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Record W4249503153 · doi:10.1149/ma2016-02/38/2496

Determining the Impact of Dynamic Load Conditions on Interfacial Liquid Water Accumulation in Polymer Electrolyte Membrane Fuel Cell Gas Diffusion Layers Using Synchrotron X-Ray Radiography

2016· article· en· W4249503153 on OpenAlexaff
Rupak Banerjee, Nan Ge, Jongmin Lee, Michael G. George, Hang Liu, Daniel Muirhead, Pranay Shrestha, Stéphane Chevalier, James Hinebaugh, Aimy Bazylak

Bibliographic record

VenueECS Meeting Abstracts · 2016
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsProton exchange membrane fuel cellElectrolyteWater transportMaterials sciencePower densityTransient responseNuclear engineeringChemical engineeringEnvironmental scienceChemistryFuel cellsPower (physics)Water flowEngineeringElectrical engineeringThermodynamicsElectrodeEnvironmental engineering

Abstract

fetched live from OpenAlex

Automakers around the world are investing in polymer electrolyte membrane (PEM) fuel cells for the next generation drivetrains for their low emission automobiles. For PEM fuel cell vehicles to become commercially competitive, fuel cell efficiency and durability must increase. However, in order to advance the performance of PEM fuel cells, the dynamic behaviour of liquid water transport must be taken into consideration, particularly because fuel cell cars will be driven with dynamic load (power draw) conditions1. The relationship between PEM fuel cell performance and dynamic load must be well understood so that next generation fuel cells can be tailored for optimal dynamic operation. Effective water management is key to obtaining the optimal performance of fuel cells2,3. It has also been shown that the transient response of two-phase flows of water is significantly longer compared to the electrochemical response1. Therefore, the changes in two-phase flows in the fuel cell can have a larger impact on the dynamic performance of fuel cells4,5. Liquid water evolves in the porous layers of the fuel cell until it reaches a steady state for the current density of operation6. However, there is a scarcity of experimental observations focused on GDL liquid water saturation as a function of time with changes in operational current density. In this work, the transient response of the cell potential and the dynamic change in water saturation of the gas diffusion layer (GDL) are investigated. The current density of the cell was increased from 0 A/cm2 to prescribed values at various rates of increasing current density (ramp rates), while the response of the cell potential was concurrently measured. Figure 1 shows the transient response of the cell potential due to a change in the current density from 0 to 1.2 A/cm2 at various rates of increase. With a step change in the current density, the voltage fluctuated for a few minutes before the potential of the cell dropped, leading to performance failure that was indicative of flooding. However, when a ramp was applied to gradually reach the same current density as the step change, the cell was able to maintain a steady potential. This shows that the ramp rate of increasing current density has a direct impact on the cell performance and its transient response. In addition, X-ray radiographic evidence of water in the porous layers of the PEM fuel cell during transient operation will be presented. The dynamic water evolution at the microporous layer (MPL)|catalyst layer interface and the MPL|GDL interface are highly influenced by the rate of increasing current density. The cell performance was correlated to the time-dependent water evolution patterns in order to identify the interfacial liquid water accumulation as a function of changing current density. References: 1. R. Banerjee and S. G. Kandlikar, Int. J. Hydrog. Energy, 40, 3990–4010 (2015). 2. S. G. Kandlikar, Heat Transf. Eng., 29, 575–587 (2008). 3. J. P. Owejan, J. J. Gagliardo, J. M. Sergi, S. G. Kandlikar, and T. A. Trabold, Int. J. Hydrog. Energy, 34, 3436–3444 (2009). 4. R. Banerjee and S. G. Kandlikar, Int. J. Hydrog. Energy, 39, 19079–19086 (2014). 5. R. Banerjee and S. G. Kandlikar, J. Power Sources, 268, 194–203 (2014). 6. Y. Wang and C.-Y. Wang, J. Electrochem. Soc., 154, B636 (2007). Figure 1

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How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.248
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2016
Admission routes1
Has abstractyes

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Same venueECS Meeting Abstracts→Same topicFuel Cells and Related Materials→French-language works237,207→