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- Synchrotron Tomography (3) (entfernen)
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Carbon fiber based felt materials are widely used as gas diffusion layer (GDL) in fuel cells. Their transport properties can be adjusted by adding hydrophobic agents such as polytetrafluoroethylene (PTFE). We present a synchrotron X-ray tomographic study on the felt material Freudenberg H2315 with different PTFE finishing. In this study, we analyze changes in microstructure and shape of GDLs at increasing degree of compression which are related to their specific PTFE load. A dedicated compression device mimicking the channel-land pattern of the flowfield is used to reproduce the inhomogeneous compression found in a fuel cell. Transport relevant geometrical parameters such as porosity, pore size distribution and geometric tortuosity are calculated and consequences for media transport discussed. PTFE finishing results in a marked change of shape of compressed GDLs: surface is smoothed and the invasion of GDL fibers into the flow field channel strongly mitigated. Furthermore, the PTFE impacts the microstructure of the compressed GDL. The number of available wide transport paths is significantly increased as compared to the untreated material. These changes improve the transport capacity liquid water through the GDL and promote the discharge of liquid water droplets from the cell.
The 5G technology promises real time data transmission for industrial processes, autonomous driving, virtual and augmented reality, E-health applications and many more. The Low Temperature Co-fired Ceramics (LTCC) technology is well suited for the manufacturing of microelectronic components for such applications. Still, improvement of the technology such as further miniaturization is required. This study focuses on the characterization of inner metallization of LTCC multilayer modules, especially on the vertical interconnect access (VIA). Critical considerations for this characterization are delamination, pore clustering in and at the edge of the VIA, deformation, and stacking offset. A LTCC multilayer consisting of a glassy crystalline matrix with silver based VIAs was investigated by synchrotron x-ray tomography (CT). The aim of this study is to propose a multitude of structural characteristic values to maximize the information gained from the available dataset. Data analysis has been done with the open source software ImageJ as well as several additional plugins. The high-resolution CT data was evaluated through 2D slices for accessibility reasons. The segmentation of all 2000 slices to assess the different regions e.g. pores, silver and glass ceramic was done by a supervised machine learning algorithm. A quantitative evaluation of shape, deformation, and porosity of the VIA with respect to its dimensions is presented and the suitability of the characterization approach is assessed.
The 5G technology promises real time data transmission for industrial processes, autonomous driving, virtual and augmented reality, E-health applications and many more. The Low Temperature Co-fired Ceramics (LTCC) technology is well suited for the manufacturing of microelectronic components for such applications. Still, improvement of the technology such as further miniaturization is required. This study focuses on the characterization of inner metallization of LTCC multilayer modules, especially on the vertical interconnect access (VIA). Critical considerations for this characterization are delamination, pore clustering in and at the edge of the VIA, deformation, and stacking offset. A LTCC multilayer consisting of a glassy crystalline matrix with silver based VIAs was investigated by synchrotron x-ray tomography (CT). The aim of this study is to propose a multitude of structural characteristic values to maximize the information gained from the available dataset. Data analysis has been done with the open source software ImageJ as well as several additional plugins. The high-resolution CT data was evaluated through 2D slices for accessibility reasons. The segmentation of all 2000 slices to assess the different regions e.g. pores, silver and glass ceramic was done by a supervised machine learning algorithm. A quantitative evaluation of shape, deformation, and porosity of the VIA with respect to its dimensions is presented and the suitability of the characterization approach is assessed.