Non-Equilibrium Green\u27s Function Based Calculations of Spin Voltage in Cobalt Based Spinel Oxides

Tougher sanctions on fossil fuel emissions and greater energy demand around the globe is forcing us to rely more on renewable energy resources. Clean energy technologies like thermoelectric power generation provides one solution to ease our dependence on non-renewable energy resources. Thermoelectric materials have the capability of directly converting heat to electricity. However, the efficiency of thermoelectrics has been limited due to the inherent coupling of the electronic and thermal carriers. More recently, an avenue to decouple these interactions has been experimental demonstrated using temperature gradient induced spin currents. With this new discovery comes the need for new transport models to understand and optimize their response. In providing a solution, this thesis is focused on the both the development of a 1D spin-transport model and the exploration of a spinel oxide design space comprising Co 3O4, Co2NiO4 and Co2ZnO 4 as the end configurations.;Ferromagnetic cobalt-based spinel oxides are potential candidates for spin-based thermoelectric energy conversion. Substitution of Co+2 and Co+3 ions with other transitional metal cations that exhibit +2 or +3 oxidation states, like Ni and Zn, can affect the net spin-polarization and electronic conductivity of the material. First principles calculations using density functional theory (DFT) were performed on supercells of cobalt-based spinel oxides to obtain their electronic band structure and density of states. The band gap, electron effective mass, the Fermi energy level, and the magnetization of the material configuration were used to calculated its spin transport characteristics in the presence of a temperature gradient.;A spin driven transport model was developed that is based on a non-equilibrium Greens function (NEGF) approach. This model treats both spin channels independently and calculates electronic and spin conductivities, and spin-Seebeck coefficient of the material. The overall thermoelectric figure of merit, ZT, of the material increased when doped Ni and Zn. These trends identified a focus region around Co2.25Ni0.25Zn0.5O4 that has the potential for a huge spin-Seebeck enhancement

Using Design Science Research to Develop a Conceptual Solution for Improving Knowledge Sharing in a Virtual Workspace

Enhancements in technology have resulted in significant changes to day-to-day operations of organizations in the present day. One especially noteworthy change is the alteration in the nature of teams from being co-located, with face-to-face interaction, to virtual, with the involvement of information and communication technologies (ICT) to facilitate communication. This change in team character has had a downstream impact on a key element of an organization’s competitive edge, namely knowledge. Overall, there is consensus that knowledge is a crucial facet of the competitive edge of an organization. Consequently, knowledge management, knowledge sharing, and organizational learning are essential components of an organization’s sustained existence and effectiveness in the competitive marketplace and considerable academic and industry attention has been paid to this matter. However, the present day scenario of global organizations and dispersed teams, within and across geographies, transforms the matter of knowledge sharing and organizational learning into one of great complexity. Thus, the present study was interested in understanding the modalities of knowledge sharing and consequently organizational learning in the context of a virtual workspace, that is, teams operating from physically distinct locations and communicating using ICT tools. Overall, the objective of this study was to propose a conceptual model using the Design Science Research (DSR) approach to enhance organizational learning and knowledge sharing in the context of the virtual workspaces of the present day work environment. Further, the conceptual model is extended to propose the use of a Learnin

Analysis of the effect of various ligands on the hydrolysis of ruthenium (III) complexes and interpretation of kinetics of hydrolysis profiles by UV-visible spectrophotometry

Cancer is one of the major causes of death in the world. Discovery of platinum metal-based drugs like cisplatin and carboplatin have proved to be successful in cancer treatment. Due to subsequent development of resistance, side effects, and fewer toxic effects of these drugs, the usage of these drugs has been limited. Novel drugs were being synthesized utilizing the transition metals like ruthenium, osmium, and copper. In this research, ruthenium metal complexes of the formula HL[RuCl4L2] (where L= ligand) were synthesized. These ruthenium-based drugs exist in prodrug forms which are activated into antitumor drugs by means of hydrolysis, redox reactions, or reactions with biological nucleophiles. In these reactions, ruthenium is reduced to the active Ru(II) form from its inactive Ru(III) state. In this research work, three ruthenium complexes with different ligands of varying basicity are synthesized, and their hydrolysis reactions are studied under different pH values using UV-Visible spectrophotometry at room temperature. The ligands utilized in this project are imidazole, thiazole, and 1H-1,2,4-triazole. Among these, ruthenium imidazole has passed the Phase I clinical trials. For the ruthenium-imidazole (RIM) complex and the ruthenium-thiazole (RTZ) complex, rates of the hydrolysis reaction are determined by fitting the experimental data to proposed kinetic models for these complexes. The kinetic models proposed did not help in the determination of the rate of hydrolysis of the ruthenium-triazole (RTrz) complex as the absorbance trend of the RTrz complex in acidic pH values was opposite to the trend displayed by the RIM and RTZ complexes indicating a different hydrolysis mechanism for the RTrz complex. The comparative data will aid in better drug design and evaluation of pharmacokinetic parameters. Future studies on hydrolysis of these complexes at different pH values using HPLC and NMR spectroscopy might reveal the exact mechanism and may lead to characterizing the products formed in the hydrolysis process

Implications of storage subsystem interactions on processing efficiency in data intensive computing

Includes bibliographical references.2015 Fall.Processing frameworks such as MapReduce allow development of programs that operate on voluminous on-disk data. These frameworks typically include support for multiple file/storage subsystems. This decoupling of processing frameworks from the underlying storage subsystem provides a great deal of flexibility in application development. However, as we demonstrate, this flexibility often exacts a price: performance. Given the data volumes, storage subsystems (such as HDFS, MongoDB, and HBase) disperse datasets over a collection of machines. Storage subsystems manage complexity relating to preservation of consistency, redundancy, failure recovery, throughput, and load balancing. Preserving these properties involve message exchanges between distributed subsystem components, updates to in-memory data structures, data movements, and coordination as datasets are staged and system conditions change. Storage subsystems prioritize these properties differently, leading to vastly different network, disk, memory, and CPU footprints for staging and accessing the same dataset. This thesis proposes a methodology for comparing and identifying the storage subsystem suited for the processing that is being performed on a dataset. We profile the network I/O, disk I/O, memory, and CPU costs introduced by a storage subsystem during data staging, data processing, and generation of results. We perform this analysis with different storage subsystems and applications with different disk-I/O to CPU processing ratios

Quantitative Analysis of Domain Testing Effectiveness.

The criticality of the applications modeled by the real-time software places stringent requirements on software quality before deploying into real use. Though automated test tools can be used to run a large number of tests efficiently, the functionality of any test tool is not complege without providing a means for analyzing the test results to determine potential problem sub-domains and sub-domains that need to be covered, and estimating the reliability of the modeled system. This thesis outlines a solution strategy and implementation of that strategy for deriving quantitative metrics from domain testing of real-time control software tested via simulation. The key portion of this thesis addresses the combinatorial problems involved with effective evaluation of test coverage and provides the developer with reliability metrics from testing of the software to gain confidence in the test phase of development. The two approaches for reliability analysis- time domain and input domain approaches are studied and a hybrid approach that combines the strengths of both these approaches is proposed. A Reliability analysis Test Tool (RATT) has been developed to implement the proposed strategies. The results show that the metrics are practically feasible to compute and can be applied to most real-time software