Resolving the gas diffusion layer substrate land and channel region contributions to the oxygen transport resistance of a partially-saturated substrate

Gao, Nan; Shrestha, Pranay; Balakrishnan, Manojkumar; Ouellette, David; Liu, H.; Lee, C.H.; Lee, J.K.; Bazylak, Aimy

doi:10.1016/j.electacta.2019.135001

Cited by 10 publications

(4 citation statements)

References 50 publications

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“…The fitting procedure was rather insensitive to the value of GDL oxygen diffusivity under the rib, meaning that oxygen transport under the rib does not contribute much to the cell performance. This result agrees with the conclusion drawn by Ge et al [19] and Guan et al [24]. On the other hand, the value of D b max is nearly uniform along the channel with the minor variation between 0.02 cm 2 s −1 and 0.03 cm 2 s −1 (figure 8(a)).…”

Section: Resultssupporting

confidence: 92%

“…Similar strong non-uniformity of GDL substrate water content under the rib and channel was reported by Ge et al [19] from X-ray measurements. Ge et al [19] developed also a simple model allowing them to obtain a power in exponential dependence of oxygen transport resistivity of land and channel substrate domains on liquid saturation. Attempt was made to derive the GDL transport resistance by fitting equivalent electric circuit (EEC) to the measured impedance spectra.…”

Section: Introductionsupporting

confidence: 87%

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A quasi three–dimensional model for PEM fuel cell impedance: The effect of local oxygen depletion under the flow field rib

Reshetenko,

Kulikovsky

2023

Mater. Res. Express

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Our previous 1d+1d model for PEM fuel cell impedance is extended to take into account 2d distribution of the oxygen concentration in the gas–diffusion layer under the flow field channel and rib. Fitting of the model impedance to the experimental spectra from the segmented PEMFC shows that in spite of nearly uniform GDL oxygen diffusivity, local oxygen concentration and current density exhibit strong decay under the rib (land). The effect is merely due to larger oxygen transport distance under the rib. The results demonstrate validity of local EIS for measuring 3D distribution of local current in an operating PEM fuel cell.

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Section: Resultssupporting

confidence: 92%

Section: Introductionsupporting

confidence: 87%

A quasi three–dimensional model for PEM fuel cell impedance: The effect of local oxygen depletion under the flow field rib

Reshetenko,

Kulikovsky

2023

Mater. Res. Express

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“…NMR [27], neutron imaging and synchrotron X-ray. Synchrotron X-ray can provide local saturation in liquid water with image resolution below 10 µm in gas diffusion layer and electrode structures [20], [28][29][30], in particular revealing far higher saturations over the ribs than over the channel [31].…”

Section: Electrochemical Impedance Spectroscopy (Eis) For Mass Transfer Phenomena In Pemfcmentioning

confidence: 99%

Electrochemical pressure impedance spectroscopy for investigation of mass transfer in polymer electrolyte membrane fuel cells

Shirsath

Raël

Bonnet

et al. 2020

Current Opinion in Electrochemistry

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Mass transfer phenomena in membrane fuel cells are complex and diversified, because of the presence of complex transport pathways including porous media of very different pore sizes, and possible formation of liquid water. Electrochemical impedance spectroscopy, although allowing valuable information on ohmic, charge and mass transfer phenomena, may nevertheless appear insufficient below 1 Hz. Use of another variable i.e. back pressure as excitation variable for electrochemical pressure impedance spectroscopy is shown here a promising tool for investigations and diagnosis of fuel cells.

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“…The single-phase permeability (k sp ) and the single-phase diffusivity of air (D sp ) were calculated using the extracted pore network of each generated material. Since an operating fuel cell is generally partially saturated, [16,51] we also evaluated the transport properties of pore networks that were numerically invaded with liquid water using the ordinary percolation algorithm. The relative permeability of water (k r ) and relative diffusivity of air (D r ) were calculated to determine how the percolation of water through the network impacted the transport of water and air through the network at varying saturations.…”

Section: Percolation and Transport Simulationsmentioning

confidence: 99%

Accurately predicting transport properties of porous fibrous materials by machine learning methods

Cawte

Bazylak

2022

Electrochemical Science Adv

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Machine learning algorithms trained on data gathered from stochastically generated gas diffusion layers (GDLs) were used to predict key transport properties that govern effective mass transport behaviour in polymer electrolyte membrane fuel cells. Specifically, we present the largest database in the present literature of stochastically generated fibrous GDL substrates (containing over 2000 unique materials) and the associated structural and transport properties determined via pore network modelling. Seven established machine learning algorithms were trained to predict the effective single‐phase permeability (ksp) and diffusivity (Dsp), and the relative permeability (kr) and diffusivity (Dr) of the generated materials using well‐defined material properties as input features. Gradient boosting regression (GBR), artificial neural network, and support vector regression were the best performing predictors of the single‐phase properties, all of which exhibited statistically insignificant differences in error. GBR provided the best prediction accuracy of relative transport properties.

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Resolving the gas diffusion layer substrate land and channel region contributions to the oxygen transport resistance of a partially-saturated substrate

Cited by 10 publications

References 50 publications

A quasi three–dimensional model for PEM fuel cell impedance: The effect of local oxygen depletion under the flow field rib

A quasi three–dimensional model for PEM fuel cell impedance: The effect of local oxygen depletion under the flow field rib

Electrochemical pressure impedance spectroscopy for investigation of mass transfer in polymer electrolyte membrane fuel cells

Accurately predicting transport properties of porous fibrous materials by machine learning methods

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