Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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Description
A Microbial fuel cell (MFC) is a bio-inspired carbon-neutral, renewable electrochemical converter to extract electricity from catabolic reaction of micro-organisms. It is a promising technology capable of directly converting the abundant biomass on the planet into electricity and potentially alleviate the emerging global warming and energy crisis. The current and power density of MFCs are low compared with conventional energy conversion techniques. Since its debut in 2002, many studies have been performed by adopting a variety of new configurations and structures to improve the power density. The reported maximum areal and volumetric power densities range from 19 mW/m2 to 1.57 W/m2 and from 6.3 W/m3 to 392 W/m3, respectively, which are still low compared with conventional energy conversion techniques. In this dissertation, the impact of scaling effect on the performance of MFCs are investigated, and it is found that by scaling down the characteristic length of MFCs, the surface area to volume ratio increases and the current and power density improves. As a result, a miniaturized MFC fabricated by Micro-Electro-Mechanical System(MEMS) technology with gold anode is presented in this dissertation, which demonstrate a high power density of 3300 W/m3. The performance of the MEMS MFC is further improved by adopting anodes with higher surface area to volume ratio, such as carbon nanotube (CNT) and graphene based anodes, and the maximum power density is further improved to a record high power density of 11220 W/m3. A novel supercapacitor by regulating the respiration of the bacteria is also presented, and a high power density of 531.2 A/m2 (1,060,000 A/m3) and 197.5 W/m2 (395,000 W/m3), respectively, are marked, which are one to two orders of magnitude higher than any previously reported microbial electrochemical techniques.
ContributorsRen, Hao (Author) / Chae, Junseok (Thesis advisor) / Bakkaloglu, Bertan (Committee member) / Phillips, Stephen (Committee member) / Goryll, Michael (Committee member) / Arizona State University (Publisher)
Created2016
Description
A photovoltaic (PV) module is a series and parallel connection of multiple PV cells; defects in any cell can cause module power to drop. Similarly, a photovoltaic system is a series and parallel connection of multiple modules, and any low-performing module in the PV system can decrease the system output power. Defects in a solar cell include, but not limited to, the presence of cracks, potential induced degradation (PID), delamination, corrosion, and solder bond degradation. State-of-the-art characterization techniques to identify the defective cells in a module and defective module in a string are i) Current-voltage (IV) curve tracing, ii) Electroluminescence (EL) imaging, and iii) Infrared (IR) imaging. Shortcomings of these techniques include i) unsafe connection and disconnection need to be made with high voltage electrical cables, and ii) labor and time intensive disconnection of the photovoltaic strings from the system.This work presents a non-contact characterization technique to address the above two shortcomings. This technique uses a non-contact electrostatic voltmeter (ESV) along with a probe sensor to measure the surface potential of individual solar cells in a commercial module and the modules in a string in both off-grid and grid-connected systems. Unlike the EL approach, the ESV setup directly measures the surface potential by sensing the electric field lines that are present on the surface of the solar cell.
The off-grid testing of ESV on individual cells and multicells in crystalline silicon (c-Si) modules and on individual cells in cadmium telluride (CdTe) modules and individual modules in a CdTe string showed less than 2% difference in open circuit voltage compared to the voltmeter values. In addition, surface potential mapping of the defective cracked cells in a multicell module using ESV identified the dark, grey, and bright areas of EL images precisely at the exact locations shown by the EL characterization.
The on-grid testing of ESV measured the individual module voltages at maximum power point (Vmpp) and quantitatively identified the exact PID-affected module in the entire system. In addition, the poor-performing non-PID modules of a grid-connected PV system were also identified using the ESV technique.
ContributorsRaza, Hamza Ahmad (Author) / Tamizhmani, Govindasamy (Thesis advisor) / Kiaei, Sayfe (Committee member) / Bakkaloglu, Bertan (Committee member) / Hacke, Peter (Committee member) / Arizona State University (Publisher)
Created2023