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Record W2124432387 · doi:10.1149/1.2766650

Active Water Management for PEM Fuel Cells

2007· article· en· W2124432387 on OpenAlexfundno aff
Shawn Litster, Cullen Buie, T Fábián, John K. Eaton, Juan G. Santiago

Bibliographic record

VenueJournal of The Electrochemical Society · 2007
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsProton exchange membrane fuel cellWater transportNuclear engineeringPower densityWater flowMaterials scienceEnvironmental scienceChemical engineeringChemistryProcess engineeringEnvironmental engineeringPower (physics)Fuel cellsThermodynamicsEngineering

Abstract

fetched live from OpenAlex

Proton exchange membrane (PEM) fuel cells require humidified gases to maintain proper membrane humidification, but this often results in a problematic accumulation of liquid water. Typically, excessive air flow rates and serpentine channel designs are used to mitigate flooding at the cost of system efficiency. In this paper, we present an active water management system that decouples water removal from oxidant delivery. The system uses a porous carbon flow field plate as an integrated wick that can passively redistribute water within the fuel cell. The system also employs an external electro-osmotic (EO) pump that actively removes excess water from the channels and gas diffusion layer. For a fuel cell with 23 parallel air channels, we demonstrate a 60% increase in maximum power density over a standard graphite plate with a low air stoichiometry of 1.3. EO pumping represents a negligible parasitic load, consuming typically less than 0.5% of the fuel cell power. Experimental and modeling results show that simple passive water transport through the porous carbon alone can prevent flooding at certain operating conditions and flow field dimensions. However, active water management with EO pumping facilitates robust operation with a high volumetric power density across all operating conditions.

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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.120
Threshold uncertainty score0.203

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 head0.194
Teacher spread0.191 · 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

Citations73
Published2007
Admission routes1
Has abstractyes

Explore more

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