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Record W2258805518 · doi:10.18331/brj2015.1.1.8

Simultaneous electricity generation and sulfide removal via a dual chamber microbial fuel cell

2014· article· en· W2258805518 on OpenAlexvenueno aff
Paniz Izadi, Mostafa Rahimnejad

Bibliographic record

VenueBiofuel Research Journal · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsnot available
Fundersnot available
KeywordsMicrobial fuel cellDual (grammatical number)Electricity generationElectricityWaste managementDual purposeSingle chamberFuel cellsEnvironmental scienceChemistryChemical engineeringEngineeringElectrical engineeringBiomedical engineeringMechanical engineeringPhysicsPower (physics)

Abstract

fetched live from OpenAlex

Microbial fuel cells (MFCs) have recently been used to alter different sources of substrates to produce bioelectricity. MFCs can also be used for wastewater treatment and electricity generation simultaneously. Sulfur compounds such as sulfides commonly exist in wastewater and organic waste. In this study a dual chamber MFC was constructed for power production. Sulfide was used as the electron donor in the anaerobic anode compartment. A mixed culture of microorganisms was used as an active biocatalyst to convert the substrate into electricity. The obtained experimental results illustrated that the MFC can successfully alter sulfide to elementary sulfur while generating power. The initial concentration of sulfide in the anode compartment was 0.4 g l-1 and it was completely removed after 3 days of MFC operation. The influence of oxygen was examined in the cathode chamber and the cell voltage gradually increased during aeration, reaching 480 mV after 1200 s. Hexacyanoferrate was added to the cathodic solution in different concentrations and its effects were investigated. The maximum generated voltage, power and current density were 988.9145 mV, 346.746 mW.m-2, 1285.64 mA.m-2, respectively and they were obtained in the presence of 1.4 g l-1 of mediator.

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)0.003
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.586
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.023
GPT teacher head0.268
Teacher spread0.245 · 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.

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

Citations33
Published2014
Admission routes1
Has abstractyes

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