Publications

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Electrochemical capacitors based on redox-active metal oxides show great promise for many energy-storage applications. These materials store charge through both electric double-layer charging and faradaic reactions in the oxide. The dimensions of the oxide nanomaterials have a strong influence on the performance of such capacitors. Not just due to surface area effects, which influence the double-layer capacitance, but also through bulk electrical and ionic conductivities. Ni(OH)2 is a prime candidate for such applications, due to low cost and high theoretical capacity. We have examined the relationship between diameter and capacity for Ni/Ni(OH)2 nanorods. Specific capacitances of up to 511 F/g of Ni were recorded in 47 nm diameter Ni(OH)2 nanorods.

Nanoporous carbon (NPC) is a purely graphitic material with highly controlled densities ranging from less than 0.1 to 2.0 g/cm3, grown via pulsed-laser deposition. Decreasing the density of NPC increases the interplanar spacing between graphene-sheet fragments. This ability to tune the interplanar spacing makes NPC an ideal model system to study the behavior of carbon electrodes in electrochemical capacitors and batteries. We examine the capacitance of NPC films in alkaline and acidic electrolytes, and measure specific capacitances as high as 242 F/g.

An uncertainty quantification (UQ) analysis is performed on the fuel regression rate model within SIERRA/Fuego by comparing to a series of hydrocarbon tests performed in the Thermal Test Complex. The fuels used for comparison for the fuel regression rate model include methanol, ethanol, JP8, and heptane. The recently implemented flamelet combustion model is also assessed with a limited comparison to data involving measurements of temperature and relative mole fractions within a 2-m diameter methanol pool fire. The comparison of the current fuel regression rate model to data without UQ indicates that the model over predicts the fuel regression rate by 65% for methanol, 63% for ethanol, 95% for JP8, and 15% for heptane. If a UQ analysis is performed incorporating a range of values for transmittance, reflectance, and heat flux at the surface the current model predicts fuel regression rates within 50% of measured values. An alternative model which uses specific heats at inlet and boiling temperatures respectively and does not approximate the sensible heat is also compared to data. The alternative model with UQ significantly improves the comparison to within 25% for all fuels except heptane. Even though the proposed alternative model provides better agreement to data, particularly for JP8 and ethanol (within 15%), there are still outstanding issues regarding significant uncertainties which include heat flux gauge measurement and placement, boiling at the fuel surface, large scale convective motion within the liquid, and semi-transparent behavior.

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In this work we report on the development of the Signature Molecular Descriptor (or Signature) for use in the solution of inverse design problems as well as in highthroughput screening applications. The ultimate goal of using Signature is to identify novel and non-intuitive chemical structures with optimal predicted properties for a given application. We demonstrate this in three studies: green solvent design, glucocorticoid receptor ligand design and the design of inhibitors for Factor XIa. In many areas of engineering, compounds are designed and/or modified in incremental ways which rely upon heuristics or institutional knowledge. Often multiple experiments are performed and the optimal compound is identified in this brute-force fashion. Perhaps a traditional chemical scaffold is identified and movement of a substituent group around a ring constitutes the whole of the design process. Also notably, a chemical being evaluated in one area might demonstrate properties very attractive in another area and serendipity was the mechanism for solution. In contrast to such approaches, computer-aided molecular design (CAMD) looks to encompass both experimental and heuristic-based knowledge into a strategy that will design a molecule on a computer to meet a given target. Depending on the algorithm employed, the molecule which is designed might be quite novel (re: no CAS registration number) and/or non-intuitive relative to what is known about the problem at hand. While CAMD is a fairly recent strategy (dating to the early 1980s), it contains a variety of bottlenecks and limitations which have prevented the technique from garnering more attention in the academic, governmental and industrial institutions. A main reason for this is how the molecules are described in the computer. This step can control how models are developed for the properties of interest on a given problem as well as how to go from an output of the algorithm to an actual chemical structure. This report provides details on a technique to describe molecules on a computer, called Signature, as well as the computer-aided molecule design algorithm built around Signature. Two applications are provided of the CAMD algorithm with Signature. The first describes the design of green solvents based on data in the GlaxoSmithKline (GSK) Solvent Selection Guide. The second provides novel non-steroidal glucocorticoid receptor ligands with some optimally predicted properties. In addition to using the CAMD algorithm with Signature, it is demonstrated how to employ Signature in a high-throughput screening study. Here, after classifying both active and inactive inhibitors for the protein Factor XIa using Signature, the model developed is used to screen a large, publicly-available database called PubChem for the most active compounds.

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In this paper we discuss a new methodology, social language network analysis (SLNA), that combines tools from social language processing and network analysis to assess socially situated working relationships within a group. Specifically, SLNA aims to identify and characterize the nature of working relationships by processing artifacts generated with computer-mediated communication systems, such as instant message texts or emails. Because social language processing is able to identify psychological, social, and emotional processes that individuals are not able to fully mask, social language network analysis can clarify and highlight complex interdependencies between group members, even when these relationships are latent or unrecognized.

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A set of Direct Simulation Monte Carlo (DSMC) chemical-reaction models recently proposed by Bird and based solely on the collision energy and the vibrational energy levels of the species involved is applied to calculate nonequilibrium chemical-reaction rates for atmospheric reactions in hypersonic flows. The DSMC non-equilibrium model predictions are in good agreement with theoretical models and experimental measurements. The observed agreement provides strong evidence that modeling chemical reactions using only the collision energy and the vibrational energy levels provides an accurate method for predicting non-equilibrium chemical-reaction rates.

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The summary of this presentation is: (1) Barrier walls are used to reduce setbacks by factor of 2; (2) We found no ignition-timing vs. over-pressure sensitivities for jet flow obstructed by barrier walls; (3) Cryogenic vapor cloud model indicates hazard length scales exceed the room-temperature release; validation experiments are required to confirm; (4) Light-up maps developed for lean limit ignition; flammability factor model provides good indication of ignition probability; and (5) Auto-ignition is enhanced by blunt-body obstructions - increases gas temperature and promotes fuel/air mixing.

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