Publications

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This report documents work done for a late-start LDRD project, which was carried out during the last quarter of FY07. The objective of this project was to experimentally explore the feasibility of converting vegetable (e.g., soybean) oils to biodiesel by employing slit-channel reactors and solid catalysts. We first designed and fabricated several slit-channel reactors with varying channel depths, and employed them to investigate the improved performance of slit-channel reactors over traditional batch reactors using a NaOH liquid catalyst. We then evaluated the effectiveness of several solid catalysts, including CaO, ZnO, MgO, ZrO{sub 2}, calcium gluconate, and heteropolyacid or HPA (Cs{sub 2.5}H{sub 0.5}PW{sub 12}O{sub 40}), for catalyzing the soybean oil-to-biodiesel transesterification reaction. We found that the slit-channel reactor performance improves as channel depth decreases, as expected; and the conversion efficiency of a slit-channel reactor is significantly higher when its channel is very shallow. We further confirmed CaO as having the highest catalytic activity among the solid catalysts tested, and we demonstrated for the first time calcium gluconate as a promising solid catalyst for converting soybean oil to biodiesel, based on our preliminary batch-mode conversion experiments.

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This LDRD began as a three year program to integrate through-wafer vias, micro-mirrors and control electronics with high-voltage capability to yield a 64 by 64 array of individually controllable micro-mirrors on 125 or 250 micron pitch with piston, tip and tilt movement. The effort was a mix of R&D and application. Care was taken to create SUMMiT{trademark} (Sandia's ultraplanar, multilevel MEMS technology) compatible via and mirror processes, and the ultimate goal was to mate this MEMS fabrication product to a complementary metal-oxide semiconductor (CMOS) electronics substrate. Significant progress was made on the via and mirror fabrication and design, the attach process development as well as the electronics high voltage (30 volt) and control designs. After approximately 22 months, the program was ready to proceed with fabrication and integration of the electronics, final mirror array, and through wafer vias to create a high resolution OMEMS array with individual mirror electronic control. At this point, however, mission alignment and budget constraints reduced the last year program funding and redirected the program to help support the through-silicon via work in the Hyper-Temporal Sensors (HTS) Grand Challenge (GC) LDRD. Several months of investigation and discussion with the HTS team resulted in a revised plan for the remaining 10 months of the program. We planned to build a capability in finer-pitched via fabrication on thinned substrates along with metallization schemes and bonding techniques for very large arrays of high density interconnects (up to 2000 x 2000 vias). Through this program, Sandia was able to build capability in several different conductive through wafer via processes using internal and external resources, MEMS mirror design and fabrication, various bonding techniques for arrayed substrates, and arrayed electronics control design with high voltage capability.

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The combination of hyperspectral confocal fluorescence microscopy and multivariate curve resolution (MCR) provides an ideal system for improved quantitative imaging when multiple fluorophores are present. However, the presence of multiple noise sources limits the ability of MCR to accurately extract pure-component spectra when there is high spectral and/or spatial overlap between multiple fluorophores. Previously, MCR results were improved by weighting the spectral images for Poisson-distributed noise, but additional noise sources are often present. We have identified and quantified all the major noise sources in hyperspectral fluorescence images. Two primary noise sources were found: Poisson-distributed noise and detector-read noise. We present methods to quantify detector-read noise variance and to empirically determine the electron multiplying CCD (EMCCD) gain factor required to compute the Poisson noise variance. We have found that properly weighting spectral image data to account for both noise sources improved MCR accuracy. In this paper, we demonstrate three weighting schemes applied to a real hyperspectral corn leaf image and to simulated data based upon this same image. MCR applied to both real and simulated hyperspectral images weighted to compensate for the two major noise sources greatly improved the extracted pure emission spectra and their concentrations relative to MCR with either unweighted or Poisson-only weighted data. Thus, properly identifying and accounting for the major noise sources in hyperspectral images can serve to improve the MCR results. These methods are very general and can be applied to the multivariate analysis of spectral images whenever CCD or EMCCD detectors are used. Copyright © 2008 John Wiley & Sons, Ltd.

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