Interlink among catalyst loading, transport and performance of proton exchange membrane fuel cells: a pore-scale study

Li, Xing; Hou, Yuze; Wu, Cheng‐Ru; Du, Qing; Jiao, Kui

doi:10.1039/d1nh00501d

Cited by 26 publications

(11 citation statements)

References 38 publications

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“…Local mass transfer near the Pt catalyst, such as proton conduction and oxygen transport, and mass transfer across a CL, such as oxygen supply and liquid water removal, were investigated to some degree before. [41][42][43][44][84][85][86][87][88][89][90][91][92] However, studies on the emerged novel CLs such as nanowire, novel layered fiber arrangement, and ordered column array architectures are absent. Therefore, additional effort needs to be made to understand the transport phenomena in these new CLs to better guide their design for ultralow Pt loading PEMFCs.…”

Section: Accelerating Catalyst Layer Development For Ultralow Pt Load...mentioning

confidence: 99%

“…These simulation methods have been employed to explore the electrochemical reactions on catalysts, evaluate catalyst activity, reveal local mass transfer characteristics, evaluate the three-phase boundaries for the reactions, explore two-phase flow in CLs, and evaluate CL performance. [19][20][21]27,41,44,[84][85][86][87]92 For example, Li et al 86 developed a pore-scale model based on the LB method to obtain the gas distribution, current density distribution, and voltage loss in CLs, by considering gas transport, proton transport, electron transport, and electrochemical reaction in this model. Fan et al 44,92 developed an oxygen transport model based on MD simulation to investigate the effect of ionomer film morphology on the local oxygen transport phenomena.…”

Section: Accelerating Catalyst Layer Development For Ultralow Pt Load...mentioning

confidence: 99%

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Towards ultralow platinum loading proton exchange membrane fuel cells

Fan

Deng²,

Zhang

et al. 2023

Energy Environ. Sci.

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Section: Accelerating Catalyst Layer Development For Ultralow Pt Load...mentioning

confidence: 99%

Section: Accelerating Catalyst Layer Development For Ultralow Pt Load...mentioning

confidence: 99%

Towards ultralow platinum loading proton exchange membrane fuel cells

Fan

Deng²,

Zhang

et al. 2023

Energy Environ. Sci.

Self Cite

View full text Add to dashboard Cite

show abstract

“…[48,49] Therefore, virtual design and optimization of electrodes can only depend on synthetic microstructures generated by various stochastic algorithms and physical models. [50][51][52] Though stochastically synthetic microstructures have similar essential structural parameters, the intrinsic assumption in the algorithms limits the ability to generate realistic microstructures and therefore possibly leads to less realistic optimization results. Besides, the variables to be optimized are also limited, for example, grain size, positions and numbers, and are therefore unable to fully reflect the high complexity of real morphologies.…”

Section: Forward Design Using π Learningmentioning

confidence: 99%

π Learning: A Performance‐Informed Framework for Microstructural Electrode Design

Niu

Zhao

et al. 2023

Advanced Energy Materials

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batteries, electrolyzers, and electrochemical CO 2 reduction reactors all depend heavily on the nature of the meso-scale reaction sites and charge conductivity in their electrodes. [2][3][4] The meso-scale electrode structure is often determined by the complex interactions of various phases under different thermal, mechanical, and chemical conditions. Therefore, how to design the next generation of high-performance electrodes remains a critical challenge.Data-driven design based on machine learning (ML) has been increasingly recognized as a revolutionary technology in structure design and has been demonstrated to successfully identify various excellent structures in protein sequences, [5] drug molecules, [6] and nanomaterials. [7] This paradigm opens the door for innovative design of high-performance catalyst materials and electrodes in electrochemical engineering. [8][9][10] A typical example is to accelerate the discovery of optimal atomic structures and compositions driven by density functional theory (DFT) calculations. [11][12][13] For instance, Ma et al. [14] proposed two deep learning algorithms to screen emerging electrode materials for sodium-ion and potassium-ion batteries. This data-driven approach for material screening is promising to accelerate the discovery of high-performance electrode materials. Benayad [15] et al. reviewed recent ML applications in Designing high-performance porous electrodes is the key to next-generation electrochemical energy devices. Current machine-learning-based electrode design strategies are mainly orientated toward physical properties; however, the electrochemical performance is the ultimate design objective. Performance-orientated electrode design is challenging because the current data driven approaches do not accurately extract high-dimensional features in complex multiphase microstructures. Herein, this work reports a novel performance-informed deep learning framework, termed π learning, which enables performance-informed microstructure generation, toward overall performance prediction of candidate electrodes by adding most relevant physical features into the learning process. This is achieved by integrating physicsinformed generative adversarial neural networks (GANs) with convolutional neural networks (CNNs) and with advanced multi-physics, multi-scale modeling of 3D porous electrodes. This work demonstrates the advantages of π learning by employing two popular design philosophies: forward and inverse designs, for the design of solid oxide fuel cells electrodes. π learning thus has the potential to unlock performance-driven learning in the design of next generation porous electrodes for advanced electrochemical energy devices such as fuel cells and batteries.

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“…Additionally, compared with VOF methods, porous structure reconstruction with LBM is usually easier. To date, LBM has been employed to study two-phase flow in the GDL, MPL, and CL of PEM fuel cells, , while few studies were reported for porous flow fields.…”

Section: Two-phase Flow Dynamicsmentioning

confidence: 99%

Porous Flow Field for Next-Generation Proton Exchange Membrane Fuel Cells: Materials, Characterization, Design, and Challenges

Zhang

Tao

et al. 2022

Chem. Rev.

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Porous flow fields distribute fuel and oxygen for the electrochemical reactions of proton exchange membrane (PEM) fuel cells through their pore network instead of conventional flow channels. This type of flow fields has showed great promises in enhancing reactant supply, heat removal, and electrical conduction, reducing the concentration performance loss and improving operational stability for fuel cells. This review presents the research and development progress of porous flow fields with insights for next-generation PEM fuel cells of high power density (e.g., ∼9.0 kW L–1). Materials, fabrication methods, fundamentals, and fuel cell performance associated with porous flow fields are discussed in depth. Major challenges are described and explained, along with several future directions, including separated gas/liquid flow configurations, integrated porous structure, full morphology modeling, data-driven methods, and artificial intelligence-assisted design/optimization.

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Interlink among catalyst loading, transport and performance of proton exchange membrane fuel cells: a pore-scale study

Abstract: For the first time, a pore-scale model is developed to simulate the reactive transport of oxygen, protons, and electrons in the catalyst layer of PEM fuel cells.

Cited by 26 publications

References 38 publications

Towards ultralow platinum loading proton exchange membrane fuel cells

Towards ultralow platinum loading proton exchange membrane fuel cells

π Learning: A Performance‐Informed Framework for Microstructural Electrode Design

Porous Flow Field for Next-Generation Proton Exchange Membrane Fuel Cells: Materials, Characterization, Design, and Challenges

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