Fundamental relationships between 3D pore topology, electrolyte conduction and flow properties: Towards knowledge-based design of ceramic diaphragms for sensor applications

Holzer, Lorenz; Stenzel, Ole; Pecho, Omar; Ott, Thomas; Boiger, Gernot Kurt; Gorbár, Michal; Hazan, Yoram de; Penner, Dirk; Schneider, Ingo; Cervera, Rinlee Butch M.; Gasser, Philippe

doi:10.1016/j.matdes.2016.03.034

Cited by 22 publications

(40 citation statements)

References 28 publications

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“…It must be emphasized repeatedly that the relationship only holds for specific definitions of b [18][19][20] and t geodesic [24], which are described in the methods part below. It was also shown in experimental studies with porous ceramic membranes [25] and with cermet-electrodes for solid oxide fuel cells [34] that Eqs. (7) and (8) are indeed capable to predict effective diffusivity (D eff ) and effective conductivity (s eff ) in a reliable way.…”

Section: Diffusion and Conductionmentioning

confidence: 88%

“…[35]. In a recent study [25], we have established the following relationship, which includes all relevant microstructure effects that have an impact on permeability: Thereby, the M-factor depends on e, b and t in the same way as reported for diffusion (compare Eqs. (12) and (8)).…”

Section: Permeabilitymentioning

confidence: 99%

“…A value of p 2 was determined empirically for the constant c in porous membranes of sintered ceramics [25]. It turns out that R hI is always somewhat smaller than the average radius of bulges, but larger than the average radius of the bottlenecks.…”

Section: Permeabilitymentioning

confidence: 99%

“…permeability) depends on the hydraulic radius, which can be defined as the average from bottleneck and bulge radii (R hII = r min /2 + r max /2, see Eq. (14)) [25]. Using this definition for R h it is possible to capture structural anisotropy, because r min depends on the transport direction.…”

Section: Hydraulic Radius (R H ) and Permeability (K) In Anisotropic Gdlmentioning

confidence: 99%

“…By combining these methods, new quantitative relationships between microstructure characteristics and effective properties have been established. But so far these micro-macro relationships were only used for isotropic porous materials [23][24][25]. One specific target for the present study is it now to adapt these methodologies for reliable prediction of effective properties in the fibrous, anisotropic GDL.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Microstructure-property relationships in a gas diffusion layer (GDL) for Polymer Electrolyte Fuel Cells, Part I: effect of compression and anisotropy of dry GDL

Holzer

Pecho

Schumacher

et al. 2017

Electrochimica Acta

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Section: Diffusion and Conductionmentioning

confidence: 88%

Section: Permeabilitymentioning

confidence: 99%

Section: Permeabilitymentioning

confidence: 99%

Section: Hydraulic Radius (R H ) and Permeability (K) In Anisotropic Gdlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Microstructure-property relationships in a gas diffusion layer (GDL) for Polymer Electrolyte Fuel Cells, Part I: effect of compression and anisotropy of dry GDL

Holzer

Pecho

Schumacher

et al. 2017

Electrochimica Acta

Self Cite

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Big data for microstructure‐property relationships: A case study of predicting effective conductivities

et al. 2017

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The analysis of big data is changing industries, businesses and research as large amounts of data are available nowadays. In the area of microstructures, acquisition of (3-D tomographic image) data is difficult and time-consuming. It is shown that large amounts of data representing the geometry of virtual, but realistic 3-D microstructures can be generated using stochastic microstructure modeling. Combining the model output with physical simulations and data mining techniques, microstructure-property relationships can be quantitatively characterized. Exemplarily, we aim to predict effective conductivities given the microstructure characteristics volume fraction, mean geodesic tortuosity, and constrictivity. Therefore, we analyze 8119 microstructures generated by two different stochastic 3-D microstructure models. This is-to the best of our knowledge-by far the largest set of microstructures that has ever been analyzed. Fitting artificial neural networks, random forests and classical equations, the prediction of effective conductivities based on geometric microstructure characteristics is possible.

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Review of Theories and a New Classification of Tortuosity Types

Holzer,

Marmet,

Fingerle

et al. 2023

Springer Series in Materials Science

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Many different definitions of tortuosity can be found in literature. In addition, also many different methodologies are nowadays available to measure or to calculate tortuosity. This leads to confusion and misunderstanding in scientific discussions of the topic. In this chapter, a thorough review of all relevant tortuosity types is presented. Thereby, the underlying concepts, definitions and associated theories are discussed in detail and for each tortuosity type separately. In total, more than 20 different tortuosity types are distinguished in this chapter. In order to avoid misinterpretation of scientific data and misunderstandings in scientific discussions, we introduce a new classification scheme for tortuosity, as well as a systematic nomenclature, which helps to address the inherent differences in a clear and efficient way. Basically, all relevant tortuosity types can be grouped into three main categories, which are (a) the indirect physics-based tortuosities, (b) the direct geometric tortuosities and (c) the mixed tortuosities. Significant differences among these tortuosity types are detected, when applying the different methods and concepts to the same material or microstructure. The present review of the involved tortuosity concepts shall serve as a basis for a better understanding of the inherent differences. The proposed classification and nomenclature shall contribute to more precise and unequivocal descriptions of tortuosity.

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Fundamental relationships between 3D pore topology, electrolyte conduction and flow properties: Towards knowledge-based design of ceramic diaphragms for sensor applications

Cited by 22 publications

References 28 publications

Microstructure-property relationships in a gas diffusion layer (GDL) for Polymer Electrolyte Fuel Cells, Part I: effect of compression and anisotropy of dry GDL

Microstructure-property relationships in a gas diffusion layer (GDL) for Polymer Electrolyte Fuel Cells, Part I: effect of compression and anisotropy of dry GDL

Big data for microstructure‐property relationships: A case study of predicting effective conductivities

Review of Theories and a New Classification of Tortuosity Types

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