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Record W4412700091 · doi:10.11159/ffhmt25.139

Vapor Concentration within the PEMFC Bipolar Plate over Long Term Operation

2025· article· en· W4412700091 on OpenAlexvenueno aff
Ngoc Dat Nguyen, Van Thai Nguyen, Jongbin Woo, Sangseok Yu

Bibliographic record

VenueProceedings of the ... International Conference on Fluid Flow, Heat and Mass Transfer · 2025
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsnot available
FundersMinistry of Trade, Industry and Energy
KeywordsTerm (time)Proton exchange membrane fuel cellMaterials scienceNuclear engineeringEnvironmental scienceFuel cellsPhysicsEngineeringChemical engineering

Abstract

fetched live from OpenAlex

One of the primary technical challenges associated with proton exchange membrane fuel cells (PEMFCs) is ensuring their durability.This study introduces an experimental methodology to evaluate the thermal and water characteristics within PEMFC during continuous operation under the New European Driving Cycle (NEDC) mode.Specifically, fifty micro relative humidity and temperature sensors (micro-RH/T sensors) are integrated into flow field plate channels to measure the distribution and characteristics of temperature and water vapor within the PEMFC.The study tracks evolution of these parameters over a 100-hour NEDC durability test, revealing notable trends.In addition, an artificial neuron network-based model (ANN-based model) is developed to analyze water transport through the membrane at both the beginning and the end of the durability test.The findings demonstrate the significant impact of temperature and water characteristics on the performance and durability of PEMFC under prolonged operational conditions.This research provides valuable insights into PEMFC operation and establishes a foundation for further advancements in PEMFC design, aiming to enhance performance and extend the lifetime of automotive PEMFCs.

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 score0.203
Threshold uncertainty score0.380

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 head0.011
GPT teacher head0.219
Teacher spread0.208 · 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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the ... International Conference on Fluid Flow, Heat and Mass TransferSame topicFuel Cells and Related MaterialsFrench-language works237,207