A sustainable supply of environmentally clean energy is one of the most significant challenges facing the 21st century as fossil fuel supplies are decreasing and world energy demand keeps increasing. The field of thermoelectric (TE) materials and devices, for efficient solid state cooling and power generation, has expanded significantly in recent years partly due to the advent of nanotechnology and the promise of higher efficiencies of electrical energy versus thermal energy inter-conversion. Such solid-state refrigeration and power generation based on thermoelectric phenomenon offer significant promises to technical applications in the computer, energy conversion, and consumer market applications. While the area itself has been investigated since the 1950's, traditional bulk thermoelectric materials, such as Bi₂Te₃ generally had low efficiencies (228}06 %) for commercial energy conversion applications. However, a deeper utilization of nanoscience at the quantum mechanical level and fabrication of lower dimensional materials such as nanowires and quantum wells, has shown promise of greatly increasing the energy conversion efficiency. Lower dimensional materials, such as nanoscale thin films and nanowires, have been predicted to have large thermoelectric figures of merit and conversion efficiencies of waste heat to useful energy for practical application, and there are strong indications that this is possible. However, ultimately one needs large volumes of active thermoelectric material for an overall high efficiency. This is, at the present time, not feasible as the typical manufacturing of bulk volumes from low dimensional structures is time consuming and difficult. In this thesis, we lay out several practical schemes to incorporate nanoscale features in bulk- fabricated thermoelectric materials for easy fabrication and a high thermoelectric figure of merit, ZT. We propose to fabricate a novel nanostructure material to impart low dimensional confinement and nanoscale defects for a high figure of merit. Various unique architectures and associated process techniques as well as device applications are introduced. It is anticipated that these techniques will lead to many novel thermoelectric materials, innovative and useful materials processing technologies, and technical applications for efficient thermal management and energy conversion of sunlight and automobile waste heat

Porcine reproductive and respiratory syndrome is an infectious disease of pigs caused by PRRS virus (PRRSV). A modified live-attenuated vaccine has been widely used to control the spread of PRRSV and the classification of field strains is a key for a successful control and prevention. Restriction fragment length polymorphism targeting the Open reading frame 5 (ORF5) genes is widely used to classify PRRSV strains but showed unstable accuracy. Phylogenetic analysis is a powerful tool for PRRSV classification with consistent accuracy but it demands large computational power as the number of sequences gets increased. Our study aimed to apply four machine learning (ML) algorithms, random forest, k-nearest neighbor, support vector machine and multilayer perceptron, to classify field PRRSV strains into four clades using amino acid scores based on ORF5 gene sequence. Our study used amino acid sequences of ORF5 gene in 1931 field PRRSV strains collected in the US from 2012 to 2020. Phylogenetic analysis was used to labels field PRRSV strains into one of four clades: Lineage 5 or three clades in Linage 1. We measured accuracy and time consumption of classification using four ML approaches by different size of gene sequences. We found that all four ML algorithms classify a large number of field strains in a very short time (<2.5 s) with very high accuracy (>0.99 Area under curve of the Receiver of operating characteristics curve). Furthermore, the random forest approach detects a total of 4 key amino acid positions for the classification of field PRRSV strains into four clades. Our finding will provide an insightful idea to develop a rapid and accurate classification model using genetic information, which also enables us to handle large genome datasets in real time or semi-real time for data-driven decision-making and more timely surveillance.

Nucleosome DNA unwrapping and its disassembly into hexasomes and tetrasomes is necessary for genomic access and plays an important role in transcription regulation. Previous single-molecule mechanical nucleosome unwrapping revealed a low- and a high-force transitions, and force-FRET pulling experiments showed that DNA unwrapping is asymmetric, occurring always first from one side before the other. However, the assignment of DNA segments involved in these transitions remains controversial. Here, using high-resolution optical tweezers with simultaneous single-molecule FRET detection, we show that the low-force transition corresponds to the undoing of the outer wrap of one side of the nucleosome (∼27 bp), a process that can occur either cooperatively or noncooperatively, whereas the high-force transition corresponds to the simultaneous unwrapping of ∼76 bp from both sides. This process may give rise stochastically to the disassembly of nucleosomes into hexasomes and tetrasomes whose unwrapping/rewrapping trajectories we establish. In contrast, nucleosome rewrapping does not exhibit asymmetry. To rationalize all previous nucleosome unwrapping experiments, it is necessary to invoke that mechanical unwrapping involves two nucleosome reorientations: one that contributes to the change in extension at the low-force transition and another that coincides but does not contribute to the high-force transition.