Matching Items (3)
Description
Nanotechnology is becoming increasingly present in our environment. Engineered nanoparticles (ENPs), defined as objects that measure less than 100 nanometers in at least one dimension, are being integrated into commercial products because of their small size, increased surface area, and quantum effects. These special properties have made ENPs antimicrobial agents in clothing and plastics, among other applications in industries such as pharmaceuticals, renewable energy, and prosthetics. This thesis incorporates investigations into both application of nanoparticles into polymers as well as implications of nanoparticle release into the environment. First, the integration of ENPs into polymer fibers via electrospinning was explored. Electrospinning uses an external electric field applied to a polymer solution to produce continuous fibers with large surface area and small volume, a quality which makes the fibers ideal for water and air purification purposes. Indium oxide and titanium dioxide nanoparticles were embedded in polyvinylpyrrolidone and polystyrene. Viscosity, critical voltage, and diameter of electrospun fibers were analyzed in order to determine the effects of nanoparticle integration into the polymers. Critical voltage and viscosity of solution increased at 5 wt% ENP concentration. Fiber morphology was not found to change significantly as a direct effect of ENP addition, but as an effect of increased viscosity and surface tension. These results indicate the possibility for seamless integration of ENPs into electrospun polymers. Implications of ENP release were investigated using phase distribution functional assays of nanoscale silver and silver sulfide, as well as photolysis experiments of nanoscale titanium dioxide to quantify hydroxyl radical production. Functional assays are a means of screening the relevant importance of multiple processes in the environmental fate and transport of ENPs. Four functional assays – water-soil, water-octanol, water-wastewater sludge and water-surfactant – were used to compare concentrations of silver sulfide ENPs (Ag2S-NP) and silver ENPs (AgNP) capped by four different coatings. The functional assays resulted in reproducible experiments which clearly showed variations between nanoparticle phase distributions; the findings may be a product of the effects of the different coatings of the ENPs used. In addition to phase distribution experiments, the production of hydroxyl radical (HO•) by nanoscale titanium dioxide (TiO2) under simulated solar irradiation was investigated. Hydroxyl radical are a short-lived, highly reactive species produced by solar radiation in aquatic environments that affect ecosystem function and degrades pollutants. HO• is produced by photolysis of TiO2 and nitrate (NO3-); these two species were used in photolysis experiments to compare the relative loads of hydroxyl radical which nanoscale TiO2 may add upon release to natural waters. Para-chlorobenzoic acid (pCBA) was used as a probe. Measured rates of pCBA oxidation in the presence of various concentrations of TiO2 nanoparticles and NO3- were utilized to calculate pseudo first order rate constants. Results indicate that, on a mass concentration basis in water, TiO2 produces hydroxyl radical steady state concentrations at 1.3 times more than the equivalent amount of NO3-; however, TiO2 concentrations are generally less than one order of magnitude lower than concentrations of NO3-. This has implications for natural waterways as the amount of nanoscale TiO2 released from consumer products into natural waterways increases in proportion to its use.
ContributorsHoogesteijn von Reitzenstein, Natalia (Author) / Westerhoff, Paul (Thesis advisor) / Herckes, Pierre (Committee member) / Hristovski, Kiril (Committee member) / Arizona State University (Publisher)
Created2015
Description
The spatial databases are used to store geometric objects such as points, lines, polygons. Querying such complex spatial objects becomes a challenging task. Index structures are used to improve the lookup performance of the stored objects in the databases, but traditional index structures cannot perform well in case of spatial databases. A significant amount of research is made to ingest, index and query the spatial objects based on different types of spatial queries, such as range, nearest neighbor, and join queries. Compressed Spatial Bitmap Index (cSHB) structure is one such example of indexing and querying approach that supports spatial range query workloads (set of queries). cSHB indexes and many other approaches lack parallel computation. The massive amount of spatial data requires a lot of computation and traditional methods are insufficient to address these issues. Other existing parallel processing approaches lack in load-balancing of parallel tasks which leads to resource overloading bottlenecks.
In this thesis, I propose novel spatial partitioning techniques, Max Containment Clustering and Max Containment Clustering with Separation, to create load-balanced partitions of a range query workload. Each partition takes a similar amount of time to process the spatial queries and reduces the response latency by minimizing the disk access cost and optimizing the bitmap operations. The partitions created are processed in parallel using cSHB indexes. The proposed techniques utilize the block-based organization of bitmaps in the cSHB index and improve the performance of the cSHB index for processing a range query workload.
In this thesis, I propose novel spatial partitioning techniques, Max Containment Clustering and Max Containment Clustering with Separation, to create load-balanced partitions of a range query workload. Each partition takes a similar amount of time to process the spatial queries and reduces the response latency by minimizing the disk access cost and optimizing the bitmap operations. The partitions created are processed in parallel using cSHB indexes. The proposed techniques utilize the block-based organization of bitmaps in the cSHB index and improve the performance of the cSHB index for processing a range query workload.
ContributorsGadkari, Ashish (Author) / Candan, Kasim Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Sapino, Maria Luisa (Committee member) / Arizona State University (Publisher)
Created2018
Description
Apache Spark is one of the most widely adopted open-source Big Data processing engines. High performance and ease of use for a wide class of users are some of the primary reasons for the wide adoption. Although data partitioning increases the performance of the analytics workload, its application to Apache Spark is very limited due to layered data abstractions. Once data is written to a stable storage system like Hadoop Distributed File System (HDFS), the data locality information is lost, and while reading the data back into Spark’s in-memory layer, the reading process is random which incurs shuffle overhead. This report investigates the use of metadata information that is stored along with the data itself for reducing shuffle overload in the join-based workloads. It explores the Hyperspace library to mitigate the shuffle overhead for Spark SQL applications. The article also introduces the Lachesis system to solve the shuffle overhead problem. The benchmark results show that the persistent partition and co-location techniques can be beneficial for matrix multiplication using SQL (Structured Query Language) operator along with the TPC-H analytical queries benchmark. The study concludes with a discussion about the trade-offs of using integrated stable storage to layered storage abstractions. It also discusses the feasibility of integration of the Machine Learning (ML) inference phase with the SQL operators along with cross-engine compatibility for employing data locality information.
ContributorsBarhate, Pratik Narhar (Author) / Zou, Jia (Thesis advisor) / Zhao, Ming (Committee member) / Elsayed, Mohamed Sarwat (Committee member) / Arizona State University (Publisher)
Created2021