Infrared spectroscopy of Mg-doped SrRuO3 thin films /

The reflectance of thin films of magnesium doped SrRu03(Mg-SR0) produced by pulsed laser deposition on SrTiOa (100) substrates has been measured at room temperature between 100 and 7500 cm~^. The films were chosen to have wide range of thickness, stoichiometry and electrical properties. As the films were very thin (less than 300 nm), and some were insulating the reflectance data shows structures due to both the film and the substrate. Hence, the data was analyzed using Kramers-Kronig constrained variational fitting (VDF) method to extract the real optical conductivity of the Mg-SRO films. Although the VDF technique is flexible enough to fit all features of the reflectance spectra, it seems that VDF could not eliminate the substrate's contribution from fllm conductivity results. Also the comparison of the two different programs implementing VDF fltting shows that this technique has a uniqueness problem. The optical properties are discussed in light of the measured structural and transport properties of the fllms which vary with preparation conditions and can be correlated with differences in stoichiometry. This investigation was aimed at checking the VDF technique and also getting answer to the question whether Mg^"*" substitutes in to Ru or Sr site. Analysis of our data suggests that Mg^+ goes to Ru site.

Far infrared spectroscopy of heavy fermion superconductor CeCoIn5

The optical response to far infrared radiation has been measured on a mosaic of heavy fermion CeColnssingle crystals. The superconducting transition temperature of the crystals has been determined by van der Pauw resistivity and ac-susceptibility measurements as Tc = 2.3 K. The optical measurements were taken above and below the transition temperature using a 3He cryostat and step and integrate Martin-Puplett type polarizing interferometer. The absolute reflectance of the heavy fermion CeColns in the superconducting state in range (0, 100)cm-1 was calculated from the measured thermal reflectance, using the normal state data of Singley et al and a low frequency extrapolation for a metallic material in the Hagen-Rubens regime. By means of Kramers-Kronig analysis the absolute reflectance was used to calculate the optical conductivity of the sample. The real part of the calculated complex conductivity 0-(w) ofCeColns indicates a possible opening of an energy gap close to 50 em-I.

Far infrared spectroscopy of heavy fermion superconductor CeCoIn5 /

The optical response to far infrared radiation has been measured on a mosaic of heavy fermion CeCoIns single crystals. The superconducting transition temperature of the crystals has been determined by van der Pauw resistivity and ac-susceptibility measurements as Tc = 2.3 K. The optical measurements were taken above and below the transition temperature using a ^He cryostat and step and integrate Martin-Puplett type polarizing interferometer. The absolute reflectance of the heavy fermion CeCoIns in the superconducting state in range (0, 100)cm~^ was calculated from the measured thermal reflectance, using the normal state data of Singley et al and a low frequency extrapolation for a metallic material in the Hagen-Rubens regime. By means of Kramers-Kronig analysis the absolute reflectance was used to calculate the optical conductivity of the sample. The real part of the calculated complex conductivity a{u)) of CeCoIns indicates a possible opening of an energy gap close to 50 cm~^.

Infrared Spectroscopy of Gadolinium

Measurements of the optical reflectivity of the normal incident light along c-axis [0001] have been made on a Gadolinium single crystal, for temperatures between 50 K and room temperature just above the Curie temperature of Gd, which is 293 K. And covering the spectrum range between 100 -11000 cm-I . This work is the first study of Gd in the far infrared range. In fact it fills the gap below 0.2 eV which has never been measured before. Extreme attention was paid to the fact that Gadolinium is a very reactive metal with air. Thus, the sample was mechanically polished and carefully handled during the measurement. However, temperature dependent optical measurements have been made in the same frequency range for a sample of Gd2O3. For comparison, both samples of Gd and Gd2O3 were examined by X-Ray diffraction. XRD analysis showed that the sample was pure gadolinium and the oxide layer either does not exist, or is very thin. Furthermore, this fact was supported by the absence of any of Gd2O3 features in the Gd sample reflectivity. Kramers Kronig analysis was applied to extract the optical functions from the reflectance data. The optical conductivity shows a strong temperature dependence feature in the mid-infrared. This feature disappears completely at room temperature which supports a magnetic origin.

Hyperspectral Mineral Identification using SVM and SOM

Remote sensing techniques involving hyperspectral imagery have applications in a number of sciences that study some aspects of the surface of the planet. The analysis of hyperspectral images is complex because of the large amount of information involved and the noise within that data. Investigating images with regard to identify minerals, rocks, vegetation and other materials is an application of hyperspectral remote sensing in the earth sciences. This thesis evaluates the performance of two classification and clustering techniques on hyperspectral images for mineral identification. Support Vector Machines (SVM) and Self-Organizing Maps (SOM) are applied as classification and clustering techniques, respectively. Principal Component Analysis (PCA) is used to prepare the data to be analyzed. The purpose of using PCA is to reduce the amount of data that needs to be processed by identifying the most important components within the data. A well-studied dataset from Cuprite, Nevada and a dataset of more complex data from Baffin Island were used to assess the performance of these techniques. The main goal of this research study is to evaluate the advantage of training a classifier based on a small amount of data compared to an unsupervised method. Determining the effect of feature extraction on the accuracy of the clustering and classification method is another goal of this research. This thesis concludes that using PCA increases the learning accuracy, and especially so in classification. SVM classifies Cuprite data with a high precision and the SOM challenges SVM on datasets with high level of noise (like Baffin Island).