Investigation of structural, optical and electrical properties and valence band splitting in cu-ln-se compounds

Investigations on thin films that started decades back due to scientific curiosity in the properties of a two-dimensional solid, has developed into a leading research field in recent years due to the ever expanding applications of the thin films in the fann of a variety of active and passive microminiaturized components and devices, solar cells, radiation sowces and detectors, magnetic memory devices, interference filters, refection and antireflection coatings etc. [1]. The recent environment and energy resource concerns have aroused an enonnous interest in the study of materials in thin film form suitable for renewable energy sources such as photovoltaic devices. Recognition of the immense potential applications of the chalcopyrites that can fonn homojunctions or heterojunctions for solar cell fabrication has attracted many researchers to extensive and intense research on them. In this thesis, we have started with studies performed on CuInSe, thin films, a technologically well recognized compound belonging to the l•ill-VI family of semiconductors and have riveted on investigations on the preparation and characterization of compoWlds Culn3Se5. Culn5Seg and CuIn7Se12, an interesting group of compounds related to CuInSe2 called Ordered Vacancy Compounds, having promising applications in photovoltaic devices. A pioneering work attempted on preparing and characterizing the compound Culn7Sel2 is detailed in the chapters on OVC's. Investigation on valence band splitting in avc's have also been attempted for the first time and included as the last chapter in the thesis. Some of the salient features of the chalcopyrite c.ompounds are given in the next section .of this introductory chapter.

A Novel Decision Tree Algorithm for Numeric Datasets - C 4.5*Stat

Decision trees are very powerful tools for classification in data mining tasks that involves different types of attributes. When coming to handling numeric data sets, usually they are converted first to categorical types and then classified using information gain concepts. Information gain is a very popular and useful concept which tells you, whether any benefit occurs after splitting with a given attribute as far as information content is concerned. But this process is computationally intensive for large data sets. Also popular decision tree algorithms like ID3 cannot handle numeric data sets. This paper proposes statistical variance as an alternative to information gain as well as statistical mean to split attributes in completely numerical data sets. The new algorithm has been proved to be competent with respect to its information gain counterpart C4.5 and competent with many existing decision tree algorithms against the standard UCI benchmarking datasets using the ANOVA test in statistics. The specific advantages of this proposed new algorithm are that it avoids the computational overhead of information gain computation for large data sets with many attributes, as well as it avoids the conversion to categorical data from huge numeric data sets which also is a time consuming task. So as a summary, huge numeric datasets can be directly submitted to this algorithm without any attribute mappings or information gain computations. It also blends the two closely related fields statistics and data mining