Publications

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Formation of complex nanomaterials would ideally involve single-pot reaction conditions with one reactive site per nanoparticle, resulting in a high yield of incrementally modified or oriented structures. Many studies in nanoparticle functionalization have sought to generate highly uniform nanoparticles with tailorable surface chemistry necessary to produce such conjugates, with limited success. In order to overcome these limitations, we have modified commercially available nanoparticles with multiple potential reaction sites for conjugation with single ssDNAs, proteins, and small unilamellar vesicles. These approaches combined heterobifunctional and biochemical template chemistries with single molecule optical methods for improved control of nanomaterial functionalization. Several interesting analytical results have been achieved by leveraging techniques unique to SNL, and provide multiple paths for future improvements for multiplex nanoparticle synthesis and characterization. Hyperspectral imaging has proven especially useful for assaying substrate immobilized fluorescent particles. In dynamic environments, temporal correlation spectroscopies have been employed for tracking changes in diffusion/hydrodynamic radii, particle size distributions, and identifying mobile versus immobile sample fractions at unbounded dilution. Finally, Raman fingerprinting of biological conjugates has been enabled by resonant signal enhancement provided by intimate interactions with nanoparticles and composite nanoshells.

This report describes recent progress made in developing and utilizing hybrid Simulated, Emulated, and Physical Investigative Analysis (SEPIA) environments. Many organizations require advanced tools to analyze their information system's security, reliability, and resilience against cyber attack. Today's security analysis utilize real systems such as computers, network routers and other network equipment, computer emulations (e.g., virtual machines) and simulation models separately to analyze interplay between threats and safeguards. In contrast, this work developed new methods to combine these three approaches to provide integrated hybrid SEPIA environments. Our SEPIA environments enable an analyst to rapidly configure hybrid environments to pass network traffic and perform, from the outside, like real networks. This provides higher fidelity representations of key network nodes while still leveraging the scalability and cost advantages of simulation tools. The result is to rapidly produce large yet relatively low-cost multi-fidelity SEPIA networks of computers and routers that let analysts quickly investigate threats and test protection approaches.

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This report summarizes work to date on a new collaboration between Sandia National Laboratories and the California Institute of Technology (Caltech) to utilize nanoelectromechanical resonators designed at Caltech as platforms to measure the mechanical properties of polymeric materials at length scales on the order of 10-50 nm. Caltech has succeeded in reproducibly building cantilever resonators having major dimensions on the order of 2-5 microns. These devices are fabricated in pairs, with free ends separated by reproducible gaps having dimensions on the order of 10-50 nm. By controlled placement of materials that bridge the very small gap between resonators, the mechanical devices become coupled through the test material, and the transmission of energy between the devices can be monitored. This should allow for measurements of viscoelastic properties of polymeric materials at high frequency over short distances. Our work to date has been directed toward establishing this measurement capability at Sandia.

This report summarizes existing statistical engines in VTK/Titan and presents the recently parallelized contingency statistics engine. It is a sequel to [PT08] and [BPRT09] which studied the parallel descriptive, correlative, multi-correlative, and principal component analysis engines. The ease of use of this new parallel engines is illustrated by the means of C++ code snippets. Furthermore, this report justifies the design of these engines with parallel scalability in mind; however, the very nature of contingency tables prevent this new engine from exhibiting optimal parallel speed-up as the aforementioned engines do. This report therefore discusses the design trade-offs we made and study performance with up to 200 processors.

Data mining and machine learning techniques can be applied to computer system design to aid in optimizing design decisions, improving system runtime performance. Data mining techniques have been investigated in the context of branch prediction. Specifically, a comparison of traditional branch predictor performance has been made to data mining algorithms. Additionally, the possiblity of whether additional features available within the architectural state might serve to further improve branch prediction has been evaluated. Results show that data mining techniques indicate potential for improved branch prediction, especially when register file contents are included as a feature set.

Plasmonic structures open up new opportunities in photonic devices, sometimes offering an alternate method to perform a function and sometimes offering capabilities not possible with standard optics. In this LDRD we successfully demonstrated metal coatings on optical surfaces that do not adversely affect the transmission of those surfaces at the design frequency. This technology could be applied as an RF noise blocking layer across an optical aperture or as a method to apply an electric field to an active electro-optic device without affecting optical performance. We also demonstrated thin optical absorbers using similar patterned surfaces. These infrared optical antennas show promise as a method to improve performance in mercury cadmium telluride detectors. Furthermore, these structures could be coupled with other components to lead to direct rectification of infrared radiation. This possibility leads to a new method for infrared detection and energy harvesting of infrared radiation.

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A molecular fountain directs slowly moving molecules against gravity to further slow them to translational energies that they can be trapped and studied. If the molecules are initially slow enough they will return some time later to the position from which they were launched. Because this round trip time can be on the order of a second a single molecule can be observed for times sufficient to perform Hz level spectroscopy. The goal of this LDRD proposal was to construct a novel Molecular Fountain apparatus capable of producing dilute samples of molecules at near zero temperatures in well-defined user-selectable, quantum states. The slowly moving molecules used in this research are produced by the previously developed Kinematic Cooling technique, which uses a crossed atomic and molecular beam apparatus to generate single rotational level molecular samples moving slowly in the laboratory reference frame. The Kinematic Cooling technique produces cold molecules from a supersonic molecular beam via single collisions with a supersonic atomic beam. A single collision of an atom with a molecule occurring at the correct energy and relative velocity can cause a small fraction of the molecules to move very slowly vertically against gravity in the laboratory. These slowly moving molecules are captured by an electrostatic hexapole guiding field that both orients and focuses the molecules. The molecules are focused into the ionization region of a time-of-flight mass spectrometer and are ionized by laser radiation. The new molecular fountain apparatus was built utilizing a new design for molecular beam apparatus that has allowed us to miniaturize the apparatus. This new design minimizes the volumes and surface area of the machine allowing smaller pumps to maintain the necessary background pressures needed for these experiments.

The fundamental spontaneous emission rate for a photon source can be modified by placing the emitter inside a periodic dielectric structure allowing the emission to be dramatically enhanced or suppressed depending on the intended application. We have investigated the relatively unexplored realm of interaction between semiconductor emitters and three dimensional photonic crystals in the visible spectrum. Although this interaction has been investigated at longer wavelengths, very little work has been done in the visible spectrum. During the course of this LDRD, we have fabricated TiO{sub 2} logpile photonic crystal structures with the shortest wavelength band gap ever demonstrated. A variety of different emitters with emission between 365 nm and 700 nm were incorporated into photonic crystal structures. Time-integrated and time-resolved photoluminescence measurements were performed to measure changes to the spontaneous emission rate. Both enhanced and suppressed emission were demonstrated and attributed to changes to the photonic density of states.

The 9/30/2009 ASC Level 2 Scalable Analysis Tools for Sensitivity Analysis and UQ (Milestone 3160) contains feature recognition capability required by the user community for certain verification and validation tasks focused around sensitivity analysis and uncertainty quantification (UQ). These feature recognition capabilities include crater detection, characterization, and analysis from CTH simulation data; the ability to call fragment and crater identification code from within a CTH simulation; and the ability to output fragments in a geometric format that includes data values over the fragments. The feature recognition capabilities were tested extensively on sample and actual simulations. In addition, a number of stretch criteria were met including the ability to visualize CTH tracer particles and the ability to visualize output from within an S3D simulation.

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Autoignition chemistry is central to predictive modeling of many advanced engine designs that combine high efficiency and low inherent pollutant emissions. This chemistry, and especially its pressure dependence, is poorly known for fuels derived from heavy petroleum and for biofuels, both of which are becoming increasingly prominent in the nation's fuel stream. We have investigated the pressure dependence of key ignition reactions for a series of molecules representative of non-traditional and alternative fuels. These investigations combined experimental characterization of hydroxyl radical production in well-controlled photolytically initiated oxidation and a hybrid modeling strategy that linked detailed quantum chemistry and computational kinetics of critical reactions with rate-equation models of the global chemical system. Comprehensive mechanisms for autoignition generally ignore the pressure dependence of branching fractions in the important alkyl + O{sub 2} reaction systems; however we have demonstrated that pressure-dependent 'formally direct' pathways persist at in-cylinder pressures.

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The goal of this research was to combine theoretical and computational approaches to better understand the potential emergent behaviors of large-scale cyber systems, such as networks of {approx} 10{sup 6} computers. The scale and sophistication of modern computer software, hardware, and deployed networked systems have significantly exceeded the computational research community's ability to understand, model, and predict current and future behaviors. This predictive understanding, however, is critical to the development of new approaches for proactively designing new systems or enhancing existing systems with robustness to current and future cyber threats, including distributed malware such as botnets. We have developed preliminary theoretical and modeling capabilities that can ultimately answer questions such as: How would we reboot the Internet if it were taken down? Can we change network protocols to make them more secure without disrupting existing Internet connectivity and traffic flow? We have begun to address these issues by developing new capabilities for understanding and modeling Internet systems at scale. Specifically, we have addressed the need for scalable network simulation by carrying out emulations of a network with {approx} 10{sup 6} virtualized operating system instances on a high-performance computing cluster - a 'virtual Internet'. We have also explored mappings between previously studied emergent behaviors of complex systems and their potential cyber counterparts. Our results provide foundational capabilities for further research toward understanding the effects of complexity in cyber systems, to allow anticipating and thwarting hackers.

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The purpose of this project was to investigate the use of Bayesian methods for the estimation of the reliability of complex systems. The goals were to find methods for dealing with continuous data, rather than simple pass/fail data; to avoid assumptions of specific probability distributions, especially Gaussian, or normal, distributions; to compute not only an estimate of the reliability of the system, but also a measure of the confidence in that estimate; to develop procedures to address time-dependent or aging aspects in such systems, and to use these models and results to derive optimal testing strategies. The system is assumed to be a system of systems, i.e., a system with discrete components that are themselves systems. Furthermore, the system is 'engineered' in the sense that each node is designed to do something and that we have a mathematical description of that process. In the time-dependent case, the assumption is that we have a general, nonlinear, time-dependent function describing the process. The major results of the project are described in this report. In summary, we developed a sophisticated mathematical framework based on modern probability theory and Bayesian analysis. This framework encompasses all aspects of epistemic uncertainty and easily incorporates steady-state and time-dependent systems. Based on Markov chain, Monte Carlo methods, we devised a computational strategy for general probability density estimation in the steady-state case. This enabled us to compute a distribution of the reliability from which many questions, including confidence, could be addressed. We then extended this to the time domain and implemented procedures to estimate the reliability over time, including the use of the method to predict the reliability at a future time. Finally, we used certain aspects of Bayesian decision analysis to create a novel method for determining an optimal testing strategy, e.g., we can estimate the 'best' location to take the next test to minimize the risk of making a wrong decision about the fitness of a system. We conclude this report by proposing additional fruitful areas of research.

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