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The 3-dimensional spatial distribution of liquid water in different gas diffusion layer (GDL) materials was analyzed using synchrotron X-ray tomography. The capability of the method was demonstrated by virtually separating the GDL components in order to facilitate individual analysis of fiber material, liquid water and gas filled pore spaces. The influence of hydrophobic surface treatment on the water distribution in the GDL was illustrated by analyzing three GDL materials with different degrees of hydrophobicity. In the least hydrophobic sample, liquid water tends to form larger clusters which stretch out about several hundred µm inside the porous GDL. In contrast, only small water clusters were found in the strongly hydrophobic material with high Polytetrafluoroethylene (PTFE)-content as the liquid is partially pressed out of the GDL. Additionally, the influence of fiber orientation on the water distribution in the felt material was demonstrated.
We present a synchrotron X-ray tomographic study on the morphology of carbon fiber-based gas diffusion layer (GDL) material under compression. A dedicated compression device is used to provide well-defined compression conditions. A flat compression punch is employed to study the fiber geometry at different degrees of compression. Transport relevant geometrical parameters such as porosity, pore size and tortuosity distributions are calculated. The geometric properties notably change upon compression which has direct impact on transport conditions for gas and fluid flow. The availability of broad 3D paths, which are most important for the transport of liquid water from the catalyst layer through the GDL, is markedly reduced after compression. In a second experiment, we study the influence of the channel-land-pattern of the flow-field on shape and microstructure of the GDL. A flow-field compression punch is employed to reproduce the inhomogeneous compression conditions found during fuel cell assembly. While homogenously compressed underneath the land the GDL is much less and inhomogeneously compressed under the channel. The GDL material extends far into the channel volume where it can considerably influence gas and fluid flow. Loose fiber endings penetrate deeply into the channel and form obstacles for the discharge of liquid water droplets.
Clarifying the Electro-Chemo-Mechanical Coupling in Li10SnP2S12 based All-Solid-State Batteries
(2022)
A fundamental clarification of the electro-chemo-mechanical coupling at the solid–solid electrode|electrolyte interface in all-solid-state batteries (ASSBs) is of crucial significance but has proven challenging. Herein, (synchrotron) X-ray tomography, electrochemical impedance spectroscopy (EIS), time-of-flight secondary-ion mass spectrometry (TOF-SIMS), and finite element analysis (FEA) modeling are jointly used to decouple the electro-chemo-mechanical coupling in Li10SnP2S12-based ASSBs. Non-destructive (synchrotron) X-ray tomography results visually disclose unexpected mechanical deformation of the solid electrolyte and electrode as well as an unanticipated evolving behavior of the (electro)chemically generated interphase. The EIS and TOFSIMS probing results provide additional information that links the interphase/electrode properties to the overall battery performance. The modeling results complete the picture by providing the detailed distribution of the mechanical stress/strain and the potential/ionic flux within the electrolyte.
Collectively, these results suggest that 1) the interfacial volume changes induced by the (electro)chemical reactions can trigger the mechanical deformation of the solid electrode and electrolyte; 2) the overall electrochemical process can accelerate the interfacial chemical reactions; 3) the reconfigured interfaces in turn influence the electric potential distribution as well as charge transportation within the SE. These fundamental discoveries that remain unreported until now significantly improve the understanding of the complicated
electro-chemo-mechanical couplings in ASSBs.
The 3D microstructure of an Al alloy matrix composite with two ceramic reinforcements was investigated by synchrotron X-ray tomography. A deep learning algorithm was used for the segmentation of four different phases. We show that convolutional networks with the U-Net architecture are able to solve complex segmentation tasks with small amount of training data.