Photopolymerization is the basis of several multi-million dollar industries including films and coating, inks, adhesives, fiber optics, and biomaterials. The fundamentals of the photopolymerization process, however, are not well understood. As a result, spatial variations of photopolymerization impose significant limitations on applications in which a high spatial resolution is required. To address these issues, microrheology was implemented to study the spatial and temporal effects of free-radical photopolymerization. In this work a photosensitive, acrylate resin was exposed to ultraviolet light, while the Brownian motion of micron sized, inert fluorescent tracer particles was tracked using optical videomicroscopy. Statistical analysis of particle motion yielded data that could then be used to extract rheological information about the embedding medium as a function of time and space, thereby relating UV exposure to the polymerization and gelation of monomeric resins. The effects of varying depth, initiator concentration, inhibitor concentration, composition of the monomer, and light intensity on the gelation process were studied. The most striking result is the measured difference in gelation time observed as a function of UV penetration depth. The observed trend was found to be independent of UV light intensity and monomer composition. The intensity results were used to test the accuracy of energy threshold model, which is used to empirically predict photo-induced polymerization. The results of this research affirm the ability of microrheology to provide the high spatial and temporal resolution necessary to accurately monitor the photopolymerization process. The experimental data provide a better understanding of the photo-induced polymerization, which could lead to expanded use and improved industrial process optimization. The use of microrheology to monitor photopolymerization can also aid in the development of predictive models and offer the ability to perform in-situ quality control of the process.

In this study, we have developed a dense La0.85Sr0.15MnO3-δ (LSM) Ce0.9Gd0.1O1.95 (GDC) composite electrode system for studying the surface modification of cathodes. The LSM and GDC grains in the composite were well defined and distinguished using energy dispersive x-ray (EDX) analysis. The specific three-phase boundary (TPB) length per unit electrode surface area was systematically controlled by adjusting the LSM to GDC volume ratio of the composite from 40% up to 70%. The TPB length for each tested sample was determined through stereological techniques and used to correlate the cell performance and degradation with the specific TPB length per unit surface area. An overlapping spheres percolation model was developed to estimate the activity of the TPB lines on the surface of the dense composite electrodes developed. The model suggested that the majority of the TPB lines would be active and the length of those lines maximized if the volume percent of the electrolyte material was kept in the range of 47 57%. Additionally, other insights into the processing conditions to maximize the amount of active TPB length were garnered from both the stereology calculations and the percolation simulations. Steady-state current voltage measurements as well as electrochemical impedance measurements on numerous samples under various environmental conditions were completed. The apparent activation energy for the reduction reaction was found to lie somewhere between 31 kJ/mol and 41 kJ/mol depending upon the experimental conditions. The exchange current density was found to vary with the partial pressure of oxygen differently over two separate regions. At relatively low partial pressures, i0 had an approximately dependence and at relatively high partial pressures, i0 had an approximately dependence. This led to the conclusion that a change in the rate limiting step occurs over this range. A method for deriving the electrochemical properties from proposed reaction mechanisms was also presented. State-space modeling was used as it is a robust approach to addressing these particular types of problems due to its relative ease of implementation and ability to efficiently handle large systems of differential algebraic equations. This method combined theoretical development with experimental results obtained previously to predict the electrochemical performance data. The simulations agreed well the experimental data and allowed for testing of operating conditions not easily reproducible in the lab (e.g. precise control and differentiation of low oxygen partial pressures).

Biochemical reactions make up most of the activity in a cell. There is inherent stochasticity in the kinetic behavior of biochemical reactions which in turn governs the fate of various cellular processes. In this work, the precision of a method for dimensionality reduction for stochastic modeling of biochemical reactions is evaluated. Further, a method of stochastic simulation of reaction kinetics is implemented in case of a specific biochemical network involved in maintenance of long-term potentiation (LTP), the basic substrate for learning and memory formation. The dimensionality reduction method diverges significantly from a full stochastic model in prediction the variance of the fluctuations. The application of the stochastic simulation method to LTP modeling was used to find qualitative dependence of stochastic fluctuations on reaction volume and model parameters. / text

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Thesis (Ph.D.); Submitted to Iowa State Univ., Ames, IA (US); 19 Dec 2004. / Published through the Information Bridge: DOE Scientific and Technical Information. "IS-T 2397" Seong Huh. US Department of Energy 12/19/2004. Report is also available in paper and microfiche from NTIS.

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