Publications

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This report describes the high-level accomplishments from the Plasma Science and Engineering Grand Challenge LDRD at Sandia National Laboratories. The Laboratory has a need to demonstrate predictive capabilities to model plasma phenomena in order to rapidly accelerate engineering development in several mission areas. The purpose of this Grand Challenge LDRD was to advance the fundamental models, methods, and algorithms along with supporting electrode science foundation to enable a revolutionary shift towards predictive plasma engineering design principles. This project integrated the SNL knowledge base in computer science, plasma physics, materials science, applied mathematics, and relevant application engineering to establish new cross-laboratory collaborations on these topics. As an initial exemplar, this project focused efforts on improving multi-scale modeling capabilities that are utilized to predict the electrical power delivery on large-scale pulsed power accelerators. Specifically, this LDRD was structured into three primary research thrusts that, when integrated, enable complex simulations of these devices: (1) the exploration of multi-scale models describing the desorption of contaminants from pulsed power electrodes, (2) the development of improved algorithms and code technologies to treat the multi-physics phenomena required to predict device performance, and (3) the creation of a rigorous verification and validation infrastructure to evaluate the codes and models across a range of challenge problems. These components were integrated into initial demonstrations of the largest simulations of multi-level vacuum power flow completed to-date, executed on the leading HPC computing machines available in the NNSA complex today. These preliminary studies indicate relevant pulsed power engineering design simulations can now be completed in (of order) several days, a significant improvement over pre-LDRD levels of performance.

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Tracking nuclear materials production and processing, particularly covert operations, is a key national security concern, given that nuclear materials processing can be a signature of nuclear weapons activities by US adversaries. Covert trafficking can also result in homeland security threats, most notably allowing terrorists to assemble devices such as dirty bombs. Existing methods depend on isotope analysis and do not necessarily detect chronic low-level exposure. In this project, indigenous organisms such as plants, small mammals, and bacteria are utilized as living sensors for the presence of chemicals used in nuclear materials processing. Such 'metabolic fingerprinting' (or 'metabonomics') employs nuclear magnetic resonance (NMR) spectroscopy to assess alterations in organismal metabolism provoked by the environmental presence of nuclear materials processing, for example the tributyl phosphate employed in the processing of spent reactor fuel rods to extract and purify uranium and plutonium for weaponization.

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Our LDRD research project sought to develop an analytical method for detection of chemicals used in nuclear materials processing. Our approach is distinctly different than current research involving hardware-based sensors. By utilizing the response of indigenous species of plants and/or animals surrounding (or within) a nuclear processing facility, we propose tracking 'suspicious molecules' relevant to nuclear materials processing. As proof of concept, we have examined TBP, tributylphosphate, used in uranium enrichment as well as plutonium extraction from spent nuclear fuels. We will compare TBP to the TPP (triphenylphosphate) analog to determine the uniqueness of the metabonomic response. We show that there is a unique metabonomic response within our animal model to TBP. The TBP signature can further be delineated from that of TPP. We have also developed unique methods of instrumental transfer for metabonomic data sets.

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The production and aging of silicone materials remains an important issue in the weapons stockpile due to their utilization in a wide variety of components and systems within the stockpile. Changes in the physical characteristics of silicone materials due to long term desiccation has been identified as one of the major aging effects observed in silicone pad components. Here we report relaxation nuclear magnetic resonance imaging (R-NMRI) spectroscopy characterization of the silica-filled and unfilled polydimethylsiloxane (PDMS) and polydiphenylsiloxane (PDPS) copolymer (M9787) silicone pads within desiccating environments. These studies were directed at providing additional details about the heterogeneity of the desiccation process. Uniform NMR spin-spin relaxation time (T2) images were observed across the pad thickness indicating that the drying process is approximately uniform, and that the desiccation of the M9787 silicone pad is not a H2O diffusion limited process. In a P2O5 desiccation environment, significant reduction of T2 was observed for the silica-filled and unfilled M9787 silicone pad for desiccation up to 225 days. A very small reduction in T2 was observed for the unfilled copolymer between 225 and 487 days. The increase in relative stiffness with desiccation was found to be higher for the unfilled copolymer. These R-NMRI results are correlated to local changes in the modulus of the material

Many data analysis algorithms that are currently employed in SAW sensors lack the ability to easily maintain calibration models in the presence of unmodeled interferents or sensor drift. The classical least squares/partial least squares (CLS/PLS) hybrid algorithm is tested in this study for its ability to update calibration models for unmodeled interferents and sensor drift with information from only a single recalibration standard. Use of the CLS/PLS hybrid algorithm for calibration and calibration maintenance of surface acoustic wave (SAW) devices was investigated for synthetic mixtures of iso-octane-methanol-water and with synthetic mixtures of nerve agent analogs, di-iso-propyl methyl phosphonate (DIMP)-kerosene-water along with a true ternary mixture of dimethyl methyl phosphonate (DMMP)-kerosene-water. Calibration statistics using the hybrid algorithm were found to be as good as those obtained from a standard partial least squares (PLS) analysis. In prediction, the hybrid algorithm models were found to perform equivalently to PLS models in the absence of unmodeled interferents or sensor drift, with an accuracy of 5-10% of the reference values and a high degree of precision. In the case of prediction in the presence of unmodeled interferents and/or sensor drift, PLS models and prediction augmented CLS/PLS (PACLS/PLS) hybrid models were compared using a single standard sample to update each model for prediction. For the cases studied, PACLS/PLS hybrid models were comparable to or outperformed updated PLS models that used subset recalibration or piece-wise direct standardization.

Two lots of manufactured Type 3a zeolite samples were compared by TGA/IR analysis. The first lot, obtained from Davidson Chemical, a commercial vendor, was characterized during the previous study cycle for its water and water-plus-CO{sub 2} uptake in order to determine whether CO{sub 2} uptake prevented water adsorption by the zeolite. It was determined that CO{sub 2} did not hamper water adsorption using the Davidson zeolite. CO{sub 2} was found on the zeolite surface at dewpoints below -40 C, however it was found to be reversibly adsorbed. During the course of the previous studies, chemical analyses revealed that the Davidson 3a zeolite contained calcium in significant quantities, along with the traditional counterions potassium and sodium. Chemical analysis of a Type 3a zeolite sample retrieved from Kansas City (heretofore referred to as the ''Stores 3a'' sample) indicated that the Stores sample was a more traditional Type 3a zeolite, containing no calcium. TGA/IR studies this year focused on obtaining CO{sub 2} and water absorbance data from the Stores 3a zeolite. Within the Stores 3a sample, CO{sub 2} was found to be reversibly absorbed within the sample, but only at and below -60 C with 5% CO{sub 2} loading. The amount of CO{sub 2} observed eluting from the Stores zeolite at this condition was similar to what was observed from the Davidson zeolite sample but with a greater uncertainty in the measured value. The results of the Stores 3a studies are summarized within this report.

The effect of non-exponential and multi-exponential decay or relaxation behavior on the performance of the direct exponential curve resolution algorithm (DECRA) is investigated through a series of numerical simulations. Three different combinations of decay or relaxation behavior were investigated through DECRA analysis of simulated pulse gradient spin echo (PGSE) NMR diffusion spectra that contained the combination of two individual components. The diffusion decay behavior of one component was described by a single-exponential decay, while the second component was described by either (1) a multi-exponential decay, (2) a decay behavior described by the empirical Kohlrausch-Williams-Watts (KWW) relation or (3) a multi-exponential decay behavior correlated with variations in the NMR spectral line shape. The magnitudes and types of errors produced during the DECRA analysis of spectral data with deviations from a pure single-exponential decay behavior are presented. It is demonstrated that the deviation from single-exponential decay impacts the resulting calculated line shapes, the calculated relative concentrations and the quantitative estimation of the decay or relaxation time constants of both components present in the NMR spectra. Copyright © 2004 John Wiley & Sons, Ltd.

A sample of polymeric propellant binder was aged from 0 to 60 days at 95 C and analyzed using FT-IR step scan photoacoustic spectroscopy. This technique has the ability of to obtain spectra of the polymer as a function of depth into the polymer material. Multivariate curve resolution was applied to the spectra data obtained to extract the contributions of the aged and un-aged spectral components from the spectra. It was found that multivariate curve resolution could efficiently separate highly overlapped spectra and yielded insights into the aging process.

Chemometric analysis of nuclear magnetic resonance (NMR) spectroscopy has increased dramatically in recent years. Various chemometric techniques have been applied to a wide range of problems in food, agricultural, medical, process, and industrial system. This article gives a brief review of chemometric analysis of NMR spectral data, including a summary of the types of mixtures and experiments analyzed with chemometric techniques. Common experiment problems encountered during the chemometric analysis of NMR data are also discussed.

The goal of this LDRD Research project was to provide a preliminary examination of the use of infrared spectroscopy as a tool to detect the changes in cell cultures upon activation by an infectious agent. Due to a late arrival of funding, only 5 months were available to transfer and setup equipment at UTTM,develop cell culture lines, test methods of in-situ activation and collect kinetic data from activated cells. Using attenuated total reflectance (ATR) as a sampling method, live cell cultures were examined prior to and after activation. Spectroscopic data were collected from cells immediately after activation in situ and, in many cases for five successive hours. Additional data were collected from cells activated within a test tube (pre-activated), in both transmission mode as well as in ATR mode. Changes in the infrared data were apparent in the transmission data collected from the pre-activated cells as well in some of the pre-activated ATR data. Changes in the in-situ activated spectral data were only occasionally present due to (1) the limited time cells were studied and (2) incomplete activation. Comparison of preliminary data to infrared bands reported in the literature suggests the primary changes seen are due an increase in ribonucleic acid (RNA) production. This work will be continued as part of a 3 year DARPA grant.

Multivariate techniques were used to address the quantification of {sup 17}O-NMR (nuclear magnetic resonance) spectra for a series of primary alcohol mixtures. Due to highly overlapping resonances, quantitative spectral evaluation using standard integration and deconvolution techniques proved difficult. Multivariate evaluation of the {sup 17}O-NMR spectral data obtained for 26 mixtures of five primary alcohols demonstrated that obtaining information about spectral overlap and interferences allowed the development of more accurate models. Initial partial least squares (PLS) models developed for the {sup 17}O-NMR data collected from the primary alcohol mixtures resulted in very poor precision, with signal overlap between the different chemical species suspected of being the primary contributor to the error. To directly evaluate the question of spectral overlap in these alcohol mixtures, net analyte signal (NAS) analyses were performed. The NAS results indicate that alcohols with similar chain lengths produced severely overlapping {sup 17}O-NMR resonances. Grouping the alcohols based on chain length allowed more accurate and robust calibration models to be developed.

This report includes the details of the model building procedure and prediction of seismic field data. Principal Components Regression, a multivariate analysis technique, was used to model seismic data collected as two pieces of equipment were cycled on and off. Models built that included only the two pieces of equipment of interest had trouble predicting data containing signals not included in the model. Evidence for poor predictions came from the prediction curves as well as spectral F-ratio plots. Once the extraneous signals were included in the model, predictions improved dramatically. While Principal Components Regression performed well for the present data sets, the present data analysis suggests further work will be needed to develop more robust modeling methods as the data become more complex.