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Experimental Study on the Performance of DMFC Using Novel Composite Materials of Polypyrrole-Coated Polypropylene as BPs

2017· article· en· W2767115489 on OpenAlexaff
Rungsima Yeetsorn, Chaiwat Prapainainar, Michael Fowler, Yaowaret Maiket

Bibliographic record

VenueKey engineering materials · 2017
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Waterloo
FundersNational Science and Technology Development Agency
KeywordsMaterials sciencePolypropylenePolypyrroleComposite materialComposite numberCarbon blackGraphiteMolding (decorative)Direct methanol fuel cellSurface modificationPolymerElectrodeChemical engineeringPolymerizationChemistry

Abstract

fetched live from OpenAlex

Polypropylene/three-carbon-filler composite bipolar plates (BPs) of direct methanol fuel cell (DMFC) were fabricated by an injection molding. The composite materials were made of polypropylene (PP), carbon black, carbon fiber and graphite. Gas flow channel surfaces on the BPs were subsequently modified by polypyrrole (PPy) using a coating technique in order to improve surface electrical conductivity. This research is a feasibility study to use PPy-coated PP composite as BPs in a DMFC. The surface electrical resistance and performance in a fuel cell containing the composite BPs under DMFC operating conditions were evaluated against conventional graphite BPs. The surface resistance values of PPy-coated PP composites decreased around six orders of magnitude, compared with those values of PP composites. According to the performance results, PPy-coated composite BPs can be used in DMFC if the surface adhesion between a PPy layer and the BP surface was further improved.

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 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 communication0.0000.000
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 head0.018
GPT teacher head0.231
Teacher spread0.213 · 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".

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Citations1
Published2017
Admission routes1
Has abstractyes

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