Anisotropic Wetting Property of Superhydrophobic Surfaces and Electrokinetic Flow on Liquid-Filled Surfaces

Understanding the wetting property of rough surface is critical in guiding droplets and novel superhydrophobic surface design. The Cassie-Baxter model and Wenzel model are always used to describe the totally non-wetting and completely wetting states, however, there were few discussions about the intermediate state. Through measuring the contact angles of groove patterned surfaces in different groove orientations, the anisotropic wetting properties of groove patterned superhydrophobic surface were investigated. The degree of water penetration into the grooves was experimentally observed and it was found that the degree of water penetration was different with groove orientations, which would affect the corresponding contact angle. Besides guiding droplets, superhydrophobic surfaces are also very important in microfluidic due to their ability to generate fluid slip and flow enhancement. After a deeper understanding of the wetting property of groove patterned superhydrophobic surface, I further investigated its important role in microfluidics. In this dissertation, I mainly focus on electrokinetics on groove patterned surface and liquid-filled slippery surfaces, a new kind of surface by filling low surface tension oil into the interstices of groove patterned surfaces. I experimentally measured the streaming potential on flat parylene surface, air-filled groove patterned surface and liquid-filled surfaces and compared their effects in streaming potential enhancement. The liquid-filled surfaces were shown to be able to enhance the generated streaming potential due to its slippery property and liquid-oil interface charges. As the electrokinetic on liquid-filled surfaces is a new phenomenon, the underlying physics is still not clear. I further investigated the influences of filled oil properties and groove orientation on streaming potentials and fluid slip. Oils with different densities, viscosities, dielectric constant, conductivities and surface tensions were filled into the interstices of groove patterned surfaces to make different types of liquid-filled surfaces. The streaming potentials on liquid-filled surfaces with different oils were experimentally measured. An empirical relationship between streaming potential and oil properties was found and the effects of electrical properties, such as interface charge density and dielectric constant of filled oil, on fluid slip were also studied. Finally, the groove orientation was varied to study the tensorial effects on streaming potential. Through both streaming potential measurement and theoretical analysis, it was found that the streaming potential at 45° was always smaller than the arithmetic mean of those at 0° and 90°, and the pressure gradient in the transvers direction generated by tensorial effects was important in the streaming potential modification. My work will be important in guiding droplets, flow patterning, lab-on-chip devices and the development of electrokientic based power sources

Stochastic gravitational-wave background from spin loss of black holes

Although spinning black holes are shown to be stable in vacuum in general relativity, there exists exotic mechanisms that can convert the spin energy of black holes into gravitational waves. Such waves may be very weak in amplitude, since the spin-down could take a long time, and a direct search may not be feasible. We propose to search for the stochastic background associated with the spin-down, and we relate the level of this background to the formation rate of spinning black holes from the merger of binary black holes, as well as the energy spectrum of waves emitted by the spin-down process. We argue that current LIGO-Virgo observations are not inconsistent with the existence of a spin-down process, as long as it is slow enough. On the other hand, the background may still exist as long as a moderate fraction of spin energy is emitted within Hubble time. This stochastic background could be one interesting target of next generation GW detector network, such as LIGO Voyager, and could be extracted from total stochastic background

ShenZhen transportation system (SZTS): a novel big data benchmark suite

Data analytics is at the core of the supply chain for both products and services in modern economies and societies. Big data workloads, however, are placing unprecedented demands on computing technologies, calling for a deep understanding and characterization of these emerging workloads. In this paper, we propose ShenZhen Transportation System (SZTS), a novel big data Hadoop benchmark suite comprised of real-life transportation analysis applications with real-life input data sets from Shenzhen in China. SZTS uniquely focuses on a specific and real-life application domain whereas other existing Hadoop benchmark suites, such as HiBench and CloudRank-D, consist of generic algorithms with synthetic inputs. We perform a cross-layer workload characterization at the microarchitecture level, the operating system (OS) level, and the job level, revealing unique characteristics of SZTS compared to existing Hadoop benchmarks as well as general-purpose multi-core PARSEC benchmarks. We also study the sensitivity of workload behavior with respect to input data size, and we propose a methodology for identifying representative input data sets

Performance evaluation of AODV, DSR and DSDV in mobile ad-hoc network using NS-2

Mobile ad-hoc network (MANET) is a set of movable hosts established without existing network infrastructure and can be self-organized dynamically. MANET protocols have faced big challenges due to dynamic changing network topology and asymmetric network link. In this paper, we simulate AODV, DSDV, DSR routing protocols in network simulator NS-2 and evaluate and compare the performance metrics for each routing protocol using packet delivery ratio, average end to end delay of packets and normalized routing overhead. We conduct the simulation by varying the sending rate of source node (2 packets/s and 4 packets/s) with different pause time using node movement model and cbr source traffic model