Microstructure reconstruction and characterization of PEMFC electrodes

Wu, Weixiong; Jiang, Fangming

doi:10.1016/j.ijhydene.2014.03.074

Cited by 75 publications

(41 citation statements)

References 52 publications

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“…(6) of porous structures in batteries [30][31][32][33][34] and proton exchange membrane (PEM) fuel cells. [35][36][37][38][39][40][41][42][43][44] Moreover, it has been implemented as a standard addition to predicting microstructures in electrochemistry models, such as in the COMSOL Multiphysics modelling software (COMSOL, Inc.). 23 However, predictions given by the Bruggeman correlation are not always consistent with experimental results.…”

Section: Porosity-tortuosity Relationshipsmentioning

confidence: 99%

“…Conclusions vary substantially as in some instances, simulations agree well with the Bruggeman correlation, 33,55 while considerable disagreement was observed in other cases. 44,[60][61][62] The reason for this seems to be sample specific, as heterogeneity and geometry are characteristics of porous materials that are not accounted for by the Bruggeman correlation. The aforementioned studies have shown that the characteristic shape of the analysed microstructure has considerable effects on the validity of the Bruggeman relation: spherical structures, which follow Bruggeman's initial hypothesis very closely, adhere to the correlation.…”

Section: Porosity-tortuosity Relationshipsmentioning

confidence: 99%

See 1 more Smart Citation

Tortuosity in electrochemical devices: a review of calculation approaches

Tjaden

Brett

Shearing

2016

International Materials Reviews

192

188

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The tortuosity of a structure plays a vital role in the transport of mass and charge in electrochemical devices. Concentration polarisation losses at high current densities are caused by mass transport limitations and are thus a function of microstructural characteristics. As tortuosity is notoriously difficult to ascertain, a wide and diverse range of methods has been developed to extract the tortuosity of a structure. These methods differ significantly in terms of calculation approach and data preparation techniques. Here, we review tortuosity calculation procedures applied in the field of electrochemical devices to better understand the resulting values presented in the literature. Visible differences between calculation methods are observed, especially when using porosity-tortuosity relationships and when comparing geometric and flux based tortuosity calculation approaches.

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Section: Porosity-tortuosity Relationshipsmentioning

confidence: 99%

Section: Porosity-tortuosity Relationshipsmentioning

confidence: 99%

Tortuosity in electrochemical devices: a review of calculation approaches

Tjaden

Brett

Shearing

2016

International Materials Reviews

192

188

View full text Add to dashboard Cite

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“…Therefore, it is also critical to assess the errors on transport property predictions associated with inaccuracies during the water intrusion process. Gas transport in the GDL microstructures has been simulated using PNM, 36,37,63,64 LBM 10,21,30,45,[65][66][67][68][69] and porescale CFD simulations, 23,[25][26][27]70,71 however the obtained results are seldom compared to experimental observation and the sensitivity of transport property predictions to errors in water distribution have not been discussed.…”

Section: F554mentioning

confidence: 99%

Virtual Liquid Water Intrusion in Fuel Cell Gas Diffusion Media

Sabharwal

Gostick

Secanell

2018

J. Electrochem. Soc.

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A cluster based full morphology (CFM) model is developed to predict liquid water intrusion in GDLs. The CFM model is used to simulate water intrusion into a dry GDL microstructure obtained from μ-CT. Numerical water saturation distributions are compared to experimental μ-CT reconstructions of the same GDL sample at varying saturation levels. A quantitative validation of the CFM model results is then provided by studying the number of voxels in the image that contain water in both the CFM model results and the μ-CT reconstructions. Results reveal that CFM simulations showed 56-95.7% agreement in the liquid water voxels when compared to the μ-CT simulations for saturations in the range of 29-92.3%. Gas transport simulations are then performed on μ-CT and CFM partially saturated GDLs in order to study the validity of the CFM model images to estimate transport properties of partially saturated GDLs. A maximum error of 20% was observed between the predicted effective diffusivities obtained from CFM simulations and those obtained directly from simulations on the μ-CT data for saturations below 40%. Effective diffusivity predictions from the CFM simulations agree well with in-plane effective diffusivities in literature while the through-plane effective diffusivities were underpredicted by a factor of 2.

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“…Stochastic modeling has proved to be a very effective method of producing realistic microstructures without the need for microscopy or computationally expensive physics-based simulations; see, e.g., [27][28][29][30][31][32][33][34][35]. Stochastic models have also been successfully coupled with numerical simulations; see, e.g., [28,36,37].…”

Section: Introductionmentioning

confidence: 99%

“…Stochastic models have also been successfully coupled with numerical simulations; see, e.g., [28,36,37]. Using fast and flexible stochastic models, it is then possible to investigate the relationships between microstructure characteristics and material functionality [38].…”

Section: Introductionmentioning

confidence: 99%