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Optimizing cathodes for microbial fuel cells is important to maximize energy harvested from wastewater. Cathodes were made by modifying a recipe from previous literature and testing the current of the cathode using linear sweep voltammetry. The cathodes contained an Fe-N-C catalyst combined with a Polytetrafluoroethylene binder. Optimizing the power resulting from the microbial fuel cells will help MFCs be an alternative energy source to fossil fuels. The new cathodes did improve in current production from −16 𝐴/𝑚 to −37 𝐴/𝑚 at -0.4 V. When fitted using a Butler-Volmer model, the cathode linear-sweep voltammograms did not follow the expected exponential trend. These results show a need for more research on the cathodes and the Butler-Volmer model, and they also show that the cathode is ready for further and longer application in a microbial fuel cell.
Microbial peroxide producing cells (MPPCs) are a type of microbial electrochemical cells that are used to produce hydrogen peroxide (H2O2). Different catholytes were evaluated in biotic and abiotic reactors to determine their impacts on reactor performance. The abiotic reactor produced cathode efficiencies of less than 1%, leading us to investigate the potential causes of the low efficiency. An acid wash of the reactor parts was observed to significantly decrease the degradation rate of peroxide in the reactor, indicating that metal impurities in the catholyte solution was the driving cause of the low peroxide yields in the reactor. Diffusion testing confirmed that peroxide diffused across the anion exchange membrane (AEM) at a rate of 13.3 mg/L/hr, but had no significant impact on the overall peroxide produced in the reactor. We also confirmed that auto-decay of H2O2 was not responsible for the low observed yields.
This outlines a mathematical model created in MATLAB for the purposes of predicting nitrous oxide emissions from wastewater treatment plants with updated an updated understanding of AOB metabolic pathway.
Anaerobic Digestion (AD) typically stabilizes 40-60% of influent wastewater sludge. Improving the methane yield in wastewater may produce enough energy to power some wastewater treatment processes, while the production of volatile-fatty acids (VFAs) generates economic incentives for yard waste pre-fermentation. In this research, pre-fermenters consisting of inocula composed of media; cellulose, lantana, or grass; and rabbit cecotrope were fed various concentrations of plant matter. The contents of these pre-fermenters were the influent for respective anaerobic digesters. The microbial consortium derived for the lignocellulosic pretreatment with common yard waste in Arizona successfully increased methane production in AD, while producing additional VFAs during pretreatment in all systems. The performance of the system appeared to depend on plant matter loading and operating time, with a higher plant loading increasing the VFA production and a longer operating time increasing soluble chemical oxygen demand (COD) in pre-fermentation, and therefore the methane production in AD increased. The pre-fermenter with the highest plant matter loading and longest operating time –1.44 g plant matter per day at a 9.6% influent concentration and 193 days of total operating time– produced 10,000 mg COD/L of VFA, and its reactor produced about 460 mL methane (CH4) per day, which was almost twice the production of the control AD at 250 mL CH4 per day. This research uses yard waste that would previously be disposed of in landfill to increase valuable product production in AD. The potential value added to wastewater treatment plant (WWTP) processes by these methods could incentivize the expansion of wastewater treatment, thereby increasing sanitation access. The use of net-neutral biogas as a fuel source for WWTPs is additionally an incremental solution for reducing carbon equivalents present in the atmosphere, thereby reducing the greenhouse gas effect.
In the radiation environment, e.g. aerospace, the memory devices face different energetic particles. The strike of these energetic particles can generate electron-hole pairs (directly or indirectly) as they pass through the semiconductor device, resulting in photo-induced current, and may change the memory state. First, the trend of radiation effects of the mainstream memory technologies with technology node scaling is reviewed. Then, single event effects of the oxide based resistive switching random memory (RRAM), one of eNVM technologies, is investigated from the circuit-level to the system level.
Physical Unclonable Function (PUF) has been widely investigated as a promising hardware security primitive, which employs the inherent randomness in a physical system (e.g. the intrinsic semiconductor manufacturing variability). In the dissertation, two RRAM-based PUF implementations are proposed for cryptographic key generation (weak PUF) and device authentication (strong PUF), respectively. The performance of the RRAM PUFs are evaluated with experiment and simulation. The impact of non-ideal circuit effects on the performance of the PUFs is also investigated and optimization strategies are proposed to solve the non-ideal effects. Besides, the security resistance against modeling and machine learning attacks is analyzed as well.
Deep neural networks (DNNs) have shown remarkable improvements in various intelligent applications such as image classification, speech classification and object localization and detection. Increasing efforts have been devoted to develop hardware accelerators. In this dissertation, two types of compute-in-memory (CIM) based hardware accelerator designs with SRAM and eNVM technologies are proposed for two binary neural networks, i.e. hybrid BNN (HBNN) and XNOR-BNN, respectively, which are explored for the hardware resource-limited platforms, e.g. edge devices.. These designs feature with high the throughput, scalability, low latency and high energy efficiency. Finally, we have successfully taped-out and validated the proposed designs with SRAM technology in TSMC 65 nm.
Overall, this dissertation paves the paths for memory technologies’ new applications towards the secure and energy-efficient artificial intelligence system.
This thesis work develops camera-based systems and algorithms to monitor several physiological waveforms and parameters, without having to bring the sensors in contact with a subject. Based on skin color change, photoplethysmogram (PPG) waveform is recorded, from which heart rate and pulse transit time are obtained. Using a dual-wavelength illumination and triggered camera control system, blood oxygen saturation level is captured. By monitoring shoulder movement using differential imaging processing method, respiratory information is acquired, including breathing rate and breathing volume. Ballistocardiogram (BCG) is obtained based on facial feature detection and motion tracking. Blood pressure is further calculated from simultaneously recorded PPG and BCG, based on the time difference between these two waveforms.
The developed methods have been validated by comparisons against reference devices and through pilot studies. All of the aforementioned measurements are conducted without any physical contact between sensors and subjects. The work presented herein provides alternative solutions to track one’s health and wellness under normal living condition.