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Multi-Segment Foam Flow Field in Ambient Pressure Polymer Exchange Membrane Fuel Cell

2013· article· en· W2141879443 on OpenAlexvenueno aff
Chih-Kai Cheng, Ting-Chu Jao, Che-Jung Hsu, Fang-Bor Weng

Bibliographic record

VenueJournal of Technology Innovations in Renewable Energy · 2013
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceComposite materialMetal foamProton exchange membrane fuel cellElectrolyteNickelPolarization (electrochemistry)Pressure dropHeat transferMembraneElectrodePorosityMetallurgyChemistryMechanics

Abstract

fetched live from OpenAlex

In order to produce low-cost flow field plates for polymer electrolyte membrane fuel cells, we used nickel foam in this study rather than conventional flow field. Nickel foam has high electron conductivity, thermal conductivity, and mechanical strength. Electrochemical impedance spectrum analysis is carried out to evidence the use on flow field plates of nickel foam. From the impedance fitting results, the nickel foam cases showed the lower contact resistance than the serpentine. However, such plates have poor performance at low temperatures and ambient pressure. In order to overcome this, a multi-segment foam flow field is designed in this study. This increased the performance of the polarization curve by 70% from 162 to 275.5 mw cm-2 than the original nickel foam design. Also, the mass transfer resistance was reduced, and the Warburg impedance value of the operation voltage decreased by 0.4 V. The numerical analysis results demonstrate that increased segment numbers can increase the performance of the multi-segment foam flow field.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.523
Threshold uncertainty score0.567

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.007
GPT teacher head0.200
Teacher spread0.194 · 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
Published2013
Admission routes1
Has abstractyes

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