III-Nitride Nanowires: Novel Emitters for Lighting (invited)
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Journal of Applied Physics
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Fracture or tearing of ductile metals is a pervasive engineering concern, yet accurate prediction of the critical conditions of fracture remains elusive. Sandia National Laboratories has been developing and implementing several new modeling methodologies to address problems in fracture, including both new physical models and new numerical schemes. The present study provides a double-blind quantitative assessment of several computational capabilities including tearing parameters embedded in a conventional finite element code, localization elements, extended finite elements (XFEM), and peridynamics. For this assessment, each of four teams reported blind predictions for three challenge problems spanning crack initiation and crack propagation. After predictions had been reported, the predictions were compared to experimentally observed behavior. The metal alloys for these three problems were aluminum alloy 2024-T3 and precipitation hardened stainless steel PH13-8Mo H950. The predictive accuracies of the various methods are demonstrated, and the potential sources of error are discussed.
Journal of Physical Chemistry A
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Passive detection of special nuclear material (SNM) at long range or under heavy shielding can only be achieved by observing the penetrating neutral particles that it emits: gamma rays and neutrons in the MeV energy range. The ultimate SNM standoff detector system would have sensitivity to both gamma and neutron radiation, a large area and high efficiency to capture as many signal particles as possible, and good discrimination against background particles via directional and energy information. Designing such a system is a daunting task. Using timemodulated collimators could be a transformative technique leading to practical gamma-neutron imaging detector systems that are highly efficient with the potential to exhibit simultaneously high angular and energy resolution. A new technique using time encoding to make a compact, high efficiency imaging detector was conceived. Design considerations using Monte Carlo modeling and the construction and demonstration of a prototype imager are described.
Next-generation exascale systems, those capable of performing a quintillion (10{sup 18}) operations per second, are expected to be delivered in the next 8-10 years. These systems, which will be 1,000 times faster than current systems, will be of unprecedented scale. As these systems continue to grow in size, faults will become increasingly common, even over the course of small calculations. Therefore, issues such as fault tolerance and reliability will limit application scalability. Current techniques to ensure progress across faults like checkpoint/restart, the dominant fault tolerance mechanism for the last 25 years, are increasingly problematic at the scales of future systems due to their excessive overheads. In this work, we evaluate a number of techniques to decrease the overhead of checkpoint/restart and keep this method viable for future exascale systems. More specifically, this work evaluates state-machine replication to dramatically increase the checkpoint interval (the time between successive checkpoint) and hash-based, probabilistic incremental checkpointing using graphics processing units to decrease the checkpoint commit time (the time to save one checkpoint). Using a combination of empirical analysis, modeling, and simulation, we study the costs and benefits of these approaches on a wide range of parameters. These results, which cover of number of high-performance computing capability workloads, different failure distributions, hardware mean time to failures, and I/O bandwidths, show the potential benefits of these techniques for meeting the reliability demands of future exascale platforms.
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Discrimination of benign sources from threat sources at Port of Entries (POE) is of a great importance in efficient screening of cargo and vehicles using Radiation Portal Monitors (RPM). Currently RPM's ability to distinguish these radiological sources is seriously hampered by the energy resolution of the deployed RPMs. As naturally occurring radioactive materials (NORM) are ubiquitous in commerce, false alarms are problematic as they require additional resources in secondary inspection in addition to impacts on commerce. To increase the sensitivity of such detection systems without increasing false alarm rates, alarm metrics need to incorporate the ability to distinguish benign and threat sources. Principal component analysis (PCA) and clustering technique were implemented in the present study. Such techniques were investigated for their potential to lower false alarm rates and/or increase sensitivity to weaker threat sources without loss of specificity. Results of the investigation demonstrated improved sensitivity and specificity in discriminating benign sources from threat sources.
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International Journal for Uncertainty Quantification
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There is national interest in the development of sophisticated materials that can automatically detect and respond to chemical and biological threats without the need for human intervention. In living systems, cell membranes perform such functions on a routine basis, detecting threats, communicating with the cell, and triggering automatic responses such as the opening and closing of ion channels. The purpose of this project was to learn how to replicate simple threat detection and response functions within artificial membrane systems. The original goals toward developing 'smart skin' assemblies included: (1) synthesizing functionalized nanoparticles to produce electrochemically responsive systems within a lipid bilayer host matrices, (2) calculating the energetics of nanoparticle-lipid interactions and pore formation, and (3) determining the mechanism of insertion of nanoparticles in lipid bilayers via imaging and electrochemistry. There are a few reports of the use of programmable materials to open and close pores in rigid hosts such as mesoporous materials using either heat or light activation. However, none of these materials can regulate themselves in response to the detection of threats. The strategies we investigated in this project involve learning how to use programmable nanomaterials to automatically eliminate open channels within a lipid bilayer host when 'threats' are detected. We generated and characterized functionalized nanoparticles that can be used to create synthetic pores through the membrane and investigated methods of eliminating the pores either through electrochemistry, change in pH, etc. We also focused on characterizing the behavior of functionalized gold NPs in different lipid membranes and lipid vesicles and coupled these results to modeling efforts designed to gain an understanding of the interaction of nanoparticles within lipid assemblies.
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Proteomics
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This is the final report for the President Harry S. Truman Fellowship in National Security Science and Engineering (LDRD project 130813) awarded to Dr. Bryan Kaehr from 2008-2011. Biological chemistries, cells, and integrated systems (e.g., organisms, ecologies, etc.) offer important lessons for the design of synthetic strategies and materials. The desire to both understand and ultimately improve upon biological processes has been a driving force for considerable scientific efforts worldwide. However, to impart the useful properties of biological systems into modern devices and materials requires new ideas and technologies. The research herein addresses aspects of these issues through the development of (1) a rapid-prototyping methodology to build 3D bio-interfaces and catalytic architectures, (2) a quantitative method to measure cell/material mechanical interactions in situ and at the microscale, and (3) a breakthrough approach to generate functional biocomposites from bacteria and cultured cells.
IEEE Antennas and Wireless Propagation Letters
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Most topic modeling algorithms that address the evolution of documents over time use the same number of topics at all times. This obscures the common occurrence in the data where new subjects arise and old ones diminish or disappear entirely. We propose an algorithm to model the birth and death of topics within an LDA-like framework. The user selects an initial number of topics, after which new topics are created and retired without further supervision. Our approach also accommodates many of the acceleration and parallelization schemes developed in recent years for standard LDA. In recent years, topic modeling algorithms such as latent semantic analysis (LSA)[17], latent Dirichlet allocation (LDA)[10] and their descendants have offered a powerful way to explore and interrogate corpora far too large for any human to grasp without assistance. Using such algorithms we are able to search for similar documents, model and track the volume of topics over time, search for correlated topics or model them with a hierarchy. Most of these algorithms are intended for use with static corpora where the number of documents and the size of the vocabulary are known in advance. Moreover, almost all current topic modeling algorithms fix the number of topics as one of the input parameters and keep it fixed across the entire corpus. While this is appropriate for static corpora, it becomes a serious handicap when analyzing time-varying data sets where topics come and go as a matter of course. This is doubly true for online algorithms that may not have the option of revising earlier results in light of new data. To be sure, these algorithms will account for changing data one way or another, but without the ability to adapt to structural changes such as entirely new topics they may do so in counterintuitive ways.
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Oxygen gas injection has been studied as one method for rapidly generating hydrogen gas from a uranium hydride storage system. Small scale reactors, 2.9 g UH{sub 3}, were used to study the process experimentally. Complimentary numerical simulations were used to better characterize and understand the strongly coupled chemical and thermal transport processes controlling hydrogen gas liberation. The results indicate that UH{sub 3} and O{sub 2} are sufficiently reactive to enable a well designed system to release gram quantities of hydrogen in {approx} 2 seconds over a broad temperature range. The major system-design challenge appears to be heat management. In addition to the oxidation tests, H/D isotope exchange experiments were performed. The rate limiting step in the overall gas-to-particle exchange process was found to be hydrogen diffusion in the {approx}0.5 {mu}m hydride particles. The experiments generated a set of high quality experimental data; from which effective intra-particle diffusion coefficients can be inferred.
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Recent work has shown that graphene, a 2D electronic material amenable to the planar semiconductor fabrication processing, possesses tunable electronic material properties potentially far superior to metals and other standard semiconductors. Despite its phenomenal electronic properties, focused research is still required to develop techniques for depositing and synthesizing graphene over large areas, thereby enabling the reproducible mass-fabrication of graphene-based devices. To address these issues, we combined an array of growth approaches and characterization resources to investigate several innovative and synergistic approaches for the synthesis of high quality graphene films on technologically relevant substrate (SiC and metals). Our work focused on developing the fundamental scientific understanding necessary to generate large-area graphene films that exhibit highly uniform electronic properties and record carrier mobility, as well as developing techniques to transfer graphene onto other substrates.
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