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Record W2911354088 · doi:10.1002/fuce.201800056

Composite Membranes of PVDF Nanofibers Impregnated with Nafion for Increased Fuel Concentrations in Direct Methanol Fuel Cells

2019· article· en· W2911354088 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueFuel Cells · 2019
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of British Columbia, Okanagan CampusKelowna General HospitalUniversity of British Columbia
FundersChina Scholarship Council
KeywordsNafionMembraneMethanol fuelMaterials scienceMethanolProton exchange membrane fuel cellChemical engineeringDirect methanol fuel cellNanofiberSwellingComposite numberComposite materialChemistryElectrochemistryOrganic chemistryElectrode

Abstract

fetched live from OpenAlex

Abstract Serious methanol crossover of Nafion greatly limits the use of increased fuel concentrations in methanol fuel cells, which results in a decreased power density. To lower the methanol crossover of Nafion, thin layers of PVDF nanofibers were successfully electrospun and impregnated with a Nafion solution to create novel fuel cell membranes. The morphological structures, mechanical properties, thermal stabilities, chemical resistance and proton conductivity were investigated for each composite membrane. The performances of membranes with different layers of PVDF nanofibers were evaluated, using a single cell direct methanol fuel cell with 10M methanol fuel. In comparison with membranes of pure Nafion, the introduction of PVDF fiber mats greatly enhanced the membrane's thermal and oxidation stabilities, suppressed swelling ratios and water uptake, and increase fuel cell performance.

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.

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 categoriesMeta-epidemiology (narrow)
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 score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.005
GPT teacher head0.186
Teacher spread0.181 · 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