<resource xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xmlns="http://datacite.org/schema/kernel-4" xsi:schemaLocation="http://datacite.org/schema/kernel-4 http://schema.datacite.org/meta/kernel-4.1/metadata.xsd"><identifier identifierType="DOI">10.26165/JUELICH-DATA/RCL4O0</identifier><creators><creator><creatorName nameType="Personal">Froning, Dieter</creatorName><givenName>Dieter</givenName><familyName>Froning</familyName><nameIdentifier nameIdentifierScheme="ORCID">0000-0003-2264-407X</nameIdentifier><affiliation>Forschungszentrum Jülich GmbH, IET-4: Electrochemical Process Engineering</affiliation></creator></creators><titles><title>Stochastic representations of fiber-based gas diffusion layers</title></titles><publisher>Jülich DATA</publisher><publicationYear>2024</publicationYear><subjects><subject>Engineering</subject><subject>Fuel cells; GDL; stochastic model; micro-structure; images</subject></subjects><contributors><contributor contributorType="ContactPerson"><contributorName nameType="Personal">Froning, Dieter</contributorName><givenName>Dieter</givenName><familyName>Froning</familyName><affiliation>Forschungszentrum Jülich GmbH, IET-4: Electrochemical Process Engineering</affiliation></contributor></contributors><dates><date dateType="Submitted">2024-11-05</date><date dateType="Updated">2024-12-13</date></dates><resourceType resourceTypeGeneral="Dataset">micro-structure</resourceType><relatedIdentifiers><relatedIdentifier relationType="IsCitedBy" relatedIdentifierType="DOI">10.1016/j.electacta.2013.04.071</relatedIdentifier><relatedIdentifier relationType="IsCitedBy" relatedIdentifierType="DOI">10.1149/1.2839570</relatedIdentifier><relatedIdentifier relationType="IsCitedBy" relatedIdentifierType="DOI">10.1007/s11242-014-0312-9</relatedIdentifier><relatedIdentifier relationType="IsCitedBy" relatedIdentifierType="DOI">10.3390/app8122536</relatedIdentifier><relatedIdentifier relationType="IsCitedBy" relatedIdentifierType="DOI">10.1007/s11242-018-1048-8</relatedIdentifier><relatedIdentifier relationType="IsCitedBy" relatedIdentifierType="DOI">10.1002/cite.201800158</relatedIdentifier><relatedIdentifier relationType="IsCitedBy" relatedIdentifierType="DOI">10.1016/j.ijhydene.2018.01.168</relatedIdentifier><relatedIdentifier relationType="IsCitedBy" relatedIdentifierType="DOI">10.1016/j.jpowsour.2018.04.004</relatedIdentifier><relatedIdentifier relationType="IsCitedBy" relatedIdentifierType="DOI">10.1007/s11242-021-01542-0</relatedIdentifier><relatedIdentifier relationType="IsCitedBy" relatedIdentifierType="DOI">10.3390/app122312193</relatedIdentifier><relatedIdentifier relationType="IsCitedBy" relatedIdentifierType="DOI">10.3390/app13126981</relatedIdentifier></relatedIdentifiers><sizes><size>142179077</size></sizes><formats><format>application/zip</format></formats><version>1.0</version><rightsList><rights rightsURI="info:eu-repo/semantics/openAccess"/><rights/></rightsList><descriptions><description descriptionType="Abstract">Gas diffusion layers (GDLs) are relevant for the efficient fluid transport between the channel structure and the membrane electrode assembly (MEA) of fuel cells [1].&#xd;
Black/white (BW) images of 25 realizations of a stochastic model represent the micro-structure of paper-type GDLs as manufactured by Toray. A binder model (5 representations) is combined with a fiber model (25 representations each). The 3D structures are represented by 130 images of size 512x512 each with a resolution of 1.5 µm/px. Every image represents a layer of 1.5 µm thickness. This leads to a total amount of 5*25*130=16250 images, arranged in a sub-folder structure that represents the binder model.&#xd;
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130 images of size 512x512 layers represent a section of 768 µm x 768 µm m 195 µm of a GDL. The fiber thickness is 7.5 µm. Binder material is located layer-wise along some fibers with a binder width of 6 µm, 18 µm, 30 µm, 40 µm or filled polygons (indicated as FF).&lt;br>&#xd;
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The stochastic fundamentals are published in [2]. Transport simulations using the Lattice Boltzmann method were conducted and presented in [1;3-9]. Machine learning (ML) aspects were addressed in [10-11].&lt;br>&#xd;
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For binder with &lt;b>WW&lt;/b> in {06, 18 30, 40, FF}, representation &lt;b>N&lt;/b> in {1...25}, image number &lt;b>I&lt;/b> in {1...130},&#xd;
image path/names are:&lt;br> binder-&lt;b>WW&lt;/b>/Sim&lt;b>N&lt;/b>/Image_512x512_&lt;b>N&lt;/b>_No_&lt;b>I&lt;/b>.png.&lt;br>&#xd;
Fig. 1 in [1] shows images with binder width of (A) 6 µm, (B) 18 µm, (C) 30 µm and (D) filled polygons. Fig. 3 in [3] extends the illustration by an 40 µm example, labelled as (D) in [3].&lt;br>&#xd;
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Subsequent simulations in [4-9] favored the binder width  of 18 µm. The ML investigations [10, 11] covered the same binder widths as [1].</description></descriptions><geoLocations/></resource>