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Multiscale Tomography-Based Analysis of Polymer Electrolyte Fuel Cells: Towards a Fully Resolved Gas Diffusion Electrode Reconstruction

[+] Author and Article Information
Matthias Klingele, Riko Moroni

IMTEK,
University of Freiburg,
Georges-Koehler-Allee 103,
Freiburg 79110, Germany

Severin Vierrath

IMTEK,
University of Freiburg,
Georges-Koehler-Allee 103,
Freiburg 79110, Germany;
Hahn-Schickard,
Georges-Koehler-Allee 103,
Freiburg 79110, Germany

Simon Thiele

IMTEK,
University of Freiburg,
Georges-Koehler-Allee 103,
Freiburg 79110, Germany;
Hahn-Schickard,
Georges-Koehler-Allee 103,
Freiburg 79110, Germany;
FIT,
University of Freiburg,
Georges-Koehler-Allee 105,
Freiburg 79110, Germany

1Corresponding author.

Manuscript received May 11, 2017; final manuscript received June 29, 2017; published online September 19, 2017. Assoc. Editor: Dirk Henkensmeier.

J. Electrochem. En. Conv. Stor. 15(1), 014701 (Sep 19, 2017) (7 pages) Paper No: JEECS-17-1045; doi: 10.1115/1.4037244 History: Received May 11, 2017; Revised June 29, 2017

The microstructure of a fuel cell electrode largely determines the performance of the whole fuel cell system. In this regard, tomographic imaging is a valuable tool for the understanding and control of the electrode morphology. The distribution of pore- and feature-sizes within fuel cell electrodes covers several orders of magnitude, ranging from millimeters in the gas diffusion layer (GDL) down to few nanometers in the catalyst layer. This obligates the application of various tomographic methods for imaging every aspect of a fuel cell. This perspective evaluates the capabilities, limits, and challenges of each of these methods. Further, it highlights and suggests efforts toward the integration of multiple tomographic methods into single multiscale datasets, a venture which aims at large-scale, and morphologically fully resolved fuel cell reconstructions.

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Figures

Grahic Jump Location
Fig. 1

Cartoon (top) and real imaged (bottom) morphology of a fuel cell. It is shown that morphological features range from dimensions of several micrometers down to few nanometers. Different methods are thus needed to image these features in varying resolutions and field of views. In this figure, this is X-ray tomography (a) and (b), FIB–SEM tomography (c), and TEM tomography (d).

Grahic Jump Location
Fig. 2

(b) Global X-ray dataset of a gas diffusion electrode. Fibers, pore space within the fibers, microporous layer (MPL), and catalyst layer (CL) can be discriminated. The microporosity of the microporous layer and the catalyst layer cannot be resolved by X-ray, but by FIB–SEM tomography, as depicted in (c) and (a), respectively. Calculated parameters from the FIB–SEM reconstruction can be inscribed into the respective areas of the X-ray dataset. From this, multiscale tomography-based analysis can be performed for the whole gas diffusion electrode.

Grahic Jump Location
Fig. 3

Integral and spatially resolved multiscale particle- and pore-size analysis of the global gas diffusion electrode reconstruction. The region starting at approximately 150 μm in depth can be assigned to the microporous layer and catalyst layer. Information within this region is obtained by FIB–SEM tomography, whereas all other information is obtained from X-ray tomography. By this, morphological features ranging from several nanometers up to micrometers can be covered within one dataset (MGS: mean grain size, GVF: grain (or particle) volume fraction, MPS: mean pore size, and PVF: pore volume fraction).

Grahic Jump Location
Fig. 4

Two methods for adding the microporous carbon matrix to the microporous layer identified by X-ray. In (a), a microporous FIB–SEM reconstruction of the microporous layer is directly inscribed by stringing together the reconstructed volume. In (b), the microporosity is added to the microporous layer by stochastically placing spheres, so that the whole layer matches the porosity and tortuosity as well as the pore- and grain-size distribution of the FIB–SEM reconstruction.

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