Publications

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Hybrid cells based on ZnO/P3HT heterojunctions have the advantage of better device stability, but suffer poor photovoltaic performance compared to all-organic cells which use PCBM as the electron acceptor. The photovoltaic effect in these hybrid systems is accomplished via photoinduced charge separation at the interface between the absorbing polymer (P3HT) and the electron acceptor (ZnO). Efforts to improve device performance in these hybrid systems have centered on reducing the required diffusion length for P3HT excitons by creating bulk heterojunctions from either ZnO nanoparticles and P3HT or using ZnO precursors which convert in situ to form ZnO networks inside a polymer matrix. In this study, we use transient photoinduced absorption to access the lifetimes of P3HT polarons and excitons in bulk heterojunctions constructed using P3HT and ZnO nanoparticles or ZnO precursors and compare to those in planar ZnO/P3HT devices. Steady-state photoinduced absorption spectra of ZnO/P3HT show characteristic of sub-bandgap transitions associated with the formation of long-lived (msec lifetimes) radical cations (polarons) in P3HT. Similar short-lived polarons (psec lifetimes) are observed by picosecond transient photoinduced absorption in addition to infrared absorption due to excitons. Here we examine the lifetimes of both the excitons and polarons in ZnO:P3HT bulk heterojunctions using both picosecond and millisecond techniques in an effort to understand the effect of the structure and morphology of the electron acceptor on charge separation. We will also compare the relative photoexitation lifetimes, hence charge separation efficiency, for the planar and bulk heterojunction hybrid system to an all-organic P3HT:PCBM system.

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Plasmonic integrated optics is an attempt to bridge the length scale gap between optics and nanometer scale electronic devices. Here we present a hybrid optical interconnect scheme which utilizes low loss dielectric waveguides for global interconnection and plasmonic structures for tightly confining light for local routing and mode manipulation.

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This model predicts thermal boundary conductance at interfaces where one material comprising the junction is characterized by high elastic anisotropy (i.e, graphite). The thermal properties of graphite are determined through a simplified vibrational model, where the bulk structure is treated as an linear assembly of two-dimensional systems. This model is validated at temperatures above cryogenic through comparison to experimentally determined values of specific heat. Elastic processes are accounted for through traditional diffuse transport theory. Inelastic contributions due to multi-phonon processes are also addressed and quantified.

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Sandia National Laboratories has developed a vehicle-scale prototype hydrogen storage system as part of a Work For Others project funded by General Motors. This Demonstration System was developed using the complex metal hydride sodium alanate. For the current work, we have continued our evaluation of the GM Demonstration System to provide learning to DOE's hydrogen storage programs, specifically the new Hydrogen Storage Engineering Center of Excellence. Baseline refueling data during testing for GM was taken over a narrow range of optimized parameter values. Further testing was conducted over a broader range. Parameters considered included hydrogen pressure and coolant flow rate. This data confirmed the choice of design pressure of the Demonstration System, but indicated that the system was over-designed for cooling. Baseline hydrogen delivery data was insufficient to map out delivery rate as a function of temperature and capacity for the full-scale system. A more rigorous matrix of tests was performed to better define delivery capabilities. These studies were compared with 1-D and 2-D coupled multi-physics modeling results. The relative merits of these models are discussed along with opportunities for improved efficiency or reduced mass and volume.

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Erbium metal thin-films have been deposited on molybdenum-on-silicon substrates and then converted to erbium dideuteride (ErD{sub 2}). Here, we study the effects of deposition temperature ({approx}300 or 723 K) and deposition rate (1 or 20 nm/s) upon the initial Er metal microstructure and subsequent ErD{sub 2} microstructure. We find that low deposition temperature and low deposition rate lead to small Er metal grain sizes, and high deposition temperature and deposition rate led to larger Er metal grain sizes, consistent with published models of metal thin-film growth. ErD{sub 2} grain sizes are strongly influenced by the prior-metal grain size, with small metal grains leading to large ErD{sub 2} grains. A novel sample preparation technique for electron backscatter diffraction of air-sensitive ErD{sub 2} was developed, and allowed the quantitative measurement of ErD{sub 2} grain size and crystallographic texture. Finer-grained ErD{sub 2} showed a strong (1 1 1) fiber texture, whereas larger grained ErD{sub 2} had only weak texture. We hypothesize that this inverse correlation may arise from improved hydrogen diffusion kinetics in the more defective fine-grained metal structure or due to improved nucleation in the textured large-grain Er.

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The ionospheric disturbance dynamo signature in geomagnetic variations is investigated using the National Center for Atmospheric Research Thermosphere-Ionosphere-Electrodynamics General Circulation Model. The model results are tested against reference magnetically quiet time observations on 21 June 1993, and disturbance effects were observed on 11 June 1993. The model qualitatively reproduces the observed diurnal and latitude variations of the geomagnetic horizontal intensity and declination for the reference quiet day in midlatitude and low-latitude regions but underestimates their amplitudes. The patterns of the disturbance dynamo signature and its source 'anti-Sq' current system are well reproduced in the Northern Hemisphere. However, the model significantly underestimates the amplitude of disturbance dynamo effects when compared with observations. Furthermore, the largest simulated disturbances occur at different local times than the observations. The discrepancies suggest that the assumed high-latitude storm time energy inputs in the model were not quantitatively accurate for this storm.

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We will describe how novel nanoporous framework materials (NFM) such as metal-organic frameworks (MOFs) can be interfaced with common mechanical sensors, such as surface acoustic wave (SAW), microcantilever array, and quartz crystal microbalance (QCM) devices, and subsequently be used to provide selectivity and sensitivity to a broad range of analytes including explosives, nerve agents, and volatile organic compounds (VOCs). NFM are highly ordered, crystalline materials with considerable synthetic flexibility resulting from the presence of both organic and inorganic components within their structure. Chemical detection using micro-electro-mechanical-systems (MEMS) devices (i.e. SAWs, microcantilevers) requires the use of recognition layers to impart selectivity. Unlike traditional organic polymers, which are dense, the nanoporosity and ultrahigh surface areas of NFM allow for greater analyte uptake and enhance transport into and out of the sensing layer. This enhancement over traditional coatings leads to improved response times and greater sensitivity, while their ordered structure allows chemical tuning to impart selectivity. We describe here experiments and modeling aimed at creating NFM layers tailored to the detection of water vapor, explosives, CWMD, and volatile organic compound (VOCs), and their integration with the surfaces of MEMS devices. Molecular simulation shows that a high degree of chemical selectivity is feasible. For example, a suite of MOFs can select for strongly interacting organics (explosives, CWMD) vs. lighter volatile organics at trace concentrations. At higher gas pressures, the CWMD are deselected in favor of the volatile organics. We will also demonstrate the integration of various NFM on the surface of microcantiliver arrays, QCM crystals, and SAW devices, and describe new synthetic methods developed to improve the quality of NFM coatings. Finally, MOF-coated MEMS devices show how temperature changes can be tuned to improve response times, selectivity, and sensitivity.

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Fire probabilistic risk assessment (PRA) methods utilize data and insights gained from actual fire events in a variety of ways. For example, fire occurrence frequencies, manual fire fighting effectiveness and timing, and the distribution of fire events by fire source and plant location are all based directly on the historical experience base. Other factors are either derived indirectly or supported qualitatively based on insights from the event data. These factors include the general nature and intensity of plant fires, insights into operator performance, and insights into fire growth and damage behaviors. This paper will discuss the potential methodology improvements that could be realized if more complete fire event reporting information were available. Areas that could benefit from more complete event reporting that will be discussed in the paper include fire event frequency analysis, analysis of fire detection and suppression system performance including incipient detection systems, analysis of manual fire fighting performance, treatment of fire growth from incipient stages to fully-involved fires, operator response to fire events, the impact of smoke on plant operations and equipment, and the impact of fire-induced cable failures on plant electrical circuits.

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This abstract explores the potential advantages of discontinuous Galerkin (DG) methods for the time-domain inversion of media parameters within the earth's interior. In particular, DG methods enable local polynomial refinement to better capture localized geological features within an area of interest while also allowing the use of unstructured meshes that can accurately capture discontinuous material interfaces. This abstract describes our initial findings when using DG methods combined with Runge-Kutta time integration and adjoint-based optimization algorithms for full-waveform inversion. Our initial results suggest that DG methods allow great flexibility in matching the media characteristics (faults, ocean bottom and salt structures) while also providing higher fidelity representations in target regions. Time-domain inversion using discontinuous Galerkin on unstructured meshes and with local polynomial refinement is shown to better capture localized geological features and accurately capture discontinuous-material interfaces. These approaches provide the ability to surgically refine representations in order to improve predicted models for specific geological features. Our future work will entail automated extensions to directly incorporate local refinement and adaptive unstructured meshes within the inversion process.

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Reprocessing nuclear fuel releases gaseous radio-iodine containing compounds which must be captured and stored for prolonged periods. Ag-loaded mordenites are the leading candidate for scavenging both organic and inorganic radioiodine containing compounds directly from reprocessing off gases. Alternately, the principal off-gas contaminant, I2, and I-containing acids HI, HIO3, etc. may be scavenged using caustic soda solutions, which are then treated with bismuth to put the iodine into an insoluble form. Our program is focused on using state-of-the-art materials science technologies to develop materials with high loadings of iodine, plus high long-term mechanical and thermal stability. In particular, we present results from research into two materials areas: (1) zeolite-based separations and glass encapsulation, and (2) in-situ precipitation of Bi-I-O waste forms. Ag-loaded mordenite is either commercially available or can be prepared via a simple Ag+ ion exchange process. Research using an Ag+-loaded Mordenite zeolite (MOR, LZM-5 supplied by UOP Corp.) has revealed that I2 is scavenged in one of three forms, as micron-sized AgI particles, as molecular (AgI)x clusters in the zeolite pores and as elemental I2 vapor. It was found that only a portion of the sorbed iodine is retained after heating at 95o C for three months. Furthermore, we show that even when the Ag-MOR is saturated with I2 vapor only roughly half of the silver reacted to form stable AgI compounds. However, the Iodine can be further retained if the AgI-MOR is then encapsulated into a low temperature glass binder. Follow-on studies are now focused on the sorption and waste form development of Iodine from more complex streams including organo-iodine compounds (CH3I). Bismuth-Iodate layered phases have been prepared from caustic waste stream simulant solutions. They serve as a low cost alternative to ceramics waste forms. Novel compounds have been synthesized and solubility studies have been completed using competing groundwater anions (HCO3-, Cl- and SO42-). Distinct variations in solubility were found that related to the structures of the materials.

Grand canonical Monte Carlo simulations were performed to investigate trends in low-pressure adsorption of a broad range of organic molecules by a set of metal-organic frameworks (MOFs). The organic analytes considered here are relevant to applications in chemical detection: small aromatics (o-, m-, and p-xylene), polycyclic aromatic hydrocarbons (naphthalene, anthracene, phenanthrene), explosives (TNT and RDX), and chemical warfare agents (GA and VM). The framework materials included several Zn-MOFs (IRMOFs 1-3, 7, 8), a Cr-MOF (CrMIL-53lp), and a Cu-MOF (HKUST-1). Many of the larger organics were significantly adsorbed by the target MOFs at low pressure, which is consistent with the exceptionally high isosteric heats of adsorption (25 kcal/mol - 60 kcal/mol) for this range of analyte. At a higher loading pressure of 101 kPa, the Zn-MOFs show a much higher volumetric uptake than either CrMIL-53-lp or HKUST-1 for all types of analyte. Within the Zn-MOF series, analyte loading is proportional to free volume, and loading decreases with increasing analyte size due to molecular packing effects. CrMIL-53lp showed the highest adsorption energy for all analytes, suggesting that this material may be suitable for low-level detection of organics.

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This paper discusses the recent development a statistical approach for the automatic identification of anomalous network activity that is characteristic of exfiltration events. This approach is based on the language processing method eferred to as latent dirichlet allocation (LDA). Cyber security experts currently depend heavily on a rule-based framework for initial detection of suspect network events. The application of the rule set typically results in an extensive list of uspect network events that are then further explored manually for suspicious activity. The ability to identify anomalous network events is heavily dependent on the experience of the security personnel wading through the network log. Limitations f this approach are clear: rule-based systems only apply to exfiltration behavior that has previously been observed, and experienced cyber security personnel are rare commodities. Since the new methodology is not a discrete rule-based pproach, it is more difficult for an insider to disguise the exfiltration events. A further benefit is that the methodology provides a risk-based approach that can be implemented in a continuous, dynamic or evolutionary fashion. This permits uspect network activity to be identified early with a quantifiable risk associated with decision making when responding to suspicious activity.

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Electron-interface scattering during electron-phonon nonequilibrium in thin films creates another pathway for electron system energy loss as characteristic lengths of thin films continue to decrease. As power densities in nanodevices increase, excitations of electrons from sub-conduction-band energy levels will become more probable. These sub-conduction-band electronic excitations significantly affect the material's thermophysical properties. In this work, the effects of d-band electronic excitations are considered in electron energy transfer processes in thin metal films. In thin films with thicknesses less than the electron mean free path, ballistic electron transport leads to electron-interface scattering. The ballistic component of electron transport, leading to electron-interface scattering, is studied by a ballistic-diffusive approximation of the Boltzmann Transport Equation. The effect of d-band excitations on electron-interface energy transfer is analyzed during electron-phonon nonequilibrium after short pulsed laser heating in thin films.

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Policy makers will most likely need to make decisions about climate policy before climate scientists have resolved all relevant uncertainties about the impacts of climate change. This study demonstrates a risk-assessment methodology for evaluating uncertain future climatic conditions. We estimate the impacts of climate change on U.S. state- and national-level economic activity from 2010 to 2050. To understand the implications of uncertainty on risk and to provide a near-term rationale for policy interventions to mitigate the course of climate change, we focus on precipitation, one of the most uncertain aspects of future climate change. We use results of the climate-model ensemble from the Intergovernmental Panel on Climate Change's (IPCC) Fourth Assessment Report 4 (AR4) as a proxy for representing climate uncertainty over the next 40 years, map the simulated weather from the climate models hydrologically to the county level to determine the physical consequences on economic activity at the state level, and perform a detailed 70-industry analysis of economic impacts among the interacting lower-48 states. We determine the industry-level contribution to the gross domestic product and employment impacts at the state level, as well as interstate population migration, effects on personal income, and consequences for the U.S. trade balance. We show that the mean or average risk of damage to the U.S. economy from climate change, at the national level, is on the order of $1 trillion over the next 40 years, with losses in employment equivalent to nearly 7 million full-time jobs.

Importance sampling is an unbiased sampling method used to sample random variables from different densities than originally defined. These importance sampling densities are constructed to pick 'important' values of input random variables to improve the estimation of a statistical response of interest, such as a mean or probability of failure. Conceptually, importance sampling is very attractive: for example one wants to generate more samples in a failure region when estimating failure probabilities. In practice, however, importance sampling can be challenging to implement efficiently, especially in a general framework that will allow solutions for many classes of problems. We are interested in the promises and limitations of importance sampling as applied to computationally expensive finite element simulations which are treated as 'black-box' codes. In this paper, we present a customized importance sampler that is meant to be used after an initial set of Latin Hypercube samples has been taken, to help refine a failure probability estimate. The importance sampling densities are constructed based on kernel density estimators. We examine importance sampling with respect to two main questions: is importance sampling efficient and accurate for situations where we can only afford small numbers of samples? And does importance sampling require the use of surrogate methods to generate a sufficient number of samples so that the importance sampling process does increase the accuracy of the failure probability estimate? We present various case studies to address these questions.

P- and S-body wave travel times collected from stations in and near the state of Nevada were inverted for P-wave velocity and the Vp/Vs ratio. These waves consist of Pn, Pg, Sn and Sg, but only the first arriving P and S waves were used in the inversion. Travel times were picked by University of Nevada Reno colleagues and were culled for inclusion in the tomographic inversion. The resulting tomographic model covers the entire state of Nevada to a depth of {approx}90 km; however, only the upper 40 km indicate relatively good resolution. Several features of interest are imaged including the Sierra Nevada, basin structures, and low velocities at depth below Yucca Mountain. These velocity structure images provide valuable information to aide in the interpretation of geothermal resource areas throughout the state on Nevada.

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The compressive mechanical response of fine sand is experimentally investigated. The strain rate, initial density, stress state, and moisture level are systematically varied. A Kolsky bar was modified to obtain uniaxial and triaxial compressive response at high strain rates. A controlled loading pulse allows the specimen to acquire stress equilibrium and constant strain-rates. The results show that the compressive response of the fine sand is not sensitive to strain rate under the loading conditions in this study, but significantly dependent on the moisture content, initial density and lateral confinement. Partially saturated sand is more compliant than dry sand. Similar trends were reported in the quasi-static regime for experiments conducted at comparable specimen conditions. The sand becomes stiffer as initial density and/or confinement pressure increases. The sand particle size become smaller after hydrostatic pressure and further smaller after dynamic axial loading.

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Innovative energy system optimization models are deployed to evaluate novel fuel cell system (FCS) operating strategies, not typically pursued by commercial industry. Most FCS today are installed according to a 'business-as-usual' approach: (1) stand-alone (unconnected to district heating networks and low-voltage electricity distribution lines), (2) not load following (not producing output equivalent to the instantaneous electrical or thermal demand of surrounding buildings), (3) employing a fairly fixed heat-to-power ratio (producing heat and electricity in a relatively constant ratio to each other), and (4) producing only electricity and no recoverable heat. By contrast, models discussed here consider novel approaches as well. Novel approaches include (1) networking (connecting FCSs to electrical and/or thermal networks), (2) load following (having FCSs produce only the instantaneous electricity or heat demanded by surrounding buildings), (3) employing a variable heat-to-power ratio (such that FCS can vary the ratio of heat and electricity they produce), (4) co-generation (combining the production of electricity and recoverable heat), (5) permutations of these together, and (6) permutations of these combined with more 'business-as-usual' approaches.

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Progress in algal biofuels has been limited by significant knowledge gaps in algal biology, particularly as they relate to scale-up. To address this we are investigating how culture composition dynamics (light as well as biotic and abiotic stressors) describe key biochemical indicators of algal health: growth rate, photosynthetic electron transport, and lipid production. Our approach combines traditional algal physiology with genomics, bioanalytical spectroscopy, chemical imaging, remote sensing, and computational modeling to provide an improved fundamental understanding of algal cell biology across multiple cultures scales. This work spans investigations from the single-cell level to ensemble measurements of algal cell cultures at the laboratory benchtop to large greenhouse scale (175 gal). We will discuss the advantages of this novel, multidisciplinary strategy and emphasize the importance of developing an integrated toolkit to provide sensitive, selective methods for detecting early fluctuations in algal health, productivity, and population diversity. Progress in several areas will be summarized including identification of spectroscopic signatures for algal culture composition, stress level, and lipid production enabled by non-invasive spectroscopic monitoring of the photosynthetic and photoprotective pigments at the single-cell and bulk-culture scales. Early experiments compare and contrast the well-studied green algae chlamydomonas with two potential production strains of microalgae, nannochloropsis and dunnaliella, under optimal and stressed conditions. This integrated approach has the potential for broad impact on algal biofuels and bioenergy and several of these opportunities will be discussed.

The quantification of the production of primary defects via displacement cascades is an important ingredient in the prediction of the influence of radiation on the performance of electronic components in radiation environments. Molecular dynamics simulations of displacement cascades are performed for GaAs The interatomic interactions are described using a recently proposed Bond Order Potential, and a simple model of electronic stopping is incorporated. The production of point defects is quantified as a function of recoil energy and recoil species. Correlations in the point defects are examined. There are a large number of anti-site defects nearest-neighbor pairs as well as di-vacancies and larger order vacancy clusters. Radiation damage and ion implantation in materials have been studied via molecular dynamics for many years. A significant challenge in these simulations is the detailed identification and quantification of the primary defect production. For the present case of a compound semiconductor, GaAs, there are a larger number of possible point defects compared to elemental materials; two types of vacancies, two types of interstitials and antisite defects. This is further complicated by the fact that, in addition to the formation of point defects, amorphous zones may also be created. The goal of the current work is to quantify the production of primary defects in GaAs due to radiation exposures. This information will be used as part of an effort to predict the influence of radiation environments on the performance of electronic components and circuits. The data provide the initial state for continuum-level analysis of the temporal evolution of defect populations. For this initial state, it is important to know both the number of the various point defects that may be produced as well as the initial spatial correlations between the primary defects. The molecular dynamics simulations employ a recently developed Bond Order Potential (BOP) for GaAs. The analysis of the resulting atomic configurations follows a generalization of methods presented previously for elemental Si. The number of point defects of various types, exclusive of the amorphous zones, is predicted as a function of recoil energy. It is also shown that certain primary point defects are initially formed in binary or larger clusters.