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Record W2270301134 · doi:10.3390/en9020079

Systematic Study of Separators in Air-Breathing Flat-Plate Microbial Fuel Cells—Part 2: Numerical Modeling

2016· article· en· W2270301134 on OpenAlexafffund
Sona Kazemi, Melissa Barazandegan, Madjid Mohseni, Khalid Fatih

Bibliographic record

VenueEnergies · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsNational Research Council CanadaUniversity of British Columbia
FundersNational Research Council CanadaAgriculture and Agri-Food CanadaNatural Sciences and Engineering Research Council of Canada
KeywordsSeparator (oil production)AnodeElectrolyteCathodeMicrobial fuel cellOxygenChemistryOxygen transportInternal resistanceElectrodeMechanicsMaterials scienceMass transferAnalytical Chemistry (journal)ThermodynamicsPower (physics)ChromatographyPhysics

Abstract

fetched live from OpenAlex

The separator plays a key role on the performance of passive air-breathing flat-plate MFCs (FPMFC) as it isolates the anaerobic anode from the air-breathing cathode. The goal of the present work was to study the separator characteristics and its effect on the performance of passive air-breathing FPMFCs. This was performed partially through characterization of structure, properties, and performance correlations of eight separators presented in Part 1. Current work (Part 2) presents a numerical model developed based on the mixed potential theory to investigate the sensitivity of the electrode potentials and the power output to the separator characteristics. According to this numerical model, the decreased peak power results from an increase in the mass transfer coefficients of oxygen and ethanol, but mainly increasing mixed potentials at the anode by oxygen crossover. The model also indicates that the peak power is affected by the proton transport number of the separator, which affects the cathode pH. Anode pH, on the other hand, remains constant due to application of phosphate buffer solution as the electrolyte. Also according to this model, the peak power is not sensitive to the resistivity of the separator because of the overshadowing effect of the oxygen crossover.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.205
Teacher spread0.196 · 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 designSimulation or modeling
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

Citations10
Published2016
Admission routes2
Has abstractyes

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