data sets were processed by AXSIA and the associated principal component spectral data were quantified by converting the weighting images into concentration maps. The results indicate automated, nonbiased, multivariate statistical analysis is useful for converting very large amounts of data into a smaller, more manageable number of compositional components needed for locating individual particles-of-interest on large area collection media.

Spectrum imaging combined with multivariate statistics is an approach to microanalysis that makes the maximum use of the large amount of data potentially collected in forensics analysis. Here, this study examines the efficacy of using spectrum imaging-enabled microscopies to identify chemical signatures in simulated bioagent materials. This approach allowed for the ready discrimination between all samples in the test. In particular, the spectrum imaging approach allowed for the identification of particles with trace elements that would have been missed with a more traditional approach to forensic microanalysis. Finally, the importance of combining signals from multiple length scales and analytical sensitivities is discussed.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Automated, nonbiased, multivariate statistical analysis techniques are useful for converting very large amounts of data into a smaller, more manageable number of chemical components (spectra and images) that are needed to describe the measurement. We report the first use of the multivariate spectral analysis program AXSIA (Automated eXpert Spectral Image Analysis) developed at Sandia National Laboratories to quantitatively analyze micro-PIXE data maps. AXSIA implements a multivariate curve resolution technique that reduces the spectral image data sets into a limited number of physically realizable and easily interpretable components (including both spectra and images). We show that the principal component spectra can be further analyzed using conventional PIXE programs to convert the weighting images into quantitative concentration maps. A common elemental data set has been analyzed using three different PIXE analysis codes and the results compared to the cases when each of these codes is used to separately analyze the associated AXSIA principal component spectral data. We find that these comparisons are in good quantitative agreement with each other. © 2006 Elsevier B.V. All rights reserved.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

HfB{sub 2} and ZrB{sub 2} are of interest for thermal protection materials because of favorable thermal stability, mechanical properties, and oxidation resistance. We have made dense diboride ceramics with 2 to 20 % SiC by hot pressing at 2000 C and 5000 psi. High-resolution transmission electron microscopy (TEM) shows very thin grain boundary phases that suggest liquid phase sintering. Fracture toughness measurements give RT values of 4 to 6 MPam{sup 1/2}. Four-pt flexure strengths measured in air up to 1450 C were as high as 450-500 MPa. Thermal diffusivities were measured to 2000 C for ZrB{sub 2} and HfB{sub 2} ceramics with SiC contents from 2 to 20%. Thermal conductivities were calculated from thermal diffusivities and measured heat capacities. Thermal diffusivities were modeled using different two-phase composite models. These materials exhibit excellent high temperature properties and are attractive for further development for thermal protection systems.

High-purity AlPt thin films prepared by self-propagating, high temperature combustion synthesis show evidence for a new rhombohedral phase. Sputter deposited Al/Pt multilayers of various designs are reacted at different rates in air and in vacuum, and each form a new trigonal/hexagonal aluminide phase with unit cell parameters a = 15.571(8) {angstrom}, c = 5.304(1) {angstrom}, space group R-3 (148), and Z, the number of formula units within a unit cell, = 39. The lattice is isostructural to that of the AlPd R-3 lattice as reported by Matkovic and Schubert (Matkovic, 1977). Reacted films have a random in-plane crystallographic texture, a modest out-of-plane (001) texture, and equiaxed grains with dimensions on the order of film thickness.

The ability to integrate metal and semiconductor micro-systems to perform highly complex functions, such as RF-MEMS, will depend on developing freestanding metal structures that offer improved conductivity, reflectivity, and mechanical properties. Three issues have prevented the proliferation of these systems: (1) warpage of active components due to through-thickness stress gradients, (2) limited component lifetimes due to fatigue, and (3) low yield strength. To address these issues, we focus on developing and implementing techniques to enable the direct study of the stress and microstructural evolution during electrodeposition and mechanical loading. The study of stress during electrodeposition of metal thin films is being accomplished by integrating a multi-beam optical stress sensor into an electrodeposition chamber. By coupling the in-situ stress information with ex-situ microstructural analysis, a scientific understanding of the sources of stress during electrodeposition will be obtained. These results are providing a foundation upon which to develop a stress-gradient-free thin film directly applicable to the production of freestanding metal structures. The issues of fatigue and yield strength are being addressed by developing novel surface micromachined tensile and bend testers, by interferometry, and by TEM analysis. The MEMS tensile tester has a ''Bosch'' etched hole to allow for direct viewing of the microstructure in a TEM before, during, and after loading. This approach allows for the quantitative measurements of stress-strain relations while imaging dislocation motion, and determination of fracture nucleation in samples with well-known fatigue/strain histories. This technique facilitates the determination of the limits for classical deformation mechanisms and helps to formulate a new understanding of the mechanical response as the grain sizes are refined to a nanometer scale. Together, these studies will result in a science-based infrastructure to enhance the production of integrated metal--semiconductor systems and will directly impact RF MEMS and LIGA technologies at Sandia.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Electron backscattered diffraction (EBSD) is a widely used technique for both identifying the crystallographic phase and for mapping the orientation of crystalline materials on the micron length scale. Often the operating conditions necessary for phase identification are not suitable for orientation mapping and vice versa. In an effort to optimize the speed involved in the mapping technique, pattern quality is sacrificed and the wealth of information present in an EBSD pattern is compressed to basically 4 values: a matched phase and three Euler angles. However, ab initio identification of phases from EBSD patterns requires high quality patterns and fairly intense computation. Spectrum imaging is an analytical approach that may offer some solutions to the aforementioned problems. Spectrum imaging consists of collecting a whole spectrum at each pixel in a mapping style measurement. This large set of data is then analyzed using multivariate statistical analysis (MSA) techniques such as principle components analysis, multivariate curve resolution, or other least squares based techniques. The result of these calculations is a set of component spectral shapes with corresponding abundances that allow the analyst to extract the greatest amount of physically relevant information from an otherwise enormous data set. Spectrum imaging has been used successfully in EDX microanalysis (both in the SEM and TEM), TOF-SIMS, WDS, and EELS. To examine the potential benefits of the spectrum imaging approach for EBSD data, a series of basic experiments and calculations were run. Test data sets (20 x 20 patterns in .jpeg format) on polycrystalline Al and on the directionally solidified eutectic oxide, CoO/ZrO{sub 2}(CaO), were collected using the HKL Channel 5 system with a Nordlys detector under normal mapping conditions. The data was collected on a FEI dual beam FIB (model DB235) and a Zeiss (Supra 55 VP) SEM at 20keV for Al and CoO/ZrO{sub 2}(CaO), respectively. The data sets were analyzed according to the schematic shown in Figure 1. Each EBSD pattern was hough transformed, unzipped into a 1-D vector of channels with intensities ranging from 0-255, and then added to an overall data matrix. A range of treatments (edge/no edge detection, spatial simplicity/spectral simplicity, etc.) were examined to determine the optimal way of treating the data. The multivariate analyses were performed using the AXSIA code developed at Sandia National Laboratories. The MSA techniques were able to correctly identify individual grains in the Al sample and individual phases in the CoO/ZrO{sub 2}(CaO) sample. For each component EBSD pattern identified from the Al data, a corresponding color map of abundance can be seen which clearly corresponds to a single grain (Figure 2). The success in the CoO/ZrO{sub 2}(CaO) sample is particularly notable due to both phases sharing the Fm-3m space group which would confuse most autoindexing routines. The range of analytical treatments identified two extremes in results: a minimal number of components (patterns) with only kikuchi line positions present or a larger number of components with full intensity information present. The further application of these results to phase mapping will be discussed.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Time-of-flight secondary ion mass spectrometry (TOF-SIMS) instruments are capable of saving an entire mass spectrum at each pixel of an image, allowing for retrospective analysis of masses that were not selected for analysis during data collection. These TOF-SIMS spectral images contain a wealth of information, but few tools are available to assist the analyst in visualizing the entire raw data set and as a result, most of the data are not analyzed. Automated, nonbiased, multivariate statistical analysis (MVSA) techniques are useful for converting the massive amount of data into a smaller number of chemical components (spectra and images) that are needed to fully describe the TOF-SIMS measurement. Many samples require two back-to-back TOF-SIMS measurements in order to fully characterize the sample, one measurement of the fraction of positively charged secondary ions (positive ion fraction) and one measurement of the fraction of negatively charged secondary ions (negative ion fraction). Each measurement then needs to be individually evaluated. In this paper, we report the first MVSA analysis of a concatenated TOF-SIMS date set comprising positive ion and negative ion spectral images collected on the same region of a sample. MVSA of concatenated data sets provides results that are intuitive and fully describe the sample. The analytical insight provided by MVSA of the concatenated data set was not obtained when either polarity data set was analyzed separately. © 2005 American Chemical Society.

Boron sub-arsenide, B{sub 12}As{sub 2}, is based on twelve-atom clusters of boron atoms and two-atom As-As chains. By contrast, SiC is a tetrahedrally bonded covalent semiconductor. Despite these fundamental differences, the basal plane hexagonal lattice constant of boron sub-arsenide is twice that of SiC. This coincidence suggests the possibility of heteroepitaxial growth of boron sub-arsenide films on properly aligned SiC. However, there are a variety of incommensurate alignments by which heteroepitaxial growth of B{sub 12}As{sub 2} on (0001) 6H-SiC can occur. In this study, we first used geometrical crystallographic considerations to describe the possible arrangements of B{sub 12}As{sub 2} on (0001) 6H-SiC. We identified four translational and two rotational variants. We then analyzed electron backscattered diffraction and transmission electron microscopy images for evidence of distinct domains of such structural variants. Micron-scale regions with each of the two possible rotational alignments of B{sub 12}As{sub 2} icosahedra with the SiC surface were seen. On a finer length scale (100-300 nm) within these regions, boron-rich boundaries were found, consistent with those between pairs of the four equivalent translational variants associated with a two-to-one lattice match. Boron-carbide reaction layers were also observed at interfaces between SiC and B{sub 12}As{sub 2}.

Metallic Phases in extraterrestrial materials are composed of Fe-Ni with minor amounts of Co, P, Si, Cr, etc. Electron microscopy techniques (SEM, TEM, EPMA, AEM) have been used for almost 50 years to study micron and submicron microscopic features in the metal phases (Fig. 1) such as clear taenite, cloudy zone, plessite, etc [1,2]. However lack of instrumentation to prepare TEM thin foils in specific sample locations and to obtain micro-scale crystallographic data have limited these investigations. New techniques such as the focused ion beam (FIB) and the electron backscatter electron diffraction (EBSD) techniques have overcome these limitations. The application of the FIB instrument has allowed us to prepare {approx}10 um long by {approx} 5um deep TEM thin sections of metal phases from specific regions of metal particles, in chondrites, irons and stony iron meteorites, identified by optical and SEM observation. Using a FEI dual beam FIB we were able to study very small metal particles in samples of CH chondrites [3] and zoneless plessite (ZP) in ordinary chondrites. Fig. 2 shows a SEM photomicrograph of a {approx}40 um ZP particle in Kernouve, a H6 chondrite. Fig. 3a,b shows a TEM photograph of a section of the FIB prepared TEM foil of the ZP particle and a Ni trace through a tetrataenite/kamacite region of the particle. It has been proposed that the Widmanstatten pattern in low P iron meteorites forms by martensite decomposition, via the reaction {gamma} {yields} {alpha}{sub 2} + {gamma} {yields} {alpha} + {gamma} in which {alpha}{sub 2}, martensite, decomposes to the equilibrium {alpha} and {gamma} phases during the cooling process [4]. In order to show if this mechanism for Widmanstatten pattern formation is correct, crystallographic information is needed from the {gamma} or taenite phases throughout a given meteorite. The EBSD technique was employed in this study to obtain the orientation of the taenite surrounding the initial martensite phase and the kamacite which forms as {alpha}{sub 2} or as Widmanstatten plates in a series of IVB irons. Fig. 4a,b shows EBSD orientation maps of taenite and kamacite from the Tawallah Valley IVB iron. We observe that the orientation of the taenite in the IVB meteorites is the same throughout the sample consistent with the orientation of the high temperature single phase taenite before formation of the Widmanstatten pattern.

Abstract not provided.

Spectral imaging where a complete spectrum is collected from each of a series of spatial locations (1D lines, 2D images or 3D volumes) is now available on a wide range of analytical tools - from electron and x-ray to ion beam instruments. With this capability to collect extremely large spectral images comes the need for automated data analysis tools that can rapidly and without bias reduce a large number of raw spectra to a compact, chemically relevant, and easily interpreted representation. It is clear that manual interrogation of individual spectra is impractical even for very small spectral images (< 5000 spectra). More typical spectral images can contain tens of thousands to millions of spectra, which given the constraint of acquisition time may contain between 5 and 300 counts per 1000-channel spectrum. Conventional manual approaches to spectral image analysis such as summing spectra from regions or constructing x-ray maps are prone to bias and possibly error. One way to comprehensively analyze spectral image data, which has been automated, is to utilize an unsupervised self-modeling multivariate statistical analysis method such as multivariate curve resolution (MCR). This approach has proven capable of solving a wide range of analytical problems based upon the counting of x-rays (SEM/STEM-EDX, XRF, PIXE), electrons (EELS, XPS) and ions (TOF-SIMS). As an example of the MCR approach, a STEM x-ray spectral image from a ZrB2-SiC composite was acquired and analyzed. The data were generated in a FEI Tecnai F30-ST TEM/STEM operated at 300kV, equipped with an EDAX SUTW x-ray detector. The spectral image was acquired with the TIA software on the STEM at 128 by 128 pixels (12nm/pixel) for 100msec dwell per pixel (total acquisition time was 30 minutes) with a probe of approximately the same size as each pixel. Each spectrum in the image had, on average, 500 counts. The calculation took 5 seconds on a PC workstation with dual 2.4GHz PentiumIV Xeon processors and 2Gbytes of RAM and resulted in four chemically relevant components, which are shown in Figure 1. The analysis region was at a triple junction of three ZrB2 grains that contained zirconium oxide, aluminum oxide and a glass phase. The power of unbiased statistical methods, such as MCR as applied here, is that no a priori knowledge of the material's chemistry is required. The algorithms, in this case, effectively reduced over 16,000 2000-channel spectra (64Mbytes) to four images and four spectral shapes (72kbytes), which in this case represent chemical phases. This three order of magnitude compression is achieved rapidly with no loss of chemical information. There is also the potential to correlate multiple analytical techniques like, for example, EELS and EDS in the STEM adding sensitivity to light elements as well as bonding information for EELS to the more comprehensive spectral coverage of EDS.

Microanalysis is typically performed to analyze the near surface of materials. There are many instances where chemical information about the third spatial dimension is essential to the solution of materials analyses. The majority of 3D analyses however focus on limited spectral acquisition and/or analysis. For truly comprehensive 3D chemical characterization, 4D spectral images (a complete spectrum from each volume element of a region of a specimen) are needed. Furthermore, a robust statistical method is needed to extract the maximum amount of chemical information from that extremely large amount of data. In this paper, an example of the acquisition and multivariate statistical analysis of 4D (3-spatial and 1-spectral dimension) x-ray spectral images is described. The method of utilizing a single- or dual-beam FIB (w/o or w/SEM) to get at 3D chemistry has been described by others with respect to secondary-ion mass spectrometry. The basic methodology described in those works has been modified for comprehensive x-ray microanalysis in a dual-beam FIB/SEM (FEI Co. DB-235). In brief, the FIB is used to serially section a site-specific region of a sample and then the electron beam is rastered over the exposed surfaces with x-ray spectral images being acquired at each section. All this is performed without rotating or tilting the specimen between FIB cutting and SEM imaging/x-ray spectral image acquisition. The resultant 4D spectral image is then unfolded (number of volume elements by number of channels) and subjected to the same multivariate curve resolution (MCR) approach that has proven successful for the analysis of lower-dimension x-ray spectral images. The TSI data sets can be in excess of 4Gbytes. This problem has been overcome (for now) and images up to 6Gbytes have been analyzed in this work. The method for analyzing such large spectral images will be described in this presentation. A comprehensive 3D chemical analysis was performed on several corrosion specimens of Cu electroplated with various metals. Figure 1A shows the top view of the localized corrosion region prepared for FIB sectioning. The TSI region has been coated with Pt and a trench has been milled along the bottom edge of the region, exposing it to the electron beam as seen in Figure 1B. The TSI consisted of 25 sections and was approximately 6Gbytes. Figure 1C shows several of the components rendered in 3D: Green is Cu; blue is Pb; cyan represents one of the corrosion products that contains Cu, Zn, O, S, and C; and orange represents the other corrosion product with Zn, O, S and C. Figure 1 D shows all of the component spectral shapes from the analysis. There is severe pathological overlap of the spectra from Ni, Cu and Zn as well as Pb and S. in spite of this clean spectral shapes have been extracted from the TSI. This powerful TSI technique could be applied to other sectioning methods well.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Sandia and Rontec have developed an annular, 12-element, 60 mm{sup 2}, Peltier-cooled, translatable, silicon drift detector called the SDD-12. The body of the SDD-12 is only 22.8 mm in total thickness and easily fits between the sample and the upstream wall of the Sandia microbeam chamber. At a working distance of 1 mm, the solid angle is 1.09 sr. The energy resolution is 170 eV at count rates <40 kcps and 200 eV for rates of 1 Mcps. X-ray count rates must be maintained below 50 kcps when protons are allowed to strike the full area of the SDD. Another innovation with this new {mu}PIXE system is that the data are analyzed using Sandia's Automated eXpert Spectral Image Analysis (AXSIA).

Abstract not provided.