Caracterização micro-estrutural de cerâmicas supercondutoras do tipo SmBaCuO através do software Imagej

• Autor(a) principal: Garcia, Caio Barbosa
• Data de Publicação: 2017
• Tipo de documento: Dissertação
• Idioma: por
• Título da fonte: Repositório Institucional da Universidade Federal do Espírito Santo (riUfes)
• Texto Completo: http://repositorio.ufes.br/handle/10/9758
• topic: Superconductivity; Superconductive ceramics; Josephson junctions; Digital image processing; Microstructural analysis; SmBaCuO; ImageJ (Programa de computador); Processamento digital de imagens; Engenharia Mecânica; Supercondutividade de alta temperatura; Supercondutores cerâmicos; Processamento de imagens - Técnicas digitais; Microestrutura - Análise; Josephson, Junções; 621

In the last 20 years, the high-temperature critical superconductivity has been highlighted by its application potential and the difficult understanding, since there is no general theory about a superconductivity that explain its properties. The macroscopic and transport properties of materials can be justified by their microstructures. Thus, knowing them quantitatively and qualitatively becomes a powerful material analysis tool. High critical temperature ceramic superconductors are characterized microstructurally by the arrangement between their grains. The grains are interpenetrating themselves, with Josephson microjunctions (weak-links) between them. Studies have pointed that weak-links (therefore grain size) directly influence high-temperature superconductors properties, such as the critical current and magnetic shielding. Due to the difficulty of measuring the weak-link points, we work with statistical treatments of grain contours, being the digital image processing a powerful tool for this purpose. This, allows us to quantify the grains from a digital image of them. In this work, SEM images (scanning electron microscopy) were used for a microstructural analysis of the set of grains that form to forms the superconductors belonging to the system SmBaCuO studied by the UFES Applied Physics group. The images were processed by ImageJ software, and the statistical analysis by the ActionStat. The ImageJ allowed treating an image by removing its noise and highlighting regions of interest (grains and pores). The ActionStat presented a statistical analysis to the data collected by ImageJ. As a result we have a measure of the porosity, a count of the grains correlated with their statistical diameters (Feret's diameter) and a curve fitting for the diameters frequency histogram. For the curve fitting, the A and D samples showed a better distribution to the Gamma curve. Nevertheless, the B and D samples presented better fitting to Weibull curve. The C sample had satisfactory results to both distributions