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Record W2119148169 · doi:10.1093/ijlct/cts069

Superhydrophobic flow channel surface and its impact on PEM fuel cell performance

2012· article· en· W2119148169 on OpenAlexafffund
Yongxin Wang, Saher Al Shakhshir, Xianguo Li, Pu Chen

Bibliographic record

VenueInternational Journal of Low-Carbon Technologies · 2012
Typearticle
Languageen
FieldMaterials Science
TopicSurface Modification and Superhydrophobicity
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceWettingContact angleProton exchange membrane fuel cellCoatingComposite materialElectrolyteHysteresisSuperhydrophobic coatingSurface modificationTetrafluoroethyleneFlow (mathematics)PolymerChemical engineeringFuel cellsElectrodeChemistryMechanics

Abstract

fetched live from OpenAlex

Water management is a critical issue in polymer electrolyte membrane fuel cells (PEMFCs), and it is normally achieved through the modification of surface wettability condition for the cell components. In this study, superhydrophobic surface-coating materials were developed and the gas flow channel surfaces were modified for superhydrophobic surface property with small sliding angles (SAs). The coated surface characteristics were measured, including static contact angle (CA), SA and CA hysteresis as well as surface geometrical properties. The flow characteristics through such surface-coated channels were measured, and comparison was made with hydrophilic channels and channels coated with poly(tetrafluoroethylene), a commonly used surface-coating agent in PEMFCs. It was found that the presently modified superhydrophobic flow channels yield the lowest resistance to the two-phase flow; and both the mechanical and thermal stabilities of the attained superhydrophobicity for the coated surfaces were also investigated. It was demonstrated experimentally that such coated flow channels result in improved PEMFC performance due to improved water management.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.706

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 communication0.0000.001
Open science0.0010.000
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 head0.017
GPT teacher head0.266
Teacher spread0.249 · 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

Citations21
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueInternational Journal of Low-Carbon TechnologiesSame topicSurface Modification and SuperhydrophobicityFrench-language works237,207