Identification, discrimination, and selective adaptation of simultaneous musical intervals

Four experiments investigated perception of major and minor thirds whose component tones were sounded simultaneously. Effects akin to categorical perception of speech sounds were found. In the first experiment, musicians demonstrated relatively sharp category boundaries in identification and peaks near the boundary in discrimination tasks of an interval continuum where the bottom note was always an F and the top note varied from A to A flat in seven equal logarithmic steps. Nonmusicians showed these effects only to a small extent. The musicians showed higher than predicted discrimination performance overall, and reaction time increases at category boundaries. In the second experiment, musicians failed to consistently identify or discriminate thirds which varied in absolute pitch, but retained the proper interval ratio. In the last two experiments, using selective adaptation, consistent shifts were found in both identification and discrimination, similar to those found in speech experiments. Manipulations of adapting and test showed that the mechanism underlying the effect appears to be centrally mediated and confined to a frequency-specific level. A multistage model of interval perception, where the first stages deal only with specific pitches may account for the results.

Selective Formation of Diblock Copolymers Using Radical Trap-Assisted Atom Transfer Radical Coupling

Polystyrene (PSt) radicals and poly(methyl acrylate) (PMA) radicals, derived from their monobrominated precursors prepared by atom transfer radical polymerization (ATRP), were formed in the presence of the radical trap 2-methyl-2-nitrosopropane (MNP), selectively forming PSt-PMA diblock copolymers with an alkoxyamine at the junction between the block segments. This radical trap-assisted, atom transfer radical coupling (RTA-ATRC) was performed in a single pot at low temperature (35 °C), while analogous traditional ATRC reactions at this temperature, which lacked the radical trap, resulted in no observed coupling and the PStBr and PMABr precursors were simply recovered. Selective formation of the diblock under RTA-ATRC conditions is consistent with the PStBr and PMABr having substantially different KATRP values, with PSt radicals initially being formed and trapped by the MNP and the PMA radicals being trapped by the in situ-formed nitroxide end-capped PSt. The midchain alkoxyamine functionality was confirmed by thermolysis of the diblock copolymer, resulting in recovery of the PSt segment and degradation of the PMA block at the relatively high temperatures (125 °C) required for thermal cleavage. A PSt-PMA diblock formed by chain extenstion ATRP using PStBr as the macroinitiator (thus lacking the alkoxyamine between the PSt-PMA segements) was inert to thermolysis. © 2013 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2013, 51, 3619–3626

A Model to Predict Percolation Threshold and Effective Conductivity of Infiltrated Electrodes for Solid Oxide Fuel Cells

We present a mechanistic modeling methodology to predict both the percolation threshold and effective conductivity of infiltrated Solid Oxide Fuel Cell (SOFC) electrodes. The model has been developed to mirror each step of the experimental fabrication process. The primary model output is the infiltrated electrode effective conductivity which provides results over a range of infiltrate loadings that are independent of the chosen electronically conducting material. The percolation threshold is utilized as a valuable output data point directly related to the effective conductivity to compare a wide range of input value choices. The predictive capability of the model is demonstrated by favorable comparison to two separate published experimental studies, one using strontium molybdate and one using La0.8Sr0.2FeO3-δ as infiltrate materials. Effective conductivities and percolation thresholds are shown for varied infiltrate particle size, pore size, and porosity with the infiltrate particle size having the largest impact on the results.

A Model to Predict Percolation Threshold and Effective Conductivity of Infiltrated Electrodes for Solid Oxide Fuel Cells

We present a mechanistic modeling methodology to predict both the percolation threshold and effective conductivity of infiltrated Solid Oxide Fuel Cell (SOFC) electrodes. The model has been developed to mirror each step of the experimental fabrication process. The primary model output is the infiltrated electrode effective conductivity which provides results over a range of infiltrate loadings that are independent of the chosen electronically conducting material. The percolation threshold is utilized as a valuable output data point directly related to the effective conductivity to compare a wide range of input value choices. The predictive capability of the model is demonstrated by favorable comparison to two separate published experimental studies, one using strontium molybdate and one using La0.8Sr0.2FeO3-delta as infiltrate materials. Effective conductivities and percolation thresholds are shown for varied infiltrate particle size, pore size, and porosity with the infiltrate particle size having the largest impact on the results. (C) 2013 The Electrochemical Society. All rights reserved.

Selective Formation of Diblock Copolymers Using Radical Trap-Assisted Atom Transfer Radical Coupling

Polystyrene (PSt) radicals and poly(methyl acrylate) (PMA) radicals, derived from their monobrominated precursors prepared by atom transfer radical polymerization (ATRP), were formed in the presence of the radical trap 2-methyl-2-nitrosopropane (MNP), selectively forming PSt-PMA diblock copolymers with an alkoxyamine at the junction between the block segments. This radical trap-assisted, atom transfer radical coupling (RTA-ATRC) was performed in a single pot at low temperature (35 degrees C), while analogous traditional ATRC reactions at this temperature, which lacked the radical trap, resulted in no observed coupling and the PStBr and PMABr precursors were simply recovered. Selective formation of the diblock under RTA-ATRC conditions is consistent with the PStBr and PMABr having substantially different K-ATRP values, with PSt radicals initially being formed and trapped by the MNP and the PMA radicals being trapped by the in situ-formed nitroxide end-capped PSt. The midchain alkoxyamine functionality was confirmed by thermolysis of the diblock copolymer, resulting in recovery of the PSt segment and degradation of the PMA block at the relatively high temperatures (125 degrees C) required for thermal cleavage. A PSt-PMA diblock formed by chain extenstion ATRP using PStBr as the macroinitiator (thus lacking the alkoxyamine between the PSt-PMA segements) was inert to thermolysis. (c) 2013 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2013, 51, 3619-3626

THE RESONATE-AND-FIRE NEURON: TIME DEPENDENT AND FREQUENCY SELECTIVE NEURONS IN NEURAL NETWORKS

The means through which the nervous system perceives its environment is one of the most fascinating questions in contemporary science. Our endeavors to comprehend the principles of neural science provide an instance of how biological processes may inspire novel methods in mathematical modeling and engineering. The application ofmathematical models towards understanding neural signals and systems represents a vibrant field of research that has spanned over half a century. During this period, multiple approaches to neuronal modeling have been adopted, and each approach is adept at elucidating a specific aspect of nervous system function. Thus while bio-physical models have strived to comprehend the dynamics of actual physical processes occurring within a nerve cell, the phenomenological approach has conceived models that relate the ionic properties of nerve cells to transitions in neural activity. Further-more, the field of neural networks has endeavored to explore how distributed parallel processing systems may become capable of storing memory. Through this project, we strive to explore how some of the insights gained from biophysical neuronal modeling may be incorporated within the field of neural net-works. We specifically study the capabilities of a simple neural model, the Resonate-and-Fire (RAF) neuron, whose derivation is inspired by biophysical neural modeling. While reflecting further biological plausibility, the RAF neuron is also analytically tractable, and thus may be implemented within neural networks. In the following thesis, we provide a brief overview of the different approaches that have been adopted towards comprehending the properties of nerve cells, along with the framework under which our specific neuron model relates to the field of neuronal modeling. Subsequently, we explore some of the time-dependent neurocomputational capabilities of the RAF neuron, and we utilize the model to classify logic gates, and solve the classic XOR problem. Finally we explore how the resonate-and-fire neuron may be implemented within neural networks, and how such a network could be adapted through the temporal backpropagation algorithm.

Development of Solid Oxide Fuel Cell Electrodes with High Conductivity and Enhanced Redox Stability

The intent of this study was the development of new ceramic SOFC anode materials which possess electrical conductivity as well as redox stability.

The Effect of Various Electrode Microstructure Parameters on the Effective Conductivity and Three-Phase Boundary Density of Infiltrated SOFC Electrodes

Solid oxide fuel cell (SOFC) technology has the potential to be a significant player in our future energy technology repertoire based on its ability to convert chemical energy into electrical energy. Infiltrated SOFCs, in particular, have demonstrated improved performance and at lower cost than traditional SOFCs. An infiltrated electrode comprises porous ceramic scaffolding (typically constructed from the oxygen ion conducting material) that is infiltrated with electron conducting and catalytic particles. Two important SOFC electrode properties are effective conductivity and three phase boundary density (TPB). Researchers study these electrode properties separately, and fail to recognize them as competing properties. This thesis aims to (1) develop a method to model the TPB density and use it to determine the effect of porosity, scaffolding particle size, and pore former size on TPB density as well as to (2) compare the effect of porosity, scaffolding particle size, and pore former size on TPB density and effective conductivity to determine a desired set of parameters for infiltrated SOFC electrode performance. A computational model was used to study the effect of microstructure parameters on the effective conductivity and TPB density of the infiltrated SOFC electrode. From this study, effective conductivity and TPB density are determined to be competing properties of SOFC electrodes. Increased porosity, scaffolding particle size, and pore former particle size increase the effective conductivity for a given infiltrate loading above percolation threshold. Increased scaffolding particle size and pore former size ratio, however, decreases the TPB density. The maximum TPB density is achievable between porosities of 45% and 60%. The effect of microstructure parameters are more prominent at low loading with scaffolding particle size being the most significant factor and pore former size ratio being the least significant factor.