{"id":14,"date":"2017-11-30T15:51:04","date_gmt":"2017-11-30T15:51:04","guid":{"rendered":"http:\/\/micdigi.deqm.puc-rio.br\/?page_id=14"},"modified":"2020-07-04T13:44:36","modified_gmt":"2020-07-04T16:44:36","slug":"linhas-de-pesquisa","status":"publish","type":"page","link":"https:\/\/micdigi.deqm.puc-rio.br\/en\/linhas-de-pesquisa\/","title":{"rendered":"Research lines"},"content":{"rendered":"
<\/div>

<\/i><\/i><\/span>Automation in Optical Microscopy<\/span><\/a><\/h4><\/div>
\n

Development of methodologies for complete automation of experiments with optical microscopy. Mosaic scanning, extended focus and topography, dynamic experiments, automatic control of image modes (bright field, circular polarization, among others). Automated image analysis and reporting.<\/span><\/p>\n<\/div><\/div><\/div>

<\/i><\/i><\/span>X-ray microtomography<\/span><\/a><\/h4><\/div>
\n

3D characterization of different materials - metals, composites, rocks, natural materials. Evaluation of the external surface and internal defects with up to 0.7 \u00b5m of resolution. Quantitative 3D image analysis.<\/span><\/p>\n<\/div><\/div><\/div>

<\/i><\/i><\/span>Characterization of materials for the mining and metallurgical industry<\/span><\/a><\/h4><\/div>
\n

2D and 3D characterization of ores. 2D and 3D porosity analysis in iron ore pellets. Mineralogical and textural analysis of iron oxides. Analysis of internal defects (pores, cracks and inclusions) in welds. Application of artificial intelligence in the classification of materials.<\/span><\/p>\n<\/div><\/div><\/div>

<\/i><\/i><\/span>Characterization of materials for the oil and gas industry<\/span><\/a><\/h4><\/div>
\n

3D analysis of porosity and defects in rocks. 3D analysis of flow and oil recovery. Automatic catalyst morphometry. In situ mechanical tests coupled to X-ray microtomography.<\/span><\/p>\n<\/div><\/div><\/div><\/div><\/div>

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