MétaCan
Menu
Back to cohort
Record W2792760538 · doi:10.1080/15435075.2018.1431547

Effect of air supply on the performance of an active direct methanol fuel cell (DMFC) fed with neat methanol

2018· article· en· W2792760538 on OpenAlexaff
Qian Xu, Weiqi Zhang, Jian Zhao, Lei Xing, Qiang Ma, Li Xu, Huaneng Su

Bibliographic record

VenueInternational Journal of Green Energy · 2018
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsAnodeDirect methanol fuel cellMethanolCathodeChemistryChemical engineeringOperating temperatureVolumetric flow rateMethanol fuelDehydrationElectrical engineeringThermodynamicsElectrodeOrganic chemistryEngineering

Abstract

fetched live from OpenAlex

Unlike the situation in the direct methanol fuel cell (DMFC) fed with dilute liquid methanol solution, the required water in anode for a DMFC fed with neat methanol is entirely transported from cathode. In this study, the water concentration in anode catalyst layer of such a DMFC operating with fully active mode is theoretically analyzed, followed by the experimental investigations on the effects of air flow rate and operating temperature on cell performance. The results revealed that the air flow rate has a strong impact on cell performance, especially at larger current density. Overmuch air causes rapid decline of cell performance, which results from the dehydration of membrane and lack of water in the anode reaction sites. Raising temperature induces faster reaction kinetics, while undesired stronger water dissipation from the DMFC. In practice, the stable cell resistance can be used as a criterion to help the DMFC to achieve a high and sustainable performance by finely combining the air flow rate and operating temperature.

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 score0.134
Threshold uncertainty score0.333

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 head0.004
GPT teacher head0.207
Teacher spread0.203 · 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

Citations28
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueInternational Journal of Green EnergySame topicFuel Cells and Related MaterialsFrench-language works237,207