Supramolecular Organization of Metal Nanoparticles in Solution and of Phenyleneethynylenes on Surfaces

Most of the procedures reported for the synthesis of metal nanoparticles involve the use of strong reducing agents or elevated temperatures. This limits the possibility of developing metal nanoparticle based sensors for the in situ detection of analytes. One of the objectives of the present investigations is to (i) develop newer methodologies for the synthesis of metal nanoparticles in aqueous medium at ambient conditions and (ii) their use in the detection of metal cations by taking advantage of the unique coordination ability. Ideally, biocompatible molecules which possess both the reducing and stabilizing groups are desirable for such applications. Formation of stable supramolecular assembly, by bringing metal nanoparticles close to each other, results in plasmon coupling and this strategy can be effectively utilized for the development of metal nanoparticle based sensors.Another objective of the present study is to understand the supramolecular organization of molecules on surfaces. Various noncovalent interactions between the molecules and with surface play a decisive role in their organizations. An in-depth understanding of these interactions is essential for device fabrications. Recent photophysical studies have revealed that phenyleneethynylene based molecular systems are ideal for device application. The second objective of the thesis focuses on understanding the (i) organization of phenyleneethynylenes on highly oriented pyrolytic graphite (HOPG) surface with atomic level precision and (ii) weak intermolecular interactions which drive their organization.

Novel Melt Transurethane Process for Cycloaliphatic Polyurethanes and Their Self-Organization for Nano-Materials

In this introduction part, importance has been given to the elastomeric properties of polyurethanes. Emphasis has been laid to this property based on microphase separation and how this could be modified by modifying the segment lengths, as well as the structure of the segments. Implication was also made on the mechanical and thermal properties of these copolymers based on various analytical methods usually used for characterization of polymers. A brief overview of the challenges faced by the polyurethane chemistry was also done, pointing to the fact that though polyurethane industry is more than 75 years old, still a lot of questions remain unanswered, that too mostly in the synthesis of polyurethanes. A major challenge in this industry is the utilization of more environmental friendly “Green Chemistry Routes” for the synthesis of polyurethanes which are devoid of any isocyanates or harsh solvents.The research work in this thesis was focused to develop non-isocyanate green chemical process for polyurethanes and also self-organize the resultant novel polymers into nano-materials. The thesis was focused on the following three major aspects:(i) Design and development of novel melt transurethane process for polyurethanes under non-isocyanate and solvent free melt condition. (ii) Solvent induced self-organization of the novel cycloaliphatic polyurethanes prepared by the melt transurethane process into microporous templates and nano-sized polymeric hexagons and spheres. (iii) Novel polyurethane-oligophenylenevinylene random block copolymer nano-materials and their photoluminescence properties. The second chapter of the thesis gives an elaborate discussion on the “Novel Melt Transurethane Process ” for the synthesis of polyurethanes under non-isocyanate and solvent free melt condition. The polycondensation reaction was carried out between equimolar amounts of a di-urethane monomer and a diol in the presence of a catalyst under melt condition to produce polyurethanes followed by the removal of low boiling alcohol from equilibrium. The polymers synthesized through this green chemical route were found to be soluble (devoid of any cross links), thermally stable and free from any isocyanate entities. The polymerization reaction was confirmed by various analytical techniques with specific references to the extent of reaction which is the main watchful point for any successful polymerization reaction. The mechanistic aspects of the reaction were another point of consideration for the novel polymerization route which was successfully dealt with by performing various model reactions. Since this route was successful enough in synthesizing polyurethanes with novel structures, they were employed for the solvent induced self-organization which is an important area of research in the polymer world in the present scenario. Chapter three mesmerizes the reader with multitudes of morphologies depending upon the chemical backbone structure of the polyurethane as well as on the nature and amount of various solvents employed for the self-organization tactics. The rationale towards these morphologies-“Hydrogen Bonding ” have been systematically probed by various techniques. These polyurethanes were then tagged with luminescent 0ligo(phenylene vinylene) units and the effects of these OPV blocks on the morphology of the polyurethanes were analyzed in chapter four. These blocks have resulted in the formation of novel “Blue Luminescent Balls” which could find various applications in optoelectronic devices as well as delivery vehicles.

Development of Hierarchical Clustering Techniques for Gridded Data from Mixed Data Sequences

Knowledge discovery in databases is the non-trivial process of identifying valid, novel potentially useful and ultimately understandable patterns from data. The term Data mining refers to the process which does the exploratory analysis on the data and builds some model on the data. To infer patterns from data, data mining involves different approaches like association rule mining, classification techniques or clustering techniques. Among the many data mining techniques, clustering plays a major role, since it helps to group the related data for assessing properties and drawing conclusions. Most of the clustering algorithms act on a dataset with uniform format, since the similarity or dissimilarity between the data points is a significant factor in finding out the clusters. If a dataset consists of mixed attributes, i.e. a combination of numerical and categorical variables, a preferred approach is to convert different formats into a uniform format. The research study explores the various techniques to convert the mixed data sets to a numerical equivalent, so as to make it equipped for applying the statistical and similar algorithms. The results of clustering mixed category data after conversion to numeric data type have been demonstrated using a crime data set. The thesis also proposes an extension to the well known algorithm for handling mixed data types, to deal with data sets having only categorical data. The proposed conversion has been validated on a data set corresponding to breast cancer. Moreover, another issue with the clustering process is the visualization of output. Different geometric techniques like scatter plot, or projection plots are available, but none of the techniques display the result projecting the whole database but rather demonstrate attribute-pair wise analysis