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Record W2601365344 · doi:10.1149/2.0381706jes

A Mixed Wettability Pore Size Distribution Based Mathematical Model for Analyzing Two-Phase Flow in Porous Electrodes

2017· article· en· W2601365344 on OpenAlexafffund
Jie Zhou, Andreas Pütz, Marc Secanell

Bibliographic record

VenueJournal of The Electrochemical Society · 2017
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsAutomotive Fuel Cell Cooperation (Canada)Alberta EnergyUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaCanada Foundation for Innovation
KeywordsKnudsen diffusionWettingSaturation (graph theory)Materials scienceAnodeCapillary pressurePorosityIsothermal processGaseous diffusionPorosimetryCapillary actionPorous mediumTransport phenomenaElectrodeThermodynamicsChemistryComposite material

Abstract

fetched live from OpenAlex

A multi-dimensional, non-isothermal, two-phase membrane electrode assembly (MEA) numerical model is developed where the micro-structure of the porous layers is characterized by a mixed wettability pore size distribution (PSD). The PSD model is used to predict local water saturation based on gas and liquid pressures, and can be used to study the effect of varying pore size and wettability. The MEA model accounts for gas transport via molecular and Knudsen diffusion, liquid water transport, sorbed water transport by back-diffusion, electro- and thermo-osmosis, and heat generation and transport. Multi-step kinetic models are used to predict anode and cathode electrochemical reactions. Local transport losses are accounted for using a local transport resistance. The PSD model is used to predict capillary pressure vs. saturation and saturation vs. relative liquid permeability curves based on PSDs from several GDLs obtained using mercury intrusion porosimetry. The PSD-based MEA model electrochemical performance predictions are also compared to experimental data from the literature. Results show that the numerical model is able to capture the performance changes associated with varying temperate and the introduction of a micro-porous layer.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.655
Threshold uncertainty score0.441

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.012
GPT teacher head0.254
Teacher spread0.242 · 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
GenreEmpirical

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

Citations68
Published2017
Admission routes2
Has abstractyes

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