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Physical Modeling of Fuel Cells and their Components

2007· other· en· W1941002961 on OpenAlexaff
Michael Eikerling, Alexei A. Kornyshev, Andrei Kulikovsky

Bibliographic record

VenueEncyclopedia of Electrochemistry · 2007
Typeother
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsBC Innovation CouncilNational Research Council CanadaSimon Fraser University
Fundersnot available
KeywordsProton exchange membrane fuel cellProton transportElectrolyteAnodeCathodePercolation (cognitive psychology)Water transportBackflowMaterials scienceChemical engineeringProtonPolymerMembraneChemical physicsChemistryComposite materialWater flowMechanical engineeringEngineeringPhysicsEnvironmental engineeringPhysical chemistryElectrode

Abstract

fetched live from OpenAlex

Abstract This chapter presents an overview of the status of physical modeling of polymer‐electrolyte fuel cells (PEFCs), the understanding gained from modeling and its impact on optimization of the operation regime and new cell design. It begins with the physical theory of proton transport in polymer‐electrolyte membranes (PEMs). This comprises microscopic aspects of the elementary act of proton transfer in aqueous environments, their realization in a single water‐filled pore and the statistical geometry of the pore network. Based on these fundamental properties, electroosmosis and different models of water backflow under fuel cell operation conditions are discussed. The resulting water‐content profiles and current–voltage performance are compared with experimental data. The diffusion model of water backflow perceives the membrane as a solution of water in a polymer host, while the hydraulic permeation model rests on the idea of a swelling porous structure, inside of which the proton and water transport take place. An important result is the critical current density, at which water content near the anode drops below the percolation threshold for water network proton conductance. Next, theories of performance and structure of composite catalyst layers are presented, mainly focusing on the cathode as an eminent example. They account for the transport of feed gas, protons and electrons, as well as for the reaction at the membrane/catalyst interface, and result in a “phase diagram”, which suggests an optimum thickness of the layer, subject to the basic parameters and the target current density. The relation between the structure and performance, rationalized using the concepts of the percolation theory, paves the way for an optimum composition of the layer. A similar theory of the complex impedance connects the composite structure and ac response. This gives a tool for determining the catalyst layer parameters. With this background we move toward 3D effects in the cell. The approach rests on a quasi‐three‐dimensional (Q3D) model of the stack element. The fuel cell stack is a two‐scale system. The small and large scales are determined, respectively, by membrane‐electrode assembly (MEA) thickness and by the length of the feed channel. The fully 3D model of the stack element is split into a 2D model of a cell cross section (internal model) and a 1D model of the feed flow along the channel (channel model). The two models are coupled via the local current density along the channel and the overall solution is obtained by iterations. The model is designed to investigate the interplay of small‐ and large‐scale processes in PEFC/DMFC. It results in distributions of gas concentrations, proton and electron currents, and reaction rates in a cell cross section, perpendicular to a long meander‐like channel, that is, it gives a functional “map of a cell”. Model equations and numerical procedures are discussed. The results of simulations are shown for the stack modules of the gas‐feed DMFC and hydrogen–oxygen PEFC. A simplified theory of “along‐the‐channel” feed gas consumption, important for understanding the cell starvation effect is surveyed and compared with simulations and experiments. The message of the model results is summarized and discussed in the sequence of the rising potential for improving PEFC/DMFC design and operation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.862
Threshold uncertainty score0.957

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.004
GPT teacher head0.187
Teacher spread0.183 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreOther

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations21
Published2007
Admission routes1
Has abstractyes

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