How theory and simulation can drive fuel cell electrocatalysis

Eslamibidgoli, Mohammad Javad; Huang, Jun; Kadyk, Thomas; Malek, Ali; Eikerling, Michael

doi:10.1016/j.nanoen.2016.06.004

Cited by 79 publications

(88 citation statements)

References 252 publications

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“…As discussed in a recent perspective article, 7 the development of fully fletched density functional theory (DFT)-based methodologies to self-consistently describe electrochemical interfaces will remain a foremost albeit highly ambitious a) Authors to whom correspondence should be addressed: jhuangelectrochem@qq.com and meikerl@sfu.ca goal in theoretical electrocatalysis for the foreseeable future. In the interim and as the foundation for further computational forays, a viable theoretical methodology of surface charging effects is needed that minimizes the required input from explicit DFT calculations.…”

Section: Introductionmentioning

confidence: 99%

Double layer of platinum electrodes: Non-monotonic surface charging phenomena and negative double layer capacitance

Huang

Zhou

Zhang

et al. 2018

The Journal of Chemical Physics

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Section: Introductionmentioning

confidence: 99%

Double layer of platinum electrodes: Non-monotonic surface charging phenomena and negative double layer capacitance

Huang

Zhou

Zhang

et al. 2018

The Journal of Chemical Physics

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“…V, Tafel slope c c mol/m 3 concentration in the CL is equal to the one in the channel. Under these conditions, the fuel cell is assumed to be operating in the stoichiometric regime [6,15,39], and the fuel cell geometry can be simplified as depicted in Fig.…”

Section: Parameters and Variables Bmentioning

confidence: 99%

“…To make the fuel cells less costly, one way is to increase the electrical power density for a given cell. This can be achieved by both improving the electrochemistry [3] and the mass transport in all the fuel cell materials [4].…”

Section: Introductionmentioning

confidence: 99%

Polymer electrolyte membrane fuel cell operating in stoichiometric regime

Chevalier

Olivier

Josset

et al. 2019

Journal of Power Sources

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is an open access repository that collects the work of Arts et Métiers ParisTech researchers and makes it freely available over the web where possible. This is an author-deposited version published in: https://sam.ensam.eu Handle ID� Fuel cell stoichiometric regime is evidenced at global and local scale. � Cathode channel mass transport governs the fuel cell performance in this regime. � Low frequency spectral signature of stoichiometric regime is measured using EIS. � Current density distributions are measured using a segmented flow field. � Fuel cell kinetic parameters can be easily measured in stoichiometric regime. A B S T R A C TIn this work, we report the existence of a stoichiometric regime where the performances of operating polymer electrolyte membrane (PEM) fuel cells are entirely governed by the mass transport in the cathode channels. An analytical model of the fuel cell stoichiometric regime is derived and evidenced experimentally: from the cell spectral signature at low frequency based on electrochemical impedance spectroscopy, and from the current density distribution measured using a segmented current collector. The existence of such regime provides a simple way to characterize, model and predict PEM fuel cell performances.

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“…[48] ML is also employed in protein design, [49] molecular design, [50,51] drug design [52] or materials discovery. [53][54][55] Through several ground-breaking works by Norskov and his coworkers [56][57][58][59][60] on developing the d-band model and the computational hydrogen electrode (CHE) scheme, quantum mechanical calculations based on DFT have become the quintessential methodology for understanding reaction mechanisms and predicting activity and selectivity of catalyst materials for electrochemical energy storage and conversion (see review paper [22] and references therein). However, complexity of the electrode-electrolyte interface and the immense parameter space it entails limit such methods in terms of consistency and accuracy, and their applicability in materials screening.…”

Section: Path Ahead: Vimi For Ml-based Catalyst Designmentioning

confidence: 99%

Virtual Materials Intelligence for Design and Discovery of Advanced Electrocatalysts

et al. 2019

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Similar to advancements gained from big data in genomics, security, internet of things, and e‐commerce, the materials workflow could be made more efficient and prolific through advances in streamlining data sources, autonomous materials synthesis, rapid characterization, big data analytics, and self‐learning algorithms. In electrochemical materials science, data sets are large, unstructured/heterogeneous, and difficult to process and analyze from a single data channel or platform. Computer‐aided materials design together with advances in data mining, machine learning, and predictive analytics are expected to provide inexpensive and accelerated pathways towards tailor‐made functionally optimized energy materials. Fundamental research in the field of electrochemical energy materials focuses primarily on complex interfacial phenomena and kinetic electrocatalytic processes. This perspective article critically assesses AI‐driven modeling and computational approaches that are currently applied to those objects. An application‐driven materials intelligence platform is introduced, and its functionalities are scrutinized considering the development of electrocatalyst materials for CO2 conversion as a use case.

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How theory and simulation can drive fuel cell electrocatalysis

Cited by 79 publications

References 252 publications

Double layer of platinum electrodes: Non-monotonic surface charging phenomena and negative double layer capacitance

Double layer of platinum electrodes: Non-monotonic surface charging phenomena and negative double layer capacitance

Polymer electrolyte membrane fuel cell operating in stoichiometric regime

Virtual Materials Intelligence for Design and Discovery of Advanced Electrocatalysts

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