First, the recombination mechanisms in polytype GaAs nanowires are studied through photoluminescence measurements coupled with rate equation analysis. When photons are absorbed in polytype nanowires, electrons and holes quickly thermalize to the band-edges of the zinc-blende and wurtzite phases, recombining indirectly in space across the type-II offset. Using a rate equation model, different configurations of polytype defects along the nanowire are investigated, which compare well with experiment considering spatially indirect recombination between different polytypes, and defect-related recombination due to twin planes and other defects. The presented analysis is a path towards predicting the performance of nanowire-based solar cells.
Following this topic, the optical mechanisms in silicon nanopillar arrays are investigated using full-wave optical simulations in comparison to measured reflectance data. The simulated electric field energy density profiles are used to elucidate the mechanisms contributing to the reduced front surface reflectance. Strong forward scattering and resonant absorption are observed for shorter- and longer- aspect ratio nanopillars, respectively, with the sub-wavelength periodicity causing additional diffraction. Their potential for light-trapping is investigated using full-wave optical simulation of an ultra-thin nanostructured substrate, where the conventional light-trapping limit is exceeded for near-bandgap wavelengths.
Finally, the correlation between the optical properties of silicon nanoparticle layers to their respective pore size distributions is investigated using optical and structural characterization coupled with full-wave optical simulation. The presence of
scattering is experimentally correlated to wider pore size distributions obtained from nitrogen adsorption measurements. The correlation is validated with optical simulation of random and clustered structures, with the latter approximating experimental. Reduced structural inhomogeneity in low-refractive-index nanoparticle inter-layers at the metal/semiconductor interface improves their performance as back reflectors, while reducing parasitic absorption in the metal.
The dissertation utilizes Interval Data (ID) and establishes three different frameworks to identify electricity losses, predict electricity consumption and detect anomalies using data mining, deep learning, and mathematical models. The process of energy analytics integrates with the computational science and contributes to several objectives which are to
1. Develop a framework to identify both technical and non-technical losses using clustering and semi-supervised learning techniques.
2. Develop an integrated framework to predict electricity consumption using wavelet based data transformation model and deep learning algorithms.
3. Develop a framework to detect anomalies using ensemble empirical mode decomposition and isolation forest algorithms.
With a thorough research background, the first phase details on performing data analytics on the demand-supply database to determine the potential energy loss reduction potentials. Data preprocessing and electricity prediction framework in the second phase integrates mathematical models and deep learning algorithms to accurately predict consumption. The third phase employs data decomposition model and data mining techniques to detect the anomalies of institutional buildings.
Human protein diversity arises as a result of alternative splicing, single nucleotide polymorphisms (SNPs) and posttranslational modifications. Because of these processes, each protein can exists as multiple variants in vivo. Tailored strategies are needed to study these protein variants and understand their role in health and disease. In this work we utilized quantitative mass spectrometric immunoassays to determine the protein variants concentration of beta-2-microglobulin, cystatin C, retinol binding protein, and transthyretin, in a population of 500 healthy individuals. Additionally, we determined the longitudinal concentration changes for the protein variants from four individuals over a 6 month period. Along with the native forms of the four proteins, 13 posttranslationally modified variants and 7 SNP-derived variants were detected and their concentration determined. Correlations of the variants concentration with geographical origin, gender, and age of the individuals were also examined. This work represents an important step toward building a catalog of protein variants concentrations and examining their longitudinal changes.