Magnetic and processability studies on rubber ferrite composites based on natural rubber and mixed ferrite

Polycrystalline single phasic mixed ferrites belonging to the series Ni1−xZnxFe2O4 for various values of x have been prepared by conventional ceramic techniques. Pre-characterized nickel zinc ferrites were then incorporated into a natural rubber matrix according to a specific recipe for various loadings. The processability and cure parameters were then determined. The magnetic properties of the ceramic filler as well as the ferrite loaded rubber ferrite composites (RFC) were evaluated and compared. A general equation for predicting the magnetic properties was also formulated. The validity of these equations were then checked and correlated with the experimental data. The coercivity of the RFCs almost resemble that of the ceramic component in the RFC. Percolation threshold is not reached for a maximum loading of 120 phr (parts per hundred rubber by weight) of the filler. These studies indicate that flexible magnets can be made with appropriate magnetic properties namely saturation magnetisation (Ms) and magnetic field strength (Hc) by a judicious choice of x and a corresponding loading. These studies also suggest that there is no possible interaction between the filler and the matrix at least at the macroscopic level. The formulated equation will aid in synthesizing RFCs with predetermined magnetic

Tailoring magnetic and dielectric properties of rubber ferrite composites containing mixed ferrites

Rubber ferrite composites containing various mixed ferrites were prepared for different compositions and various loadings. The magnetic and dielectric properties of the fillers as well as the ferrite filled matrixes were evaluated separately. The results are correlated. Simple equations are proposed to predetermine the magnetic and dielectric properties. The validity of these equations is verified and they are found to be in good agreement. These equations are useful in tailoring the magnetic and dielectric properties of these composites with predetermined properties

On the dielectric dispersion and absorption in nanosized manganese zinc mixed ferrites

The temperature and frequency dependence of dielectric permittivity and dielectric loss of nanosized Mn1−xZnxFe2O4 (for x = 0, 0.2, 0.4, 0.6, 0.8, 1) were investigated. The impact of zinc substitution on the dielectric properties of the mixed ferrite is elucidated. Strong dielectric dispersion and broad relaxation were exhibited by Mn1−xZnxFe2O4. The variation of dielectric relaxation time with temperature suggests the involvement of multiple relaxation processes. Cole–Cole plots were employed as an effective tool for studying the observed phenomenon. The activation energies were calculated from relaxation peaks and Cole–Cole plots and found to be consistent with each other and indicative of a polaron conduction

Mechanism of ac conduction in nanostructured manganese zinc mixed ferrites

Mn1−xZnxFe2O4 nanoparticles (x = 0 to 1) were synthesized by the wet chemical co-precipitation technique. X-ray diffraction and transmission electron microscopy and high resolution transmission electron microscopy were effectively utilized to investigate the different structural parameters. The ac conductivity of nanosized Mn1−xZnxFe2O4 were investigated as a function of frequency, temperature and composition. The frequency dependence of ac conductivity is analysed by the power law σ(ω)ac = Bωn which is typical for charge transport by hopping or tunnelling processes. The temperature dependence of frequency exponent n was investigated to understand the conduction mechanism in different compositions. The conduction mechanisms are mainly based on polaron hopping conduction

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