Investigations on The Finite Size Effects of Some Inverse Spinels and Studies on Their Composites Based on Nitrile Rubber

Magnetism and magnetic materials have been an ever-attractive subject area for engineers and scientists alike because of its versatility in finding applications in useful devices. They find applications in a host of devices ranging from rudimentary devices like loud speakers to sophisticated gadgets like waveguides and Magnetic Random Access Memories (MRAM).The one and only material in the realm of magnetism that has been at the centre stage of applications is ferrites and in that spinel ferrites received the lions share as far as practical applications are concerned.It has been the endeavour of scientists and engineers to remove obsolescence and improve upon the existing so as to save energy and integrate in to various other systems. This has been the hallmark of material scientists and this has led to new materials and new technologies.In the field of ferrites too there has been considerable interest to devise new materials based on iron oxides and other compounds. This means synthesising ultra fine particles and tuning its properties to device new materials. There are various preparation techniques ranging from top- down to bottom-up approaches. This includes synthesising at molecular level, self assembling,gas based condensation. Iow temperature eo-precipitation, solgel process and high energy ball milling. Among these methods sol-gel process allows good control of the properties of ceramic materials. The advantage of this method includes processing at low temperature. mixing at the molecular level and fabrication of novel materials for various devices.Composites are materials. which combine the good qualities of one or more components. They can be prepared in situ or by mechanical means by the incorporation of fine particles in appropriate matrixes. The size of the magnetic powders as well as the nature of matrix affect the processability and other physical properties of the final product. These plastic/rubber magnets can in turn be useful for various applications in different devices. In applications involving ferrites at high frequencies, it is essential that the material possesses an appropriate dielectric permittivity and suitable magnetic permeability. This can be achieved by synthesizing rubber ferrite composites (RFC's). RFCs are very useful materials for microwave absorptions. Hence the synthesis of ferrites in the nanoregirne.investigations on their size effects on the structural, magnetic, and electrical properties and the incorporation of these ferrites into polymer matrixes assume significance.In the present study, nano particles of NiFe204, Li(!5Fe2S04 and Col-e-O, are prepared by sol gel method. By appropriate heat treatments, particles of different grain sizes are obtained. The structural, magnetic and electrical measurements are evaluated as a function of grain size and temperature. NiFel04 prepared in the ultrafine regime are then incorporated in nitrile rubber matrix. The incorporation was carried out according to a specific recipe and for various loadings of magnetic fillers. The cure characteristics, magnetic properties, electrical properties and mechanical properties of these elastomer blends are carried out. The electrical permittivity of all the rubber samples in the X - band are also conducted.

Emission spectral studies of phthalocyanines in borate glass matrix

Cochin University of Science & Technology

Spectral studies of naphthalocyanine (Nc) and rare earth phthalocyanine (RePc) molecules in an inorganic glassy borate matrix

Optical absorption and emission spectral studies of free and metal naphthalocyanine doped borate glass matrix are reported for the first time. Absorption spectra recorded in the UV- VIS-NIR region show the characteristic absorption bands, namely, the B-band and Q-band of the naphthalocyanine (Nc) molecule. Some of the important spectral parameters, namely, the optical absorption coefficient (α), molar extinction coefficient (ε) and absorption cross section (σa) of the principal absorption transitions are determined. Optical band gap (Eg) of the materials evaluated from the functional dependence of absorption coefficient on photon energy lies in the range 1.6 eV≤Eg≤2.1 eV. All fluorescence spectra except that of EuNc consist of an intense band in the 765 nm region corresponding to the excitation of Q-band. In EuNc the maximum fluorescence intensity band is observed at 824 nm. The intensity of the principal fluorescence band is maximum in ZnNc, whereas it is minimum in H2Nc. Radiative parameters of the principal fluorescence transitions corresponding to the Q-band excitation are also reported for the naphthalocyanine and phthalocyanine based matrices.

Optical properties of phthalocyanine molecules in cyano acrylate polymer matrix

Optical absorption and emission spectral studies of various phthalocyanine molecules, viz., LaPc, NdPc, SmPc, EuPc, CuPc and ZnPc in a polymer matrix of cyano acrylate are reported for the first time. All the absorption spectra show an intense B band (Soret) in the UV region followed by a weaker Q band in the visible region. The positions of the Q and B bands are found to have dependence on the metallic substitution. Values of the important spectral parameters, viz., molar extinction coefficient (ϵ), oscillator strength (f), radiative transition rate and decay time of the excited singlet state are also presented and compared with other solid matrices. The recorded fluorescence spectrum shows two broad emission bands in the case of NdPc, whereas for ZnPc only a very weak band is observed. The absence of emission bands for the other metallated phthalocyanines is attributed to increased spin orbit interaction and intersystem crossing.

Optimization of Two Stage Process for the Growth of In2S3 , CuInSe2 and CuIn (Sel-xSx)2Thin Films for Solar Cell Application

Two stage processes consisting of precursor preparation by thermal evaporation followed by chalcogenisation in the required atmosphere is found to be a feasible technique for the PV materials such as n-Beta In2S3, p-CulnSe2, p-CulnS2 and p-CuIn(Sel_xSx)2. The growth parameters such as chalcogenisation temperature and duration of chalcogenisation etc have been optimised in the present study.Single phase Beta-In2S3 thin films can be obtained by sulfurising the indium films above 300°C for 45 minutes. Low sulfurisation temperatures required prolonged annealing after the sulfurisation to obtain single phase Beta-1n2S3, which resulted in high material loss. The maximum band gap of 2.58 eV was obtained for the nearly stoichiometric Beta-In2S3 film which was sulfurised at 350°C. This wider band gap, n type Beta-In2S3 can be used as an alternative to toxic CdS as window layer in photovoltaics .The systematic study on the structural optical and electrical properties of CuInSe2 films by varying the process parameters such as the duration of selenization and the selenization temperature led to the conclusion that for the growth of single-phase CuInSe2, the optimum selenization temperature is 350°C and duration is 3 hours. The presence of some binary phases in films for shorter selenization period and lower selenization temperature may be due to the incomplete reaction and indium loss. Optical band gap energy of 1.05 eV obtained for the films under the optimum condition.In order to obtain a closer match to the solar spectrum it is desirable to increase the band gap of the CulnSe2 by a few meV . Further research works were carried out to produce graded band gap CuIn(Se,S)2 absorber films by incorporation of sulfur into CuInSe2. It was observed that when the CulnSe2 prepared by two stage process were post annealed in sulfur atmosphere, the sulfur may be occupying the interstitial positions or forming a CuInS2 phase along with CuInSe2 phase. The sulfur treatment during the selenization process OfCu11 ln9 precursors resulted in Culn (Se,S)2 thin films. A band gap of 1.38 eV was obtained for the CuIn(Se,S)2.The optimised thin films n-beta 1n2S3, p-CulnSe2 and p-Culn(Sel-xSx)2 can be used for fabrication of polycrystalline solar cells.

Optical properties of porphyrins in borate glassy matrix

Optical properties of free and substituted porphyrins (PP) doped borate glass matrix are reported for the first time. Absorption spectral measurements of H2TPP, CdTPP, MgTPP and ZnTPP doped borate glass matrix have been made in the 200–1100 nm region and the spectra obtained are analyzed to obtain the optical bandgap (Eg) and other important spectral parameters viz. oscillator strength (f), molar extinction coefficient (ε), electric dipole strength (q2), absorption cross-section (σa) and molecular concentration (N). Intense fluorescence was observed in the region 668–685 nm for CdTPP, ZnTPP and MgTPP doped matrices, whereas no such fluorescence was observed in H2TPP doped matrix. Fluorescence intensity was observed to be almost similar in all the metallated porphyrine matrices. Fluorescence bandwidth (Δλ), decay time (τ), stimulated emission cross-section (σ) and optical gain (G) of the principal fluorescence transitions corresponding to the Q-band excitation were also evaluated and discussed.

Optical-limiting response of rare-earth metallo-phthalocyanine-doped copolymer matrix

The nanosecond optical-limiting characteristics (at 532 nm) of some rare-earth metallo-phthalocyanines (Sm(Pc)2, Eu(Pc)2, and LaPc) doped in a copolymer matrix of poly(methyl methacrylate) and methyl-2-cyanoacrylate have been studied for the first time to our knowledge. The optical-limiting response is attributed to reverse saturable absorption due to excited-state absorption. The performance of LaPc in a copolymer host is studied at different linear transmissions. The laser damage thresholds of all the samples are also reported.

Mean Squared Residue Based Biclustering Algorithms for the Analysis of Gene Expression Data

Computational Biology is the research are that contributes to the analysis of biological data through the development of algorithms which will address significant research problems.The data from molecular biology includes DNA,RNA ,Protein and Gene expression data.Gene Expression Data provides the expression level of genes under different conditions.Gene expression is the process of transcribing the DNA sequence of a gene into mRNA sequences which in turn are later translated into proteins.The number of copies of mRNA produced is called the expression level of a gene.Gene expression data is organized in the form of a matrix. Rows in the matrix represent genes and columns in the matrix represent experimental conditions.Experimental conditions can be different tissue types or time points.Entries in the gene expression matrix are real values.Through the analysis of gene expression data it is possible to determine the behavioral patterns of genes such as similarity of their behavior,nature of their interaction,their respective contribution to the same pathways and so on. Similar expression patterns are exhibited by the genes participating in the same biological process.These patterns have immense relevance and application in bioinformatics and clinical research.Theses patterns are used in the medical domain for aid in more accurate diagnosis,prognosis,treatment planning.drug discovery and protein network analysis.To identify various patterns from gene expression data,data mining techniques are essential.Clustering is an important data mining technique for the analysis of gene expression data.To overcome the problems associated with clustering,biclustering is introduced.Biclustering refers to simultaneous clustering of both rows and columns of a data matrix. Clustering is a global whereas biclustering is a local model.Discovering local expression patterns is essential for identfying many genetic pathways that are not apparent otherwise.It is therefore necessary to move beyond the clustering paradigm towards developing approaches which are capable of discovering local patterns in gene expression data.A biclusters is a submatrix of the gene expression data matrix.The rows and columns in the submatrix need not be contiguous as in the gene expression data matrix.Biclusters are not disjoint.Computation of biclusters is costly because one will have to consider all the combinations of columans and rows in order to find out all the biclusters.The search space for the biclustering problem is 2 m+n where m and n are the number of genes and conditions respectively.Usually m+n is more than 3000.The biclustering problem is NP-hard.Biclustering is a powerful analytical tool for the biologist.The research reported in this thesis addresses the problem of biclustering.Ten algorithms are developed for the identification of coherent biclusters from gene expression data.All these algorithms are making use of a measure called mean squared residue to search for biclusters.The objective here is to identify the biclusters of maximum size with the mean squared residue lower than a given threshold. All these algorithms begin the search from tightly coregulated submatrices called the seeds.These seeds are generated by K-Means clustering algorithm.The algorithms developed can be classified as constraint based,greedy and metaheuristic.Constarint based algorithms uses one or more of the various constaints namely the MSR threshold and the MSR difference threshold.The greedy approach makes a locally optimal choice at each stage with the objective of finding the global optimum.In metaheuristic approaches particle Swarm Optimization(PSO) and variants of Greedy Randomized Adaptive Search Procedure(GRASP) are used for the identification of biclusters.These algorithms are implemented on the Yeast and Lymphoma datasets.Biologically relevant and statistically significant biclusters are identified by all these algorithms which are validated by Gene Ontology database.All these algorithms are compared with some other biclustering algorithms.Algorithms developed in this work overcome some of the problems associated with the already existing algorithms.With the help of some of the algorithms which are developed in this work biclusters with very high row variance,which is higher than the row variance of any other algorithm using mean squared residue, are identified from both Yeast and Lymphoma data sets.Such biclusters which make significant change in the expression level are highly relevant biologically.

Influence of additives on the charactaristics of stone matrix asphalt

The increase in traffic growth and maintenance expenditures demands the urgent need for building better, long-lasting, and more efficient roads preventing or minimizing bituminous pavement distresses. Many of the principal distresses in pavements initiate or increase in severity due to the presence of water. In Kerala highways, where traditional dense graded mixtures are used for the surface courses, major distress is due to moisture induced damages. The Stone Matrix Asphalt (SMA) mixtures provide a durable surface course. Proven field performance of test track at Delhi recommends Stone Matrix Asphalt as a right choice to sustain severe climatic and heavy traffic conditions. But the concept of SMA in India is not so popularized and its application is very limited mainly due to the lack of proper specifications. This research is an attempt to study the influence of additives on the characteristics of SMA mixtures and to propose an ideal surface course for the pavements. The additives used for this investigation are coir, sisal, banana fibres (natural fibres), waste plastics (waste material) and polypropylene (polymer). A preliminary investigation is conducted to characterize the materials used in this study. Marshall test is conducted for optimizing the SMA mixtures (Control mixture-without additives and Stabilized mixtures with additives). Indirect tensile strength tests, compression strength tests, triaxial strength tests and drain down sensitivity tests are conducted to study the engineering properties of stabilized mixtures. The comparison of the performance of all stabilized mixtures with the control mixture and among themselves are carried out. A statistical analysis (SPSS package Ver.16) is performed to establish the findings of this study

Squared interaction matrix Sherrington-Kirkpatrick model for a spin glass

The mean-field theory of a spin glass with a specific form of nearest- and next-nearest-neighbor interactions is investigated. Depending on the sign of the interaction matrix chosen we find either the continuous replica symmetry breaking seen in the Sherrington-Kirkpartick model or a one-step solution similar to that found in structural glasses. Our results are confirmed by numerical simulations and the link between the type of spin-glass behavior and the density of eigenvalues of the interaction matrix is discussed.

Semiclassical evaluation of average nuclear one- and two-body matrix elements

Thomas-Fermi theory is developed to evaluate nuclear matrix elements averaged on the energy shell, on the basis of independent particle Hamiltonians. One- and two-body matrix elements are compared with the quantal results, and it is demonstrated that the semiclassical matrix elements, as function of energy, well pass through the average of the scattered quantum values. For the one-body matrix elements it is shown how the Thomas-Fermi approach can be projected on good parity and also on good angular momentum. For the two-body case, the pairing matrix elements are considered explicitly.

Density matrix functional theory that includes pairing correlations

The extension of density functional theory (DFT) to include pairing correlations without formal violation of the particle-number conservation condition is described. This version of the theory can be considered as a foundation of the application of existing DFT plus pairing approaches to atoms, molecules, ultracooled and magnetically trapped atomic Fermi gases, and atomic nuclei where the number of particles is conserved exactly. The connection with Hartree-Fock-Bogoliubov (HFB) theory is discussed, and the method of quasilocal reduction of the nonlocal theory is also described. This quasilocal reduction allows equations of motion to be obtained which are much simpler for numerical solution than the equations corresponding to the nonlocal case. Our theory is applied to the study of some even Sn isotopes, and the results are compared with those obtained in the standard HFB theory and with the experimental ones.

Development of Techniques for the Automatic Extraction and Grade Detection of Glioma Tumors from Conventional Brain Magnetic Resonant Images

Cerebral glioma is the most prevalent primary brain tumor, which are classified broadly into low and high grades according to the degree of malignancy. High grade gliomas are highly malignant which possess a poor prognosis, and the patients survive less than eighteen months after diagnosis. Low grade gliomas are slow growing, least malignant and has better response to therapy. To date, histological grading is used as the standard technique for diagnosis, treatment planning and survival prediction. The main objective of this thesis is to propose novel methods for automatic extraction of low and high grade glioma and other brain tissues, grade detection techniques for glioma using conventional magnetic resonance imaging (MRI) modalities and 3D modelling of glioma from segmented tumor slices in order to assess the growth rate of tumors. Two new methods are developed for extracting tumor regions, of which the second method, named as Adaptive Gray level Algebraic set Segmentation Algorithm (AGASA) can also extract white matter and grey matter from T1 FLAIR an T2 weighted images. The methods were validated with manual Ground truth images, which showed promising results. The developed methods were compared with widely used Fuzzy c-means clustering technique and the robustness of the algorithm with respect to noise is also checked for different noise levels. Image texture can provide significant information on the (ab)normality of tissue, and this thesis expands this idea to tumour texture grading and detection. Based on the thresholds of discriminant first order and gray level cooccurrence matrix based second order statistical features three feature sets were formulated and a decision system was developed for grade detection of glioma from conventional T2 weighted MRI modality.The quantitative performance analysis using ROC curve showed 99.03% accuracy for distinguishing between advanced (aggressive) and early stage (non-aggressive) malignant glioma. The developed brain texture analysis techniques can improve the physician’s ability to detect and analyse pathologies leading to a more reliable diagnosis and treatment of disease. The segmented tumors were also used for volumetric modelling of tumors which can provide an idea of the growth rate of tumor; this can be used for assessing response to therapy and patient prognosis.