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Record W2030144881 · doi:10.1115/fuelcell2011-54410

Experimental Analysis of a Single Cell Flowing Electrolyte-Direct Methanol Fuel Cell

2011· article· en· W2030144881 on OpenAlexaff
Nasim Sabet-Sharghi, Cynthia A. Cruickshank, Edgar Matida

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsCarleton University
Fundersnot available
KeywordsElectrolyteDirect methanol fuel cellMembrane electrode assemblyProton exchange membrane fuel cellMethanolMaterials scienceSulfuric acidNafionChemical engineeringElectrodeChemistryAnalytical Chemistry (journal)MembraneChromatographyInorganic chemistryElectrochemistryOrganic chemistryAnode

Abstract

fetched live from OpenAlex

A single cell flowing electrolyte - direct methanol fuel cell (FE-DMFC) was studied experimentally. Nafion® NRE-212 was used in the membrane electrode assembly (MEA). The flowing electrolyte channel was formed by a polyethylene porous material. The active area of the fuel cell was approximately 25 cm2. Effects of varying flowing electrolyte conditions (channel thickness, sulfuric acid concentration, channel pressure), methanol concentration, and fuel cell temperature on the overall performance of the cell were studied. It was observed that stopping the flowing electrolyte caused a reduction in the open circuit voltage as well as the current of the cell, indicating that the methanol crossover affected the cell performance. Also, it is presented that a thicker flowing electrolyte channel results in lower power density, and sulfuric acid concentration of 2 molar (18%) was found to be the most advantageous. Raising operating temperature resulted in much better performance of the cell. Increasing flowing electrolyte pressure slightly decreased the 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.

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)0.001
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.013
GPT teacher head0.190
Teacher spread0.177 · 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".

Quick stats

Citations4
Published2011
Admission routes1
Has abstractyes

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