Publications

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A heteroleptic titanium metal alkoxide (OPy){sub 2}Ti(4MP){sub 2}, where OPy = NC{sub 5}H{sub 4}(CH{sub 2}O)-2 and 4MP = OC{sub 6}H{sub 4}(SH)-4, was investigated as a candidate precursor for the solution-based (sol-gel) synthesis of titanium oxide via the photoactivation of intermolecular linking reactions (e.g., hydrolysis/condensation). The evolution of the electronic structure of the solution-based molecule arising from conventional (dark) chemical reaction kinetics was compared with that of samples exposed to ultraviolet (UV) radiation at wavelengths of {lambda} = 337.1 nm and 405 nm using UV-visible absorption spectroscopy. Photoinduced changes in the spectra were examined as a function of both the incident wavelength of exposure and the total fluence. Experimental results confirm the UV-induced modification of spectral absorption features, attributed to ligand-localized and charge transfer transitions accompanied by structural changes associated with hydrolysis and condensation. The photoenhancement of reaction kinetics in these processes was confirmed by the increased modification of the absorption features in the solution spectra, which saturated more rapidly under UV-illumination than under dark conditions. Similar saturation behaviors were observed for both the 337.1 nm and the 405 nm incident wavelengths with the same total deposited energy density indicating a relative insensitivity of the photoinduced response to excitation energy for the wavelengths and fluences studied.

Standoff neutron detection technology has advanced in recent years, primarily for counterterrorism applications. Sandia National Laboratories has developed the Neutron Scatter Camera -- a fast neutron imaging system using liquid scintillator with potential applications in long range neutron detection. This talk will explore the pros, cons and practical uses of the Neutron Scatter Camera versus more traditional neutron detectors such as He-3 proportional counters. Several applications for neutron detection and imaging will be explored. We will perform predictive calculations of the response of the Neutron Scatter Camera and traditional He-3 detectors. The applications range from counterterrorism to arms control to safeguards. We will discuss future evolution of the scatter camera to enhance long range detection.

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Many systems involving chemical reactions between small numbers of molecules exhibit inherent stochastic variability. Such stochastic reaction networks are at the heart of processes such as gene transcription, cell signaling or surface catalytic reactions, which are critical to bioenergy, biomedical, and electrical storage applications. The underlying molecular reactions are commonly modeled with chemical master equations (CMEs), representing jump Markov processes, or stochastic differential equations (SDEs), rather than ordinary differential equations (ODEs). As such reaction networks are often inferred from noisy experimental data, it is not uncommon to encounter large parametric uncertainties in these systems. Further, a wide range of time scales introduces the need for reduced order representations. Despite the availability of mature tools for uncertainty/sensitivity analysis and reduced order modeling in deterministic systems, there is a lack of robust algorithms for such analyses in stochastic systems. In this talk, we present advances in algorithms for predictability and reduced order representations for stochastic reaction networks and apply them to bistable systems of biochemical interest. To study the predictability of a stochastic reaction network in the presence of both parametric uncertainty and intrinsic variability, an algorithm was developed to represent the system state with a spectral polynomial chaos (PC) expansion in the stochastic space representing parametric uncertainty and intrinsic variability. Rather than relying on a non-intrusive collocation-based Galerkin projection [1], this PC expansion is obtained using Bayesian inference, which is ideally suited to handle noisy systems through its probabilistic formulation. To accommodate state variables with multimodal distributions, an adaptive multiresolution representation is used [2]. As the PC expansion directly relates the state variables to the uncertain parameters, the formulation lends itself readily to sensitivity analysis. Reduced order modeling in the time dimension is accomplished using a Karhunen-Loeve (KL) decomposition of the stochastic process in terms of the eigenmodes of its covariance matrix. Subsequently, a Rosenblatt transformation relates the random variables in the KL decomposition to a set of independent random variables, allowing the representation of the system state with a PC expansion in those independent random variables. An adaptive clustering method is used to handle multimodal distributions efficiently, and is well suited for high-dimensional spaces. The spectral representation of the stochastic reaction networks makes these systems more amenable to analysis, enabling a detailed understanding of their functionality, and robustness under experimental data uncertainty and inherent variability.

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In recent papers the authors discussed the advantages of forming spotlight-mode SAR imagery from phase history data via a technique that is rooted in the principles of phased-array beamforming, which is closely related to back-projection. The application of a traditional autofocus algorithm, such as Phase Gradient Autofocus (PGA), requires some care in this situation. Specifically, a stated advantage of beamforming is that it easily allows for reconstruction of the SAR image onto an arbitrary imaging grid. One very useful grid, for example, is a Cartesian grid in the ground plane. Autofocus via PGA for such an image, however, cannot be performed in a straightforward manner, because in PGA a Fourier transform relationship is required between the image domain and the range-compressed phase history, and this is not the case for such an imaging grid. In this paper we propose a strategy for performing autofocus in this situation, and discuss its limitations. We demonstrate the algorithm on synthetic phase errors applied to real SAR imagery.

We report the observation of an even-denominator fractional quantum Hall state at {nu}=1/4 in a high quality, wide GaAs quantum well. The sample has a quantum well width of 50 nm and an electron density of n{sub e}=2.55 x 10{sup 11} cm{sup -2}. We have performed transport measurements at T{approx}35 mK in magnetic fields up to 45 T. When the sample is perpendicular to the applied magnetic field, the diagonal resistance displays a kink at {nu}=1/4. Upon tilting the sample to an angle of {theta}=20.3{sup o} a clear fractional quantum Hall state emerges at {nu}=1/4 with a plateau in the Hall resistance and a strong minimum in the diagonal resistance.

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The parent document and cornerstone of the Z136 series of laser safety standards, the revised ANSI Z136.1 (2007) provides guidance for the safe use of lasers and laser systems by defining control measures for each of the four laser classes. As a result of advances in laser devices and applications, new guidelines have been incorporated into this 2007 revision. The new revision should be obtained by all laser end users and is a must for users of class 3B and 4 lasers as it renders all previous editions obsolete. Since the ANSI Z136.1 standard is the foundation of laser safety programs for industrial, military, medical, and educational applications nationwide, revisions to the previous version can and will affect the training and practice of laser safety in these environments. Changes to the previous version include the addition of new laser hazard classification definitions, new requirements for refresher training, and changes to medical surveillance requirements. The ANSI Z136.1 (2007) standard provides an updated and thorough set of guidelines for implementing a safe laser program. In addition to these changes, the standard covers laser safety program provisions including the duties and responsibilities of the LSO, non-beam hazards, administrative/engineering control measures, definitions, optical density, nominal hazard zone (NHZ), MPEs, accessible emission limit (AEL), bioeffects, standard operating procedures (SOPs), and example calculations.

This project is aimed to gain added durability by supporting ripening-resistant dendritic platinum and/or platinum-based alloy nanostructures on carbon. We have developed a new synthetic approach suitable for directly supporting dendritic nanostructures on VXC-72 carbon black (CB), single-walled carbon nanotubes (SWCNTs), and multi-walled carbon nanotubes (MWCNTs). The key of the synthesis is to creating a unique supporting/confining reaction environment by incorporating carbon within lipid bilayer relying on a hydrophobic-hydrophobic interaction. In order to realize size uniformity control over the supported dendritic nanostructures, a fast photocatalytic seeding method based on tin(IV) porphyrins (SnP) developed at Sandia was applied to the synthesis by using SnP-containing liposomes under tungsten light irradiation. For concept approval, one created dendritic platinum nanostructure supported on CB was fabricated into membrane electrode assemblies (MEAs) for durability examination via potential cycling. It appears that carbon supporting is essentially beneficial to an enhanced durability according to our preliminary results.

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Factor analysis has proven an effective approach for distilling high dimensional spectral-image data into a limited number of components that describe the spatial and spectral characteristics of the imaged sample. Principal Component Analysis (PCA) is the most commonly used factor analysis tool; however, PCA constrains both the spectral and abundance factors to be orthogonal, and forces the components to serially maximize the variance that each accounts for. Neither constraint has any basis in physical reality; thus, principal components are abstract and not easily interpreted. The mathematical properties of PCA scores and loadings also differ subtly, which has implications for how they can be used in abstract factor 'rotation' procedures such as Varimax. The Singular Value Decomposition (SVD) is a mathematical technique that is frequently used to compute PCA. In this talk, we will argue that SVD itself provides a more flexible framework for spectral image analysis since spatial-domain and spectral-domain singular vectors are treated in a symmetrical fashion. We will also show that applying an abstract rotation in our choice of either the spatial or spectral domain relaxes the orthogonality requirement in the complementary domain. For instance, samples are often approximately orthogonal in a spatial sense, that is, they consist of relatively discrete chemical phases. In such cases, rotating the singular vectors in a way designed to maximize the simplicity of the spatial representation yields physically acceptable and readily interpretable estimates of the pure-component spectra. This talk will demonstrate that this approach can achieve excellent results for difficult-to-analyze data sets obtained by a variety of spectroscopic imaging techniques.

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The rate coefficient for the self-reaction of vinyl radicals has been measured by two independent methods. The rate constant as a function of temperature at 20 Torr has been determined by a laser-photolysis/laser absorption technique. Vinyl iodide is photolyzed at 266 nm, and both the vinyl radical and the iodine atom photolysis products are monitored by laser absorption. The vinyl radical concentration is derived from the initial iodine atom concentration, which is determined by using the known absorption cross section of the iodine atomic transition to relate the observed absorption to concentration. The measured rate constant for the self-reaction at room temperature is approximately a factor of 2 lower than literature recommendations. The reaction displays a slightly negative temperature dependence, which can be represented by a negative activation energy, (E{sub a}/R) = -400 K. The laser absorption results are supported by independent experiments at 298 K and 4 Torr using time-resolved synchrotron-photoionization mass-spectrometric detection of the products of divinyl ketone and methyl vinyl ketone photolysis. The photoionization mass spectrometry experiments additionally show that methyl + propargyl are formed in the vinyl radical self-reaction, with an estimated branching fraction of 0.5 at 298 K and 4 Torr.

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We describe an assembly of detectors that quantifies the background radiation present at potential above ground antineutrino detector development and deployment sites. Antineutrino detectors show great promise for safeguard applications in directly detecting the total fission rate as well as the change in fissile content of nuclear power reactors. One major technical challenge that this safeguard application must overcome is the ability to distinguish signals from antineutrinos originating in the reactor core from noise due to background radiation created by terrestrial and cosmogenic sources. To date, existing detectors increase their ability to distinguish antineutrino signals by being surrounded with significant shielding and being placed underground. For the safeguard's agency, this is less than optimal, increasing the overall size and limiting the placement of this system. For antineutrino monitoring to be a widely deployable solution, we must understand the backgrounds found above ground at nuclear power plants that can mimic the antineutrino signal so that these backgrounds can be easily identified, separated, and subtracted rather than shielded. The design, construction, calibration, and results from the deployment of these background detectors at a variety of sites will be presented.

Tensor decompositions (e.g., higher-order analogues of matrix decompositions) are powerful tools for data analysis. In particular, the CANDECOMP/PARAFAC (CP) model has proved useful in many applications such chemometrics, signal processing, and web analysis; see for details. The problem of computing the CP decomposition is typically solved using an alternating least squares (ALS) approach. We discuss the use of optimization-based algorithms for CP, including how to efficiently compute the derivatives necessary for the optimization methods. Numerical studies highlight the positive features of our CPOPT algorithms, as compared with ALS and Gauss-Newton approaches.

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