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Record W2026042049 · doi:10.1002/cjce.22147

Improved performance of a passive air breathing flat‐plate microbial fuel cell

2014· article· en· W2026042049 on OpenAlexafffundvenue
Sona Kazemi, Khalid Fatih, Madjid Mohseni

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsNational Research Council CanadaUniversity of British Columbia
FundersNational Research Council CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsAnodePower densityGraphiteElectrodeMaterials scienceCathodeSubstrate (aquarium)Microbial fuel cellLayer (electronics)Nitric acidComposite materialAnalytical Chemistry (journal)ChemistryPower (physics)Chromatography

Abstract

fetched live from OpenAlex

This study aims to investigate the effect of the graphite felt (GF) substrate surface treatment, the GF active surface area, and the anode chamber depth on the performance of the passive air breathing flat‐plate microbial fuel cell (FPMFC) configuration. Three passive air breathing FPMFCs (depth of anode chamber: 2 mm, 4 mm, and 8 mm) were developed and operated using 1, 2, and 3 packed layers of three‐dimensional (3D) graphite felt anodes, respectively, with similar cross sectional (geometric) surface area as the cathode and the membrane. The surface of the GF substrate was treated by soaking in a hot solution of nitric acid prior to inoculation. The 2 mm FPMFC generated a peak power density superior to that previously reported for the same configuration with no GF treatment. The peak power density in the 8 mm and 4 mm FPMFCs with 3 and 2 layers of GF increased by 118 % and 48 %, respectively, compared to the 2 mm FPMFC with 1 layer of GF. By using only 1 layer of GF, the peak power density showed no significant variation with the electrode spacing.

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 score0.027
Threshold uncertainty score0.273

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 head0.002
GPT teacher head0.139
Teacher spread0.136 · 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

Citations8
Published2014
Admission routes3
Has abstractyes

Explore more

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