Interference lithography for optical devices and coatings

Interference lithography can create large-area, defect-free nanostructures with unique optical properties. In this thesis, interference lithography will be utilized to create photonic crystals for functional devices or coatings. For instance, typical lithographic processing techniques were used to create 1, 2 and 3 dimensional photonic crystals in SU8 photoresist. These structures were in-filled with birefringent liquid crystal to make active devices, and the orientation of the liquid crystal directors within the SU8 matrix was studied. Most of this thesis will be focused on utilizing polymerization induced phase separation as a single-step method for fabrication by interference lithography. For example, layered polymer/nanoparticle composites have been created through the one-step two-beam interference lithographic exposure of a dispersion of 25 and 50 nm silica particles within a photopolymerizable mixture at a wavelength of 532 nm. In the areas of constructive interference, the monomer begins to polymerize via a free-radical process and concurrently the nanoparticles move into the regions of destructive interference. The holographic exposure of the particles within the monomer resin offers a single-step method to anisotropically structure the nanoconstituents within a composite. A one-step holographic exposure was also used to fabricate self- healing coatings that use water from the environment to catalyze polymerization. Polymerization induced phase separation was used to sequester an isocyanate monomer within an acrylate matrix. Due to the periodic modulation of the index of refraction between the monomer and polymer, the coating can reflect a desired wavelength, allowing for tunable coloration. When the coating is scratched, polymerization of the liquid isocyanate is catalyzed by moisture in air; if the indices of the two polymers are matched, the coatings turn transparent after healing. Interference lithography offers a method of creating multifunctional self-healing coatings that readout when damage has occurred.

Seeing Turkish state formation processes: Mapping language and education census data

Language provides an interesting lens to look at state-building processes because of its cross-cutting nature. For example, in addition to its symbolic value and appeal, a national language has other roles in the process, including: (a) becoming the primary medium of communication which permits the nation to function efficiently in its political and economic life, (b) promoting social cohesion, allowing the nation to develop a common culture, and (c) forming a primordial basis for self-determination. Moreover, because of its cross-cutting nature, language interventions are rarely isolated activities. Languages are adopted by speakers, taking root in and spreading between communities because they are legitimated by legislation, and then reproduced through institutions like the education and military systems. Pádraig Ó’ Riagáin (1997) makes a case for this observing that “Language policy is formulated, implemented, and accomplishes its results within a complex interrelated set of economic, social, and political processes which include, inter alia, the operation of other non-language state policies” (p. 45). In the Turkish case, its foundational role in the formation of the Turkish nation-state but its linkages to human rights issues raises interesting issues about how socio-cultural practices become reproduced through institutional infrastructure formation. This dissertation is a country-level case study looking at Turkey’s nation-state building process through the lens of its language and education policy development processes with a focus on the early years of the Republic between 1927 and 1970. This project examines how different groups self-identified or were self-identified (as the case may be) in official Turkish statistical publications (e.g., the Turkish annual statistical yearbooks and the population censuses) during that time period when language and ethnicity data was made publicly available. The overarching questions this dissertation explores include: 1.What were the geo-political conditions surrounding the development and influencing the Turkish government’s language and education policies? 2.Are there any observable patterns in the geo-spatial distribution of language, literacy, and education participation rates over time? In what ways, are these traditionally linked variables (language, literacy, education participation) problematic? 3.What do changes in population identifiers, e.g., language and ethnicity, suggest about the government’s approach towards nation-state building through the construction of a civic Turkish identity and institution building? Archival secondary source data was digitized, aggregated by categories relevant to this project at national and provincial levels and over the course of time (primarily between 1927 and 2000). The data was then re-aggregated into values that could be longitudinally compared and then layered on aspatial administrative maps. This dissertation contributes to existing body of social policy literature by taking an interdisciplinary approach in looking at the larger socio-economic contexts in which language and education policies are produced.

Soft computing based parameter identification in pavements and geomechanical systems

Accurate estimation of road pavement geometry and layer material properties through the use of proper nondestructive testing and sensor technologies is essential for evaluating pavement’s structural condition and determining options for maintenance and rehabilitation. For these purposes, pavement deflection basins produced by the nondestructive Falling Weight Deflectometer (FWD) test data are commonly used. The nondestructive FWD test drops weights on the pavement to simulate traffic loads and measures the created pavement deflection basins. Backcalculation of pavement geometry and layer properties using FWD deflections is a difficult inverse problem, and the solution with conventional mathematical methods is often challenging due to the ill-posed nature of the problem. In this dissertation, a hybrid algorithm was developed to seek robust and fast solutions to this inverse problem. The algorithm is based on soft computing techniques, mainly Artificial Neural Networks (ANNs) and Genetic Algorithms (GAs) as well as the use of numerical analysis techniques to properly simulate the geomechanical system. A widely used pavement layered analysis program ILLI-PAVE was employed in the analyses of flexible pavements of various pavement types; including full-depth asphalt and conventional flexible pavements, were built on either lime stabilized soils or untreated subgrade. Nonlinear properties of the subgrade soil and the base course aggregate as transportation geomaterials were also considered. A computer program, Soft Computing Based System Identifier or SOFTSYS, was developed. In SOFTSYS, ANNs were used as surrogate models to provide faster solutions of the nonlinear finite element program ILLI-PAVE. The deflections obtained from FWD tests in the field were matched with the predictions obtained from the numerical simulations to develop SOFTSYS models. The solution to the inverse problem for multi-layered pavements is computationally hard to achieve and is often not feasible due to field variability and quality of the collected data. The primary difficulty in the analysis arises from the substantial increase in the degree of non-uniqueness of the mapping from the pavement layer parameters to the FWD deflections. The insensitivity of some layer properties lowered SOFTSYS model performances. Still, SOFTSYS models were shown to work effectively with the synthetic data obtained from ILLI-PAVE finite element solutions. In general, SOFTSYS solutions very closely matched the ILLI-PAVE mechanistic pavement analysis results. For SOFTSYS validation, field collected FWD data were successfully used to predict pavement layer thicknesses and layer moduli of in-service flexible pavements. Some of the very promising SOFTSYS results indicated average absolute errors on the order of 2%, 7%, and 4% for the Hot Mix Asphalt (HMA) thickness estimation of full-depth asphalt pavements, full-depth pavements on lime stabilized soils and conventional flexible pavements, respectively. The field validations of SOFTSYS data also produced meaningful results. The thickness data obtained from Ground Penetrating Radar testing matched reasonably well with predictions from SOFTSYS models. The differences observed in the HMA and lime stabilized soil layer thicknesses observed were attributed to deflection data variability from FWD tests. The backcalculated asphalt concrete layer thickness results matched better in the case of full-depth asphalt flexible pavements built on lime stabilized soils compared to conventional flexible pavements. Overall, SOFTSYS was capable of producing reliable thickness estimates despite the variability of field constructed asphalt layer thicknesses.