Carbon nanostructure based electrodes for high efficiency dye sensitized solar cell

Synthesis and functionalization of large-area graphene and its structural, electrical and electrochemical properties has been investigated. First, the graphene films, grown by thermal chemical vapor deposition (CVD), contain three to five atomic layers of graphene, as confirmed by Raman spectroscopy and high-resolution transmission electron microscopy. Furthermore, the graphene film is treated with CF4 reactive-ion plasma to dope fluorine ions into graphene lattice as confirmed by X-ray photoelectron spectroscopy (XPS) and UV-photoemission spectroscopy (UPS). Electrochemical characterization reveals that the catalytic activity of graphene for iodine reduction enhanced with increasing plasma treatment time, which is attributed to increase in catalytic sites of graphene for charge transfer. The fluorinated graphene is characterized as a counter-electrode (CE) in a dye-sensitized solar cell (DSSC) which shows ~ 2.56% photon to electron conversion efficiency with ~11 mAcm−2 current density. Second, the large scale graphene film is covalently functionalized with HNO3 for high efficiency electro-catalytic electrode for DSSC. The XPS and UPS confirm the covalent attachment of C-OH, C(O)OH and NO3- moieties with carbon atoms through sp2-sp3 hybridization and Fermi level shift of graphene occurs under different doping concentrations, respectively. Finally, CoS-implanted graphene (G-CoS) film was prepared using CVD followed by SILAR method. The G-CoS electro-catalytic electrodes are characterized in a DSSC CE and is found to be highly electro-catalytic towards iodine reduction with low charge transfer resistance (Rct ~5.05 Ωcm 2) and high exchange current density (J0~2.50 mAcm -2). The improved performance compared to the pristine graphene is attributed to the increased number of active catalytic sites of G-CoS and highly conducting path of graphene. We also studied the synthesis and characterization of graphene-carbon nanotube (CNT) hybrid film consisting of graphene supported by vertical CNTs on a Si substrate. The hybrid film is inverted and transferred to flexible substrates for its application in flexible electronics, demonstrating a distinguishable variation of electrical conductivity for both tension and compression. Furthermore, both turn-on field and total emission current was found to depend strongly on the bending radius of the film and were found to vary in ranges of 0.8 - 3.1 V/μm and 4.2 - 0.4 mA, respectively.

Field programmable gate array based target detection and gesture recognition

The move from Standard Definition (SD) to High Definition (HD) represents a six times increases in data, which needs to be processed. With expanding resolutions and evolving compression, there is a need for high performance with flexible architectures to allow for quick upgrade ability. The technology advances in image display resolutions, advanced compression techniques, and video intelligence. Software implementation of these systems can attain accuracy with tradeoffs among processing performance (to achieve specified frame rates, working on large image data sets), power and cost constraints. There is a need for new architectures to be in pace with the fast innovations in video and imaging. It contains dedicated hardware implementation of the pixel and frame rate processes on Field Programmable Gate Array (FPGA) to achieve the real-time performance. ^ The following outlines the contributions of the dissertation. (1) We develop a target detection system by applying a novel running average mean threshold (RAMT) approach to globalize the threshold required for background subtraction. This approach adapts the threshold automatically to different environments (indoor and outdoor) and different targets (humans and vehicles). For low power consumption and better performance, we design the complete system on FPGA. (2) We introduce a safe distance factor and develop an algorithm for occlusion occurrence detection during target tracking. A novel mean-threshold is calculated by motion-position analysis. (3) A new strategy for gesture recognition is developed using Combinational Neural Networks (CNN) based on a tree structure. Analysis of the method is done on American Sign Language (ASL) gestures. We introduce novel point of interests approach to reduce the feature vector size and gradient threshold approach for accurate classification. (4) We design a gesture recognition system using a hardware/ software co-simulation neural network for high speed and low memory storage requirements provided by the FPGA. We develop an innovative maximum distant algorithm which uses only 0.39% of the image as the feature vector to train and test the system design. Database set gestures involved in different applications may vary. Therefore, it is highly essential to keep the feature vector as low as possible while maintaining the same accuracy and performance^