Identification of pore spaces in 3D CT soil images using a PFCM partitional clustering

Recent advances in non-destructive imaging techniques, such as X-ray computed tomography (CT), make it possible to analyse pore space features from the direct visualisation from soil structures. A quantitative characterisation of the three-dimensional solid-pore architecture is important to understand soil mechanics, as they relate to the control of biological, chemical, and physical processes across scales. This analysis technique therefore offers an opportunity to better interpret soil strata, as new and relevant information can be obtained. In this work, we propose an approach to automatically identify the pore structure of a set of 200-2D images that represent slices of an original 3D CT image of a soil sample, which can be accomplished through non-linear enhancement of the pixel grey levels and an image segmentation based on a PFCM (Possibilistic Fuzzy C-Means) algorithm. Once the solids and pore spaces have been identified, the set of 200-2D images is then used to reconstruct an approximation of the soil sample by projecting only the pore spaces. This reconstruction shows the structure of the soil and its pores, which become more bounded, less bounded, or unbounded with changes in depth. If the soil sample image quality is sufficiently favourable in terms of contrast, noise and sharpness, the pore identification is less complicated, and the PFCM clustering algorithm can be used without additional processing; otherwise, images require pre-processing before using this algorithm. Promising results were obtained with four soil samples, the first of which was used to show the algorithm validity and the additional three were used to demonstrate the robustness of our proposal. The methodology we present here can better detect the solid soil and pore spaces on CT images, enabling the generation of better 2D?3D representations of pore structures from segmented 2D images

Bibliometric Analysis of the Mass Transport in a Gas Diffusion Layer in PEM Fuel Cells

The growth trend of publications in the field of Proton Exchange Membrane Fuel Cell (PEMFC) was analyzed using bibliometric techniques to the identification of the areas with significant development and the orientations that have guided the research on energy cells. This study extracted the data from Scopus and Web of Science (WoS) databases to compare the bibliometric indicators of the published productions. In spite of bibliometric analysis advantages to knowing about the trends in a study area, this research requires methods to support the investigation process through the selection of a relevant bibliographic portfolio. This study applied the Methodi Ordinatio that provides a new approach to achieve it. A proposed list of the articles ranked by InOrdinatio is presented to compose the final portfolio. The obtained results in the research sub-theme of the Mass Transport in Gas Diffusion Layer (GDL) confirm the complexity in the study area by presenting erratic patterns of exponential growth. United States, China, and Japan are the leading countries on PEMFC publications. These countries have in common a strong spending by the business sector for R&D, and their gross domestic product is greater than 2%