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Record W2103839090 · doi:10.1115/1.4025932

Nonisothermal Hydrodynamic Modeling of the Flowing Electrolyte Channel in a Flowing Electrolyte–Direct Methanol Fuel Cell

2013· article· en· W2103839090 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

VenueJournal of Fuel Cell Science and Technology · 2013
Typearticle
Languageen
FieldEngineering
TopicFuel Cells and Related Materials
Canadian institutionsCarleton University
Fundersnot available
KeywordsElectrolyteDirect methanol fuel cellPressure dropPorosityChemical engineeringChemistryMaterials scienceIsothermal processThermodynamicsComposite materialElectrode

Abstract

fetched live from OpenAlex

The performance of a direct methanol fuel cell (DMFC) can be significantly reduced by methanol crossover. One method to reduce methanol crossover is to utilize a flowing electrolyte channel. This is known as a flowing electrolyte–direct methanol fuel cell (FE–DMFC). In this study, recommendations for the improvement of the flowing electrolyte channel design and operating conditions are made using previous modeling studies on the fluid dynamics in the porous domain of the flowing electrolyte channel and on the performance of a 1D isothermal FE-DMFC incorporating multiphase flow, in addition to modeling of the nonisothermal effects on the fluid dynamics of the FE-DMFC flowing electrolyte channel. The results of this study indicate that temperature difference between flowing electrolyte inflow and the fuel cell have negligible hydrodynamic implications, except that higher fuel-cell temperatures reduce pressure drop. Reducing porosity and increasing permeability is recommended, with a porosity of around 0.4 and a porous-material microstructure typical dimension around 60–70 μm being potentially suitable values for achieving these goals.

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)0.001
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 score0.532
Threshold uncertainty score0.573

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.000
Research integrity0.0000.001
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 head0.003
GPT teacher head0.168
Teacher spread0.165 · 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