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Available online at www.sciencedirect.com
journal homepage: www.elsevier.com/locate/he
3D phase-differentiated GDL microstructure generation with
binder and PTFE distributions
Michael M. Daino, Satish G. Kandlikar*
Mechanical Engineering Department, Rochester Institute of Technology, 76 Lomb Memorial Drive, Rochester, NY 14623, USA
article info
abstract
Article history:
In this work, a new framework and model for the digital generation and characterization of
Received 21 September 2011
the microstructure of gas diffusion layer (GDL) materials with localized binder and poly-
Received in revised form
tetrafluoroethylene (PTFE) distributions were developed using 3D morphological imaging
22 November 2011
processing. This new generation technique closely mimics manufacturing processes and
Accepted 8 December 2011
produces complete phase-differentiated (void, fiber, binder, and PTFE) digital 3D micro-
Available online 29 December 2011
structures in a cost- and time-effective manner for the first time. The results for the digital
generation of Toray TGP-H-060 with 5 and 0 wt.% PTFE were in close agreement with
Keywords:
confocal laser scanning microscope (CLSM) images as well as 3D X-ray tomography studies.
PEM fuel cell
The resulting structure can be readily used for analyzing transport processes utilizing
Gas diffusion layer
commercial CFD software.
Stochastic generation
Copyright ª 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights
reserved.
1.
Introduction
Proton exchange membrane fuel cells (PEMFCs) are an
attractive alternative for electrical power generation, particularly for automotive applications due to their high efficiency,
low emissions, quiet operation, quick startup and refueling,
and use hydrogen as the fuel source. Although water is
a byproduct of the reaction and needs to be removed from the
cell, the most widely used membrane (Nafion) must remain
hydrated to sustain protonic conductivity [1]. An overabundance of water in a PEMFC may block reactant pathways
and/or catalytic sites which significantly impacts performance and could potentially lead to cell failure. Maintaining
a sufficient amount of water in the cell without hindering
reactant transport is a critical aspect of PEMFC research that
continues to be widely studied [2e5]. Specifically, the gas
diffusion layer (GDL) strongly affects the water distribution
throughout the cell and must be well characterized in terms of
material properties and geometry for evaluation of its effect
on cell performance and longevity.
Gas diffusion layers in PEMFCs provide several functions
for efficient operation: mechanical support to the membrane,
pathways for reactants and byproduct water, thermally and
electrically conductive, and resistive to intrusion into the gas
distribution channels. The ability of GDL materials to efficiently perform these functions is intrinsically linked to the
microstructure geometry and composition. Gas diffusion
layers commonly used in PEMFCs (e.g. Toray TGP-H) are
comprised of graphitized fibers and carbonized resin and are
usually treated with polytetrafluoroethylene (PTFE) to
increase hydrophobicity prior to in situ use [6]. The distribution of these solid phases as well as their three-dimensional
(3D) geometry within a GDL has been challenging to determine by direct observation with visible light microscopes.
Advanced 3D imaging techniques utilizing x-rays (synchrotron [7,8] or non-synchrotron [9e14] sources) for computed
* Corresponding author. Tel.: þ1 585 475 6728; fax: þ1 585 475 7710.
E-mail addresses: [email protected] (M.M. Daino), [email protected] (S.G. Kandlikar).
0360-3199/$ e see front matter Copyright ª 2011, Hydrogen Energy Publications, LLC. Published by Elsevier Ltd. All rights reserved.
doi:10.1016/j.ijhydene.2011.12.050
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tomography (CT) reconstruction of GDLs have recently been
employed to reveal the 3D microstructure achieving various
spatial resolution and contrast between GDL material and
void. The distributions of carbonized resin, graphitized fibers,
and wet-proofing agent (e.g. PTFE) are highly desired for
transport modeling but the ability to differentiate these phases throughout the GDL remains a considerable challenge.
Additionally, the time required and expense of sophisticated
micro-CT imaging systems is prohibitively substantial.
A relatively new method utilizing the field of stochastic
geometry to predict the internal microstructure of GDL
materials has proven to be a valuable tool to ascertain the 3D
geometry in a cost- and time-effective manner [15].
Stochastic generation has been applied to various porous
materials including coalescers [16], acoustic trim [17], solid
oxide fuel cell electrodes [18], PEMFC GDLs [8,15,19e27], and
PEMFC catalyst layers [19,28,29]. Since the initial application of
stochastic geometry to generate a virtual GDL by Schulz et al.
[15] in 2007, many studies have used the original concept [17]
for pure fiber GDLs [19,21,25,26], fiber with carbonized resin
(referred to as binder) [8,22-24,27], or fiber with PTFE but
without binder [20].
The current work expands upon previous stochastic
generation studies with a novel method utilizing 3D image
processing to incorporate both carbonaceous binder and
the widely used PTFE treatment. To the authors’ knowledge,
this is the first attempt in open literature to include both the
binder and PTFE in virtual GDL generation. Further,
the method of adding the binder is unique in that mimics the
wetting property of the precursor material (thermoset resin)
used in common manufacturing processes. The implementation of the proposed algorithm was developed in
MATLAB using a toolbox organization that allows for extension of the current body of work with minimal overhead cost.
Additionally, a new underlying framework (based on Schladtiz
methodology [17]) is introduced that enables intuitive input
parameters that include fiber radius, GDL thickness, porosity,
percent binder, percent PTFE, and voxel size through a userfriendly graphical user interface. Equipped with the virtual
3D GDL, material characterization and boundary conditions
required for heat and mass transport simulations can be
extracted in a financially and computationally efficient
manner.
The remainder of the paper is organized as follows: Section
2 details the proposed mathematical model for the GDL
microstructure including the model for binder material given
in Section 2.2 and algorithm in Section 2.4. Section 3 describes
the characterization methods used in the iterative generation
algorithm as well as evaluation of generated GDLs. Section 4
reports the results of modeling Toray TGP-H-060 using the
proposed algorithm with a direct comparison to 2D micrographs and 3D X-ray synchrotron data.
2.
Digital GDL generation
Digital GDL materials were simulated using a simplified
mathematical model of intersecting cylinders coupled with 3D
morphological image processing to incorporate the carbonaceous binder and PTFE wetting-proofing treatment. This
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method of adding the binder material was unique because it
directly mimicked the wetting property of the precursor
material (thermoset resin) used in common manufacturing
processes. PTFE treatment was similarly incorporated
utilizing an additional morphological image processing step.
Equipped with the virtual GDL, material characterization,
geometry, and microstructure composition data required for
accurate transport simulations can be extracted in a financially and computationally efficient manner.
2.1.
Mathematical model for graphitized fibers
The graphitized fiber skeleton of GDL materials was modeled
as a random collection of intersecting cylinders (carbonaceous binder will be considered in the following section).
A cylinder oriented along an arbitrary axis was defined as the
collection of all points that are less than one radius from the
axis of the cylinder. The axis of the cylinder was uniquely
^ and a point
defined in Cartesian space by the unit vector C
(x0, y0, z0) it passes through. The closest point on the cylinder
axis to an arbitrary point (x, y, z) in the domain can be
written as:
^ 1 þ y y0 C
^3 C
^ 2 þ ðz z0 ÞC
^
x0 ; y0; z0 þ ðx x0 ÞC
(1)
Thus, an arbitrary point (x, y, z) is contained by the cylinder
^ and (x0, y0, z0) only if the squared distance D
defined by C
between that point and the closest point on the cylinder axis
(defined in Eq. (1)) is less than or equal to the square of the
radius of the cylinder. Using this formulation, any cylinder in
3D Cartesian space is well defined. Randomization of the
collection of digital fibers was implemented with a pseudo^ and
random number generator for both the orientation C
the point (x0, y0, z0) it passes through. The widely used
[8,15,19e27] assumptions for GDL digital generation have been
utilized in this work and can be summarized as:
1. Fibers are straight, cylindrical, and infinitely long.
2. All fibers have the same diameter.
3. Fibers are allowed to intersect.
In this study, highly anisotropic GDL materials were
^ and z0 points
modeled by suppressing the z-component of C
were selected such that the fiber skeleton could be constructed by stacking layers until the desired thickness was
obtained.
2.2.
Binder model
The carbonaceous binder in GDL material is commonly added
as a thermoset resin in the manufacturing process to allow for
flexibility in the final GDL thickness and density [6]. The
thermoset resin acts as a wetting fluid prior to carbonization,
as evident in the confocal laser scanning microscope (CLSM)
images of Toray TGP-H-060 without PTFE in Fig. 1 obtained
with a Keyence VK-9710 Color 3D Laser Scanning Microscope.
The image shows the binder material retained the thermoset
resin’s relatively low static contact angle on the fibers and
generally accumulated at fiber intersections. This property
was mimicked digitally with 3D morphological image
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Fig. 1 e CLSM images of Toray TGP-H-060 0 wt. % PTFE showing carbonized binder morphology.
processing of the fiber skeleton. This new proposed binder
model is representative of common manufacturing techniques and provides a digital representation of the physical
interaction of the binder material with the fibrous substrate.
Morphological closing of the fibrous skeleton digitally adds
material to the substrate in regions of small crevasses near
the intersection of fibers. Utilizing a spherical structuring
element (SE) for the morphological closing retains the wetting
fluid morphology desired of the digital binder. The volume of
binder added to the fibrous skeleton is proportional to the
SE’s radius. To enable intuitive input from the user, a correlation between volume fraction of binder added and SE radius
was computed and is shown in Fig. 2. The binder volume
fraction as a function of SE radius was computed by taking
the ratio of the binder voxels (obtained by subtracting the
initial image from its closing, i.e. a bottom-hat transformation) to the entire binder plus fiber voxels. A least
squares polynomial fit was applied to the data and the
correlation is shown in Fig. 2. This particular correlation was
determined using 1/2 mm voxels, 7 mm fiber diameters, and
a 189 mm thickness was chosen as the closest thickness to the
commercial value of 190 mm that is evenly divisible by the
fiber diameter. Since the binder addition was accomplished
Fig. 2 e Binder correlation for algorithm implementation.
using isotropic 3D morphological image processing, it is only
dependent on the fiber radii voxel thickness and independent
of GDL thickness. Spherical SE radius varied from 0 to 25
resulted in binder volume fractions spanning 0 to about 70%.
The precision of the binder addition is dictated by the voxel
size (this effect will also propagate to the PTFE addition),
which must be a compromise between computational
resources/time and precision required of the target structure.
In the current study, 1/2 mm voxels were found to provide
sufficient control over binder and PTFE additions using
reasonable memory and computational time.
2.3.
PTFE model
Polytetrafluoroethylene is generally added to GDL materials to
increase hydrophobicity and reduce water holdup to provide
more reactant pathways to the catalyst layer. Fig. 3 shows
a CLSM image of Toray TGP-H-060 10 wt.% PTFE. Notice the
very small dark regions that do not appear in the micrographs
of Toray TGP-H-060 shown in Fig. 1. A high resolution CLSM
image of Toray TGP-H-060 40 wt.% PTFE and 0 wt.% PTFE are
shown in Fig. 4 for comparison. The darker regions that are
absent in the images of untreated Toray TGP-H-060 appear to
be comprised of very small (<0.5 mm) particles in Fig. 4(b). This
observation is consistent with PTFE resin particles used in
aqueous dispersions for impregnation porous woven goods
such as Teflon PTFE TE-3836 from DuPont.
In the proposed model, PTFE was digitally added to the
GDL after application of the binder through an additional
morphological closing. The PTFE treatment was accomplished digitally using a closing with a spherical SE of a larger
radius than was used for the binder addition. The volume
fraction of PTFE added to the digital GDL was controlled by
the difference in SE radii between the binder and PTFE
additions. This procedure results in PTFE additions in the
small crevices mimicking the dark regions shown in Figs. 3
and 4(b). This is the first study in open literature to model
both binder and PTFE in a digitally generated GDL. A correlation between change in SE radii from the binder to PTFE
step and PTFE volume fraction was calculated with a similar
method used for the binder correlation described in Section
2.2. The resulting correlation using 30 wt.% binder is shown
in Fig. 5.
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Fig. 3 e CLSM images of Toray TGP-H-060 10 wt.% PTFE. Note the dark regions concentrated in small crevasses not present
in TGP-H-060 without PTFE (Fig. 1).
2.4.
Algorithm
The algorithm used for GDL generation was programmed in
MATLAB and uses the following input parameters:
1. Substrate (fibers and binder) porosity.
2. Fiber radius.
3.
4.
5.
6.
7.
Substrate thickness.
Volume fraction (VF) binder.
Voxel physical size.
Domain size.
VF PTFE.
The digital GDL was generated using the following
algorithm:
1. Initiate the state of the pseudo-random number
generator.
2. Estimate the number of fibers required per layer from
porosity and binder VF inputs.
3. Randomly generate the points for the fibers of two layers
to pass through (x0, y0).
^ 2 for fibers
^1 ; C
4. Randomly generate the fiber orientations C
of two layers.
5. Generate two layers using Eq. (1).
6. Add binder VF using correlation in Fig. 2.
7. Compute porosity.
8. If needed: adjust the number of fibers per layer and iterate
Steps 1 and 3e7 until porosity converges.
9. Generate all layers.
10. Add PTFE VF using correlation in Fig. 5.
Fig. 6 shows a block diagram of the proposed algorithm to
create digital GDLs with binder and PTFE distributions. The
overall functionally of the generation algorithm was implemented through a graphical user interface.
Fig. 4 e CLSM images of Toray TGP-H-060 with (a) 0 wt.%
PTFE and (b) 40 wt.% PTFE. Note the accumulation of small
(<0.5 mm) dark particles present in (b) that could be
attributed to the addition of PTFE.
3.
Microstructure characterization
3.1.
Global porosity
The fiber and binder volume fractions were considered in the
calculation of global porosity during the generation algorithm
outlined in Section 2.4 and used for comparison to manufacturer data. After the completed GDL structure was generated
(with or without PTFE), the global porosity was computed
again for material characterization and comparison to
manufacturer data. The generated microstructure is a threedimensional phase-differentiated image, which facilitates
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MATLAB workspace. This GUI allows for simple operation of
the overall algorithm without the need to call individual
functions.
4.
Fig. 5 e Correlation between PTFE volume fraction and
difference in SE radii for 30 wt.% binder.
global porosity computation. The global porosity was
computed by searching for and counting the number of void
voxels and taking their ratio with the total number of voxels in
the structure. This computation was used in two ways in the
generation algorithm. First, it was used when generating the
initial two layers for convergence on the number of fibers
needed for each layer. After the entire microstructure was
generated, the global porosity was computed again for the
resulting porosity of the virtual GDL.
3.2.
Results and discussion
A digital GDL with binder and PTFE was generated using the
algorithm detailed in Section 2.4, and a single layer of the
GDL was extracted from the 3D structure to exemplify each
step of the generation process. The initial step of generating
the fibrous skeleton of the GDL is shown in Fig. 7(a) where
the fibers were arbitrarily colored green (for interpretation of
the references to color in this figure, the reader is referred to
the web version of this article). The crimp of the fibers was
assumed negligible and this assumption can clearly be
demonstrated in Fig. 7(a). The second step in generating the
digital GDL was to add the carbonaceous binder used in the
manufacturing process. The result of the method described
in Section 2.2 on the single layer of GDL is shown in Fig. 7(b)
where the binder material is colored orange. Notice from the
image that the added binder material behaved as a wetting
fluid as discussed in Section 2.2. The last step in the generation algorithm was to add PTFE to the digital GDL; the result
is shown in Fig. 7(c) where the PTFE is colored cyan. Note
that the PTFE in this processing step also filled in small
crevasses and somewhat reduced the porosity of the overall
sample.
Local porosity
Local porosity variations throughout the GDL have a strong
influence on local transport phenomena and need to be well
characterized for modeling efforts. Determination of local
porosity has been conventionally unattainable using porosimetry techniques for global porosity measurements (such as
mercury intrusion) but has recently been explored using
advanced imaging techniques [14]. Utilizing the threedimensional nature of the GDLs generated in this work, local
variations with arbitrary resolution are implemented in
a similar manner as global porosity with a sliding computation domain.
3.3.
Graphical user interface layout
The digital generation algorithm was implemented through
the use of a user-friendly graphical user interface (GUI)
programmed in MATLAB. The GUI allows the user to visually
examine the generated structure from the three perpendicular two-dimensional views in steps of one voxel. The displayed colors of each solid phase can be adjusted to the
user’s preference using a color map input. The converged
porosity, binder and PTFE volume fractions are also displayed on the GUI. The resulting structure can be saved as
a multi-paged TIFF and/or stored as a 3D array in the main
Fig. 6 e Block diagram outlining processing steps of the
algorithm.
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Fig. 8 e Three-dimensional view of simulated Toray TGPH-060 5 wt.% PTFE. Fibers, binder, and PTFE are shown as
green, orange, and cyan, respectively. (For interpretation of
the references to color in this figure legend, the reader is
referred to the web version of this article.)
solid phase distribution of commercial GDLs for transport
modeling. Toray TGP-H-060 was chosen as a commonly used
and commercially available GDL to test the generation algorithm. Toray TGP-H-060 has 7 mm fiber diameters, thickness of
190 mm, porosity of 78% [30], and is composed of about 27%
solid fraction carbonaceous binder. These values were used as
input parameters in the generation algorithm combined with
a Dr ¼ 1 for the SE radii for the digital PTFE treatment. Fig. 8
shows the three-dimensional view of the generated structure with 1/2 mm voxels (500 500 189 mm) with fibers,
binder, and PTFE colored green, orange, and cyan, respectively. The resulting porosity of the GDL without PTFE
Fig. 7 e Single layer of a digitally generated GDL at the (a)
fiber skeleton, (b) binder addition, and (c) PTFE addition
steps. Fibers, binder, and PTFE are shown in green, orange,
and cyan, respectively. (For interpretation of the references
to color in this figure legend, the reader is referred to the
web version of this article.)
4.1.
Toray TGP-H-060 generation
The motivation for the digital generation of the 3D microstructure of GDL materials was to obtain the 3D geometry and
Fig. 9 e In-plane view of simulated Toray TGP-H-060
5 wt.% PTFE with color intensity linearly decreasing as
a function of depth. Fibers, binder, and PTFE are shown as
green, orange, and cyan, respectively. (For interpretation of
the references to color in this figure legend, the reader is
referred to the web version of this article.)
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Fig. 11 e Through-plane 1/2 mm section of generated Toray
TGP-H-060 5 wt.% PTFE extracted from the central region of
the simulated GDL. Note the expected interconnected pore
structure and binder interaction with the fibrous skeleton.
Void, fiber, binder, and PTFE are shown as black, green,
orange, and cyan, respectively. (For interpretation of the
references to color in this figure legend, the reader is
referred to the web version of this article.)
Fig. 10 e Comparison of (a) actual Toray TGP-H-060 0 wt.%
PTFE to (b) digital generation using proposed algorithm. The
micrograph in (a) is a CLSM laser intensity image of Toray
TGP-H-060 0 wt.% PTFE and (b) is an in-plane view of the
generated GDL with depth into the GDL shown as grayscale.
treatment was determined to be 81% with 30% volume fraction of binder, which are in good agreement with manufacturer data.
The generated structure is shown from the top view in
Fig. 9 with depth, up to a maximum of 50 mm into the GDL,
represented as a linear decrease in color intensity. A direct
comparison of the generated GDL is made in Fig. 10 where
a CLSM image of Toray TGP-H-060 0 wt.% PTFE is shown in
Fig. 10(a) and a grayscale image of the generated GDL (PTFE
removed) is shown in Fig. 10(b). The grayscale in Fig. 10(b)
represents depth into the GDL to a maximum depth of 50 mm.
Note the similarities between the images and how the model
for the digital binder closely mimics actual Toray TGP-H-060
GDL.
The generated 3D phase-differentiated microstructure
allows for material characterization and evaluation of any part
or whole that may be otherwise unavailable. A 1/2 mm slice
from the central region of the generated microstructure is
shown in Fig. 11 and reveals the distribution of the fibers,
binder, and PTFE in the through-plane direction. The phases of
the generated GDL are shown as black, green, orange, and cyan
for void, fiber, binder, and PTFE, respectively (for interpretation of the references to color in this figure, the reader is
referred to the web version of this article). Notice from Fig. 11
that the pore space is very interconnected and the binder
material accumulated at fiber intersections reinforcing the
fiber skeleton. The local porosity of this thin (1/2 mm) deviates
somewhat from the global porosity with a value of 78% void
compared to the global structure of 81% and would have an
effect on local transport properties. These small changes in the
local properties were found to be fairly sensitive to the volume
and location of the extracted section. Fig. 12 shows a central
slice of the generated structure 25 mm from the section shown
in Fig. 11. The local porosity of this section is about the same as
the previous section (79% vs. 78%) but the physical distributions of the phases and associated transport properties are
noticeably different. These local changes in the distribution of
the phases in relation to the reactant channels and lands in an
actual PEMFC could have a strong effect on the local current
density, water saturation, and temperature distribution and
should be carefully considered in modeling efforts.
Fig. 12 e Through-plane 1/2 mm section of generated Toray
TGP-H-060 5 wt.% PTFE extracted from the central region of
the simulated GDL 25 mm from the cross-section shown in
Fig. 11. Note the significant difference in the local
distribution of fibers, binder, and PTFE compared to Fig. 11.
Void, fiber, binder, and PTFE are shown as black, green,
orange, and cyan, respectively. (For interpretation of the
references to color in this figure legend, the reader is
referred to the web version of this article.)
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4.2.
Local porosity variation
Local porosity variations can have a strong effect on local
transport properties and thus influence local current density
and performance. To further evaluate the generated GDL, the
local porosity distribution was calculated as described in
Section 3.2 using a 25 25 189 mm averaging volume. The
result is shown in Fig. 13 with the color scale representative of
porosity and each square represents 625 mm2 in area in the inplane direction through the entire GDL thickness. Note from
the figure that porosity has significant variation (shown in
a color spectrum from blue representing the lowest to red
representing the highest porosity, for interpretation of the
references to color in this figure, the reader is referred to the
web version of this article) at this scale. Local porosity variation
on the order of channel separation (about 1 mm) in actual
PEMFCs could have a strong influence on local performance
depending on channel and land configuration directly above
(or below) the GDL microstructure. Regions of high porosity
(e.g. see lower right of Fig. 13) may have a negative impact on
local performance in the presence of a land. Higher porosity in
the through-plane direction implies less fiber-to-fiber interaction and binder material and thus would result in lower thermal
and electrical conductivity with the latter decreasing performance. Additionally, if the surface of the GDL also has high
porosity, the contact resistance would be greater due to lack of
GDL material to make intimate contact with the lands. These
localized regions of high porosity may also provide regions for
water to accumulate increasing water saturation and reducing
available reactant flow paths. Thus, it is clear that these local
variations have a complex effect on the overall performance
and should be carefully considered for improved modeling
efforts over the assumption of homogeneous porosity.
4.3.
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size and can be used to directly compare the generated GDL
with PTFE removed [8]. Since the generated GDL is a phaseindexed (or equivalently, phase-differentiated) 3D image,
only a simple logic step is required to remove the PTFE for
direct comparison. A 3D view of actual Toray TGP-H-060
without PTFE is shown in Fig. 14(a) for comparison to a 3D
view of the simulated Toray TGP-H-060 0 wt.% PTFE shown in
Fig. 14(b). Note from these images that the generation algorithm performed fairly well generating a realistic digital GDL.
The addition of binder in the generation algorithm was an
attempt to mimic manufacturing techniques and the result
was in agreement with the top view CLSM images in Fig. 10.
Further comparison of the through-plane images with the
binder’s interaction with the fiber skeleton can be made from
the results of Becker et al. [8]. Fig. 15(a) shows a cross-sectional
slice of the actual Toray TGP-H-060 0 wt.% PTFE obtained
through X-ray tomography by Becker et al. [8] and Fig. 15(b)
shows a similar cross-section from the generated model of
Toray TGP-H-060 0 wt.% PTFE. Although the images in Fig. 15
are not identical, they contain regions with qualitatively
similar features and have been circled for comparison.
Localized regions with significant binder material appear in
both images and are outlined by the small dashed circles. The
Comparison to actual GDL
The actual 3D structure of Toray TGP-H-060 without PTFE was
obtained by Becker et al. using synchrotron-based phase
contrast X-ray tomographic microscopy with a 0.74 mm pixel
Fig. 13 e Local porosity map of simulated Toray TGP-H-060
5 wt.% PTFE averaged in the through-plane direction
(189 mm) using 25 3 25 mm bins in the in-plane direction.
Fig. 14 e Comparison of (a) actual 3D image of Toray TGPH-060 0 wt.% PTFE [8] to (b) generation algorithm. Note (a)
was reprinted with permission from Ref. [8]. Copyright
2009, The Electrochemical Society.
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0, 1, 2, and 3, respectively. This phase-differentiated image
could be written to the MATLAB workspace as a 3D array or as
a multi-page TIFF image saved to the local disk. The 3D
geometry can then be used in commercial CFD software
packages for transport simulation studies. The generated GDL
materials were also characterized by the local porosity variations throughout the structure through an additional function
of the MATLAB toolbox. The local porosity function allowed
for examination at an arbitrary user-defined scale throughout
the generated GDL structure. The generation algorithm was in
agreement with CLSM images and X-ray characterization of
commercial GDL (Toray TGP-H-060) using input parameters
from manufacturer data. This generation algorithm will
provide 3D geometries of commercial or hypothetical GDL
materials in a cost- and time-effective manner for modeling
efforts.
Acknowledgements
Fig. 15 e Through-plane phase distribution comparison of
(a) actual Toray TGP-H-060 0 wt.% PTFE [8] to (b) generation
algorithm. Solid phases (fiber and binder) are shown as
a lighter gray than void in both images. Note the similar
features in the circled regions indicating the binder model
closely simulates actual Toray TGP-H-060. Note (a) was
reprinted with permission from Ref. [8]. Copyright 2009,
The Electrochemical Society.
images in Fig. 15 also reveal fiber cross-sections without
binder material further indicating that binder accumulated
mostly near high fiber density locations. In addition, crosssections of fibers severed by quarrying the 3D datasets can
be seen in the solid circles in both images with minimal
interaction with neighboring layers. These qualitative observations on the quantity and interaction of binder material
with the fibrous skeleton in these images are in close agreement indicating the model of the binder represents actual GDL
materials reasonable well.
5.
Conclusions
A numerical simulation tool was developed to provide 3D
geometry of GDL materials with localized binder and PTFE
distributions. This tool was developed using a new model and
framework for the digital generation of GDL materials and was
successful in producing realistic GDL geometries using 3D
morphological image processing. The 3D morphological processing mimicked manufacturing processes of the addition of
binder material and subsequent PTFE treatments. The overall
generation algorithm enables intuitive user inputs of the
desired GDL parameters (e.g. fiber radius, porosity, etc.)
through a GUI developed in MATLAB. The outputs from the
generation algorithm were the global porosity, volume fraction of binder, volume fraction of PTFE, and a 3D phasedifferentiated image. The output 3D image was an indexed
image where void, fiber, binder, and PTFE were represented by
Support for this project was provided by the US Department of
Energy under award numbers: DE-FG3607GO17018 & DEEE0000470.
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