Photothermal Investigations on Certain Plasma Polymerized Thin Films and Near IR Overtone Studies of Some Organic Molecules

Photothermal effect refers to heating of a sample due to the absorption of electromagnetic radiation. Photothermal (PT) heat generation which is an example of energy conversion has in general three kinds of applications. 1. PT material probing 2. PT material processing and 3. PT material destruction. The temperatures involved increases from 1-. 3. Of the above three, PT material probing is the most important in making significant contribution to the field of science and technology. Photothermal material characterization relies on high sensitivity detection techniques to monitor the effects caused by PT material heating of a sample. Photothermal method is a powerful high sensitivity non-contact tool used for non-destructive thermal characterization of materials. The high sensitivity of the photothermal methods has led to its application for analysis of low absorbance samples. Laser calorimetry, photothermal radiometry, pyroelectric technique, photoacoustic technique, photothermal beam deflection technique, etc. come under the broad class ofphotothermal techniques. However the choice of a suitable technique depends upon the nature of the sample, purpose of measurement, nature of light source used, etc. The present investigations are done on polymer thin films employing photothermal beam deflection technique, for the successful determination of their thermal diffusivity. Here the sample is excited by a He-Ne laser (A = 6328...\ ) which acts as the pump beam. Due to the refractive index gradient established in the sample surface and in the adjacent coupling medium, another optical beam called probe beam (diode laser, A= 6500A ) when passed through this region experiences a deflection and is detected using a position sensitive detector and its output is fed to a lock-in amplifier from which the amplitude and phase of the deflection can be directly obtained. The amplitude and phase of the signal is suitably analysed for determining the thermal diffusivity.The production of polymer thin film samples has gained considerable attention for the past few years. Plasma polymerization is an inexpensive tool for fabricating organic thin films. It refers to formation of polymeric materials under the influence of plasma, which is generated by some kind of electric discharge. Here plasma of the monomer vapour is generated by employing radio frequency (MHz) techniques. Plasma polymerization technique results in homogeneous, highly adhesive, thermally stable, pinhole free, dielectric, highly branched and cross-linked polymer films. The possible linkage in the formation of the polymers is suggested by comparing the FTIR spectra of the monomer and the polymer.Near IR overtone investigations on some organic molecules using local mode model are also done. Higher vibrational overtones often provide spectral simplification and greater resolution of peaks corresponding to nonequivalent X-H bonds where X is typically C, N or O. Vibrational overtone spectroscopy of molecules containing X-H oscillators is now a well established tool for molecular investigations. Conformational and steric differences between bonds and structural inequivalence ofCH bonds (methyl, aryl, acetylenic, etc.) are resolvable in the higher overtone spectra. The local mode model in which the X-H oscillators are considered to be loosely coupled anharmonic oscillators has been widely used for the interpretation of overtone spectra. If we are exciting a single local oscillator from the vibrational ground state to the vibrational state v, then the transition energy of the local mode overtone is given by .:lE a......v = A v + B v2 • A plot of .:lE / v versus v will yield A, the local mode frequency as the intercept and B, the local mode diagonal anharmonicity as the slope. Here A - B gives the mechanical frequency XI of the oscillator and B = X2 is the anharmonicity of the bond. The local mode parameters XI and X2 vary for non-equivalent X-H bonds and are sensitive to the inter and intra molecular environment of the X-H oscillator.

Spectral Analysis of Bounded Self-adjoint operators - A Linear Algebraic Approach

This thesis Entitled Spectral theory of bounded self-adjoint operators -A linear algebraic approach.The main results of the thesis can be classified as three different approaches to the spectral approximation problems. The truncation method and its perturbed versions are part of the classical linear algebraic approach to the subject. The usage of block Toeplitz-Laurent operators and the matrix valued symbols is considered as a particular example where the linear algebraic techniques are effective in simplifying problems in inverse spectral theory. The abstract approach to the spectral approximation problems via pre-conditioners and Korovkin-type theorems is an attempt to make the computations involved, well conditioned. However, in all these approaches, linear algebra comes as the central object. The objective of this study is to discuss the linear algebraic techniques in the spectral theory of bounded self-adjoint operators on a separable Hilbert space. The usage of truncation method in approximating the bounds of essential spectrum and the discrete spectral values outside these bounds is well known. The spectral gap prediction and related results was proved in the second chapter. The discrete versions of Borg-type theorems, proved in the third chapter, partly overlap with some known results in operator theory. The pure linear algebraic approach is the main novelty of the results proved here.

Spectral clustering independent component analysis for tissue classification from brain MRI

A spectral angle based feature extraction method, Spectral Clustering Independent Component Analysis (SC-ICA), is proposed in this work to improve the brain tissue classification from Magnetic Resonance Images (MRI). SC-ICA provides equal priority to global and local features; thereby it tries to resolve the inefficiency of conventional approaches in abnormal tissue extraction. First, input multispectral MRI is divided into different clusters by a spectral distance based clustering. Then, Independent Component Analysis (ICA) is applied on the clustered data, in conjunction with Support Vector Machines (SVM) for brain tissue analysis. Normal and abnormal datasets, consisting of real and synthetic T1-weighted, T2-weighted and proton density/fluid-attenuated inversion recovery images, were used to evaluate the performance of the new method. Comparative analysis with ICA based SVM and other conventional classifiers established the stability and efficiency of SC-ICA based classification, especially in reproduction of small abnormalities. Clinical abnormal case analysis demonstrated it through the highest Tanimoto Index/accuracy values, 0.75/98.8%, observed against ICA based SVM results, 0.17/96.1%, for reproduced lesions. Experimental results recommend the proposed method as a promising approach in clinical and pathological studies of brain diseases