Negative index metamaterials and metaspacers for optical frequencies

Metamaterials are artificial materials that exhibit properties, such as negative index of refraction, that are not possible through natural materials. Due to many potential applications of negative index metamaterials, significant progress in the field has been observed in the last decade. However, achieving negative index at visible frequencies is a challenging task. Generally, fishnet metamaterials are considered as a possible route to achieve negative index in the visible spectrum. However, so far no metamaterial has been demonstrated to exhibit simultaneously negative permittivity and permeability (double-negative) beyond the red region of the visible spectrum. This study is mainly focused on achieving higher operating frequency for low-loss, double-negative metamaterials. Two double-negative metamaterials have been proposed to operate at highest reported frequencies. The first proposed metamaterial is based on the interaction of surface plasmon polaritons of a thin metal film with localized surface plasmons of a metallic array placed close to the thin film. It is demonstrated that the metamaterial can easily be scaled to operate at any frequency in the visible spectrum as well as possibly to the ultraviolet spectrum. Furthermore, the underlying physical phenomena and possible future extensions of the metamaterial are also investigated. The second proposed metamaterial is a modification to the so-called fishnet metamaterial. It has been demonstrated that this ‘modified fishnet’ exhibits two double-negative bands in the visible spectrum with highest operating frequency in the green region with considerably high figure of merit. In contrast to most of the fishnet metamaterials proposed in the past, behavior of this modified fishnet is independent of polarization of the incident field. In addition to the two negative index metamaterials proposed in this study, the use of metamaterial as a spacer, named as metaspacer, is also investigated. In contrast to naturally available dielectric spacers used in microfabrication, metaspacers can be realized with any (positive or negative) permittivity and permeability. As an example, the use of a negative index metaspacer in place of the dielectric layer in a fishnet metamaterial is investigated. It is shown that fishnet based on negative index metaspacer gives many improved optical properties over the conventional fishnet such as wider negative index band, higher figure of merit, higher optical transmission and stronger magnetic response. In addition to the improved properties, following interesting features were observed in the metaspacer based fishnet metamaterial. At the resonance frequency, the shape of the permeability curve was ‘inverted’ as compared to that for conventional fishnet metamaterial. Furthermore, dependence of the resonance frequency on the fishnet geometry was also reversed. Moreover, simultaneously negative group and phase velocities were observed in the low-loss region of the metaspacer based fishnet metamaterial. Due to interesting features observed using metaspacer, this study will open a new horizon for the metamaterial research.

IDENTIFICATION OF GENES CONTROLLING BIOLOGICAL PROCESSES AND PATHWAYS THROUGH STATISTICAL ANALYSIS AND NETWORK RECONSTRUCTION

The developmental processes and functions of an organism are controlled by the genes and the proteins that are derived from these genes. The identification of key genes and the reconstruction of gene networks can provide a model to help us understand the regulatory mechanisms for the initiation and progression of biological processes or functional abnormalities (e.g. diseases) in living organisms. In this dissertation, I have developed statistical methods to identify the genes and transcription factors (TFs) involved in biological processes, constructed their regulatory networks, and also evaluated some existing association methods to find robust methods for coexpression analyses. Two kinds of data sets were used for this work: genotype data and gene expression microarray data. On the basis of these data sets, this dissertation has two major parts, together forming six chapters. The first part deals with developing association methods for rare variants using genotype data (chapter 4 and 5). The second part deals with developing and/or evaluating statistical methods to identify genes and TFs involved in biological processes, and construction of their regulatory networks using gene expression data (chapter 2, 3, and 6). For the first part, I have developed two methods to find the groupwise association of rare variants with given diseases or traits. The first method is based on kernel machine learning and can be applied to both quantitative as well as qualitative traits. Simulation results showed that the proposed method has improved power over the existing weighted sum method (WS) in most settings. The second method uses multiple phenotypes to select a few top significant genes. It then finds the association of each gene with each phenotype while controlling the population stratification by adjusting the data for ancestry using principal components. This method was applied to GAW 17 data and was able to find several disease risk genes. For the second part, I have worked on three problems. First problem involved evaluation of eight gene association methods. A very comprehensive comparison of these methods with further analysis clearly demonstrates the distinct and common performance of these eight gene association methods. For the second problem, an algorithm named the bottom-up graphical Gaussian model was developed to identify the TFs that regulate pathway genes and reconstruct their hierarchical regulatory networks. This algorithm has produced very significant results and it is the first report to produce such hierarchical networks for these pathways. The third problem dealt with developing another algorithm called the top-down graphical Gaussian model that identifies the network governed by a specific TF. The network produced by the algorithm is proven to be of very high accuracy.