An investigation by AFM and TEM of the mechanism of anodic formation of nanoporosity in n-InP in KOH

The early stages of nanoporous layer formation, under anodic conditions in the absence of light, were investigated for n-type InP with a carrier concentration of ∼3× 1018 cm-3 in 5 mol dm-3 KOH and a mechanism for the process is proposed. At potentials less than ∼0.35 V, spectroscopic ellipsometry and transmission electron microscopy (TEM) showed a thin oxide film on the surface. Atomic force microscopy (AFM) of electrode surfaces showed no pitting below ∼0.35 V but clearly showed etch pit formation in the range 0.4-0.53 V. The density of surface pits increased with time in both linear potential sweep and constant potential reaching a constant value at a time corresponding approximately to the current peak in linear sweep voltammograms and current-time curves at constant potential. TEM clearly showed individual nanoporous domains separated from the surface by a dense ∼40 nm InP layer. It is concluded that each domain develops as a result of directionally preferential pore propagation from an individual surface pit which forms a channel through this near-surface layer. As they grow larger, domains meet, and the merging of multiple domains eventually leads to a continuous nanoporous sub-surface region.

Content-aware compression for big textual data analysis

A substantial amount of information on the Internet is present in the form of text. The value of this semi-structured and unstructured data has been widely acknowledged, with consequent scientific and commercial exploitation. The ever-increasing data production, however, pushes data analytic platforms to their limit. This thesis proposes techniques for more efficient textual big data analysis suitable for the Hadoop analytic platform. This research explores the direct processing of compressed textual data. The focus is on developing novel compression methods with a number of desirable properties to support text-based big data analysis in distributed environments. The novel contributions of this work include the following. Firstly, a Content-aware Partial Compression (CaPC) scheme is developed. CaPC makes a distinction between informational and functional content in which only the informational content is compressed. Thus, the compressed data is made transparent to existing software libraries which often rely on functional content to work. Secondly, a context-free bit-oriented compression scheme (Approximated Huffman Compression) based on the Huffman algorithm is developed. This uses a hybrid data structure that allows pattern searching in compressed data in linear time. Thirdly, several modern compression schemes have been extended so that the compressed data can be safely split with respect to logical data records in distributed file systems. Furthermore, an innovative two layer compression architecture is used, in which each compression layer is appropriate for the corresponding stage of data processing. Peripheral libraries are developed that seamlessly link the proposed compression schemes to existing analytic platforms and computational frameworks, and also make the use of the compressed data transparent to developers. The compression schemes have been evaluated for a number of standard MapReduce analysis tasks using a collection of real-world datasets. In comparison with existing solutions, they have shown substantial improvement in performance and significant reduction in system resource requirements.