Analysis of representative elementary volume and through-plane regional characteristics of carbon-fiber papers: diffusivity, permeability and electrical/thermal conductivity

Understanding the transport processes that occur in carbon-fiber papers (CFPs) used in fuel cells, electrolyzers, and metal-air/redox flow batteries is necessary to help predict cell performance and durability, optimize materials and diagnose problems. The most common technique used to model these thin, heterogeneous, anisotropic porous media is the volume-averaged approximation based on the existence of a representative elementary volume (REV). However, the applicability of the continuum hypothesis to these materials has been questioned many times, and the error incurred in the predictions is yet to be quantified. In this work, the existence of a REV in CFPs is assessed in terms of dry effective transport properties: mass diffusivity, permeability and electrical/thermal conductivity. Multiple sub-samples with different widths and thicknesses are examined by combining the lattice Boltzmann method with X-ray tomography images of four uncompressed CFPs. The results show that a meaningful length scale can be defined in the material plane in the order of 1–2 mm, which is comparable to the rib/channel width used in the aforementioned devices. As for the through-plane direction, no distinctive length scale smaller than the thickness can be identified due to the lack of a well-defined separation between pore and volume-averaged scales in these inherently thin heterogeneous materials. The results also show that the highly porous surface region (amounting up to 20% of the thickness) significantly reduces the through-plane electrical/thermal conductivity. Overall, good agreement is found with previous experimental data of virtually uncompressed CFPs when approximately the full thickness is considered.The authors thank the support team of Calcul Quebec and Compute Canada for their help during the simulation campaign, as well as Dr. Dula Parkinson and Dr. Alastair MacDowell at the Advanced Light Source (ALS) for help in obtaining the tomographic images. This work was funded under the Fuel Cell Performance and Durability Consortium (FC-PAD), by the Fuel Cell Technologies Office (FCTO), Office of Energy Efficiency and Renewable Energy (EERE), of the U.S. Department of Energy under contract number DE-AC02-05CH11231, Project ENE2015-68703-C2-1-R (MINECO/FEDER, UE) and the research grant 'Ayudas a la Investigation en Energia y Medio Ambiente' awarded to the first author by the Spanish lberdrola Foundation. I.V. Zenyuk and A.D. Shum would like to acknowledge support from the National Science Foundation under CBET Award 1605159. X-ray tomography experiments were performed on beamline 8.3.2 at the ALS (Lawrence Berkeley National Laboratory), which is a national user facility funded by the Department of Energy, Office of Basic Energy Sciences under contract DE-ACO2-05CH11231. Numerical calculations were performed on the supercomputing clusters Briaree, Colosse, Guillimin and Mp2, managed by Calcul Quebec and Compute Canada. The operation of these supercomputers is funded by the Canada Foundation for Innovation (CFI), Ministere de l'Economie, de l'Innovation et des Exportations du Quebec (MEIE), RMGA and the Fonds de recherche du Quebec -Nature et technologies (FRQ-NT)

Metal Oxide Clusters on Nitrogen-Doped Carbon are Highly Selective for CO2Electroreduction to CO

The electrochemical reduction of CO2 (eCO2RR) using renewable energy is an effective approach to pursue carbon neutrality. The eCO2RR to CO is indispensable in promoting C-C coupling through bifunctional catalysis and achieving cascade conversion from CO2 to C2+. This work investigates a series of M/N-C (M = Mn, Fe, Co, Ni, Cu, and Zn) catalysts, for which the metal precursor interacted with the nitrogen-doped carbon support (N-C) at room temperature, resulting in the metal being present as (sub)nanosized metal oxide clusters under ex situ conditions, except for Cu/N-C and Zn/N-C. A volcano trend in their activity toward CO as a function of the group of the transition metal is revealed, with Co/N-C exhibiting the highest activity at -0.5 V versus RHE, while Ni/N-C shows both appreciable activity and selectivity. Operando X-ray absorption spectroscopy shows that the majority of Cu atoms in Cu/N-C form Cu0 clusters during eCO2RR, while Mn/, Fe/, Co/, and Ni/N-C catalysts maintain the metal hydroxide structures, with a minor amount of M0 formed in Fe/, Co/, and Ni/N-C. The superior activity of Fe/, Co/, and Ni/N-C is ascribed to the phase contraction and the HCO3- insertion into the layered structure of metal hydroxides. Our work provides a facile synthetic approach toward highly active and selective electrocatalysts to convert CO2 into CO. Coupled with state-of-the-art NiFe-based anodes in a full-cell device, Ni/N-C exhibits >80% Faradaic efficiency toward CO at 100 mA cm-2.The research leading to these results has received funding from the A-LEAF Project, which is funded by the European Union’s H2020 Programme under grant agreement no. 732840. ICN2 and ICIQ acknowledge funding from the FEDER/Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación (projects ENE2017-85087-C3 and RTI2018-095618-B-I00) and the Generalitat de Catalunya (2017 SGR 327 and 2017- SGR-1406) and by the CERCA Programme / Generalitat de Catalunya. ICN2 and ICIQ are supported by the Severo Ochoa program from Spanish MINECO (grants no. SEV-2017-0706 and CEX2019-000925-S)

Lithium Metal Battery Quality Control via Transformer–CNN Segmentation

Lithium metal battery (LMB) has the potential to be the next-generation battery system because of its high theoretical energy density. However, defects known as dendrites are formed by heterogeneous lithium (Li) plating, which hinders the development and utilization of LMBs. Non-destructive techniques to observe the dendrite morphology often use X-ray computed tomography (XCT) to provide cross-sectional views. To retrieve three-dimensional structures inside a battery, image segmentation becomes essential to quantitatively analyze XCT images. This work proposes a new semantic segmentation approach using a transformer-based neural network called TransforCNN that is capable of segmenting out dendrites from XCT data. In addition, we compare the performance of the proposed TransforCNN with three other algorithms, U-Net, Y-Net, and E-Net, consisting of an ensemble network model for XCT analysis. Our results show the advantages of using TransforCNN when evaluating over-segmentation metrics, such as mean intersection over union (mIoU) and mean Dice similarity coefficient (mDSC), as well as through several qualitatively comparative visualizations

Lithium Metal Battery Quality Control via Transformer-CNN Segmentation.

Lithium metal battery (LMB) has the potential to be the next-generation battery system because of its high theoretical energy density. However, defects known as dendrites are formed by heterogeneous lithium (Li) plating, which hinders the development and utilization of LMBs. Non-destructive techniques to observe the dendrite morphology often use X-ray computed tomography (XCT) to provide cross-sectional views. To retrieve three-dimensional structures inside a battery, image segmentation becomes essential to quantitatively analyze XCT images. This work proposes a new semantic segmentation approach using a transformer-based neural network called TransforCNN that is capable of segmenting out dendrites from XCT data. In addition, we compare the performance of the proposed TransforCNN with three other algorithms, U-Net, Y-Net, and E-Net, consisting of an ensemble network model for XCT analysis. Our results show the advantages of using TransforCNN when evaluating over-segmentation metrics, such as mean intersection over union (mIoU) and mean Dice similarity coefficient (mDSC), as well as through several qualitatively comparative visualizations

Spatially Resolved Modeling of Electric Double Layers and Surface Chemistry for the Hydrogen Oxidation Reaction in Water-Filled Platinum–Carbon Electrodes

We present a multidimensional model that spatially resolves transport, surface chemistry, and electrochemical kinetics within water-filled pores of a porous electrode with an adjacent Nafion polymer electrolyte. A novel aspect of this model is the simultaneous capturing of the electric double layers (EDLs) at the water|Nafion and water|electrode interfaces. In addition, the model incorporates discrete domains to spatially resolve specific adsorption at the inner Helmholtz plane (IHP); surface charging due to functional groups; and multistep, multipathway electrochemical reactions at the outer Helmholtz plane (OHP). Herein, we apply the model to the hydrogen oxidation reaction (HOR) in water-filled mesopores of a platinum– (Pt−) carbon electrode, similar to a polymer electrolyte fuel cell’s (PEFC’s) anode. This work was motivated by the limited understanding of how incomplete polymer electrolyte coverage of a catalyst affects the kinetics and transport in these electrodes. Our results indicate that the Pt within a water-filled pore is only 5% effective for an applied potential of 20 mV. At low potentials (<150 mV), the current is limited by the low H₂ solubility in water according to the Tafel–Volmer HOR pathway. At higher potentials, the current is reduced by proton exclusion by the overlapping EDLs and the Donnan potential at the water|polymer electrolyte interface, suppressing the Heyrovsky–Volmer pathway. Our analysis includes a parametric study of the pore radius and length

Coupling continuum and pore-network models for polymer-electrolyte fuel cells

© 2015 Hydrogen Energy Publications, LLC. Three novel iterative methodologies for coupling continuum and pore-network models (PNM) applied to polymer-electrolyte fuel cells (PEFCs) are presented. The modeling framework developed in this work merges the advantages of a continuum model, such as computational time, ease of implementation, and complicated physics, with those of relatively novel PNMs, such as discrete information on water-front location and distribution. The outputs generated by the PNM are fed into the continuum model to compute electrochemical reaction rates and associated heat and mass fluxes. Out of three presented coupling methodologies, the most effective coupling is identified to be where locally-resolved effective diffusivity, thermal conductivity, and liquid permeability are computed with the PNM and fed into the continuum model and the fluxes from continuum model fed back into the PNM in an iterative scheme until solution convergence is reached. The described method is computationally efficient with stable convergence of less than five iterations. The proposed algorithms can be applied to multiple computational platforms and PEFC and related model architectures

Probing water distribution in compressed fuel-cell gas-diffusion layers using X-ray computed tomography

X-ray computed tomography was used to investigate geometrical land and channel effects on spatial liquid-water distribution in gas-diffusion layers (GDLs) of polymer-electrolyte fuel cells under different levels of compression. At low compression, a uniform liquid-water front was observed due to water redistribution and uniform porosity; however, at high compression, the water predominantly advanced at locations under the channel for higher liquid pressures. At low compression, no apparent correlation between the spatial liquid water and porosity distributions was observed, whereas at high compression, a strong correlation was shown, indicating a potential for smart GDL architecture design with modulated porosity. Keywords: X-ray computed tomography, Gas-diffusion layers, Water saturation, Land-channel effects, Compression, Polymer-electrolyte fuel cell

Modeling Single and Two-Phase Transport in Thin Porous Layers Using a Composite Continuum-Pore Network Formulation

In this work, a composite continuum-pore network formulation is presented to model single and two-phase transport thin porous layers, such as gas diffusion layers in polymer electrolyte fuel cells (PEFCs) and active electrodes in redox flow batteries (RFBs). The formulation can be integrated into CFD codes, thus combining the ease of implementation of continuum-based modeling and the computational power of pore-network modeling. The composite model includes a control volume (CV) mesh at the layer scale, which embeds a cubic pore network [1,2]. The pore-network model is used to determine analytically local anisotropic effective transport properties (local effective diffusivity and permeability), which are mapped onto the CV mesh to simulate transport in the porous transport layer [3,4]. Good agreement is found between the predicted global effective transport properties (global effective diffusivity and permeability) under dry and wet conditions and previous experimental data reported in the literature for Toray TGP-H series carbon paper. Water saturation distributions are also compared with results obtained using X-ray computed tomography [4]. [1] P.A. García-Salaberri, I.V. Zenyuk, J.T. Gostick, A.Z. Weber, Modeling Gas Diffusion Layer in Polymer Electrolyte Fuel Cells Using a Continuum-Based Pore-Network Formulation, ECS Trans. 97 (2020) 615. [2] P.A. García-Salaberri, Modeling diffusion and convection in thin porous transport layers using a composite continuum-network model: Application to gas diffusion layers in polymer electrolyte fuel cells, Int J. Heat Mass Transf. (2020), submitted. [3] P.A. García-Salaberri, J.T. Gostick, G. Hwang, A.Z. Weber, M. Vera, Effective diffusivity in partially-saturated carbon-fiber gas diffusion layers: Effect of local saturation and application to macroscopic continuum models, J. Power Sources 296 (2015) 440–453. [4] P.A. García-Salaberri, G. Hwang, M. Vera, A.Z. Weber, J.T. Gostick, Effective diffusivity in partially-saturated carbon-fiber gas diffusion layers: Effect of through-plane saturation distribution, Int. J. Heat Mass Transf. 86 (2015) 319–333

Modeling Gas Diffusion Layers in Polymer Electrolyte Fuel Cells Using a Continuum-based Pore-network Formulation

A pore-network formulation is presented to model gas diffusion layers (GDLs) in polymer electrolyte fuel cells (PEFCs) using a continuum-based approach. The formulation can easily be integrated into macroscopic models in CFD codes, thus improving the modeling predictions while keeping a moderate computational cost. The continuum-based pore-network formulation is based on a cubic lattice [1], which is divided into control volumes (cubes) of prescribed size. Pores and throats are placed inside the control volumes, and “connectors” of negligible volume interconnect the control volumes. The “connectors” are used to regulate the invasion-percolation pattern according to the size of the throat that links the pores within neighboring control volumes. Hence, the formulation can account for both invasion-percolation between pores as well as evaporation/condensation in the pore volume inside each control volume. This is a major advantage compared to traditional pore-network models based on a fully discrete formulation where phase-change phenomena are difficult to implement. Local anisotropic effective transport properties (permeability and diffusivity) are determined using a 1D resistor network analogy inside each control volume according to the size of the pore and throats in it. The model is validated against capillary pressure curves and effective transport properties (effective diffusivity and permeability) measured ex situ. In addition, water saturation profiles are compared with distributions obtained using X-ray computed tomography [2]. [1] Jeff T. Gostick, Marios A. Ioannidis, Michael W. Fowler, Mark D. Pritzker, Pore network modeling of fibrous gas diffusion layers for polymer electrolyte membrane fuel cells, J. Power Sources 173 (2007) 277-290. [2] P.A. García-Salaberri, G. Hwang, M. Vera, A.Z. Weber, J.T. Gostick, Effective diffusivity in partially-saturated carbon-fiber gas diffusion layers: Effect of through-plane saturation distribution, International Journal of Heat and Mass Transfer 86 (2015) 319–333

Modeling Gas Diffusion Layers in Polymer Electrolyte Fuel Cells Using a Continuum-based Pore-network Formulation

Multiscale modeling of porous media in polymer electrolyte fuel cells is of paramount importance to improve predictions and assist the design of new materials. In this work, a composite-continuum-network formulation is presented to model species diffusion and convection in gas diffusion layers (GDLs). The model can be incorporated into CFD codes with moderate computational cost. The macroscopic model is based on a structured mesh composed of parallelepiped control volumes (CVs) and differential connectors (with negligible volume). The CV mesh embeds an internal structured pore network, which is used to determine analytically local anisotropic effective transport properties (effective diffusivity and permeability). The global structural parameters and effective transport properties predicted by the model are in good agreement with previous experimental data. Moreover, the results show that heterogeneities in the GDL can have significant influence on the fluxes from/to the catalyst layer, thus affecting local degradation rates.The model was conceived and partially developed during the stay of P.A. García-Salaberri at LBNL, which was supported by the US-Spain Fulbright commission. P.A. GarcíaSalaberri also thanks the support of the project for young researchers PEM4ENERGYCMUC3M (Universidad Carlos III de Madrid)