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Record W3095334680 · doi:10.19044/esj.2020.v16n30p1

Performance Of Inherent Electrogens In Benthic Sediment Mud At Different Ph Conditions In Microbial Fuel Cell

2020· article· en· W3095334680 on OpenAlexaffabout
Binjal Pradhan, Ranjan Pradhan

Bibliographic record

VenueEuropean Scientific Journal ESJ · 2020
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsUniversity of GuelphSt. Clair College
Fundersnot available
KeywordsBenthic zoneSedimentMicrobial fuel cellEnvironmental scienceEnvironmental chemistryChemistryOceanographyGeologyGeomorphology

Abstract

fetched live from OpenAlex

Comparative measurement of electricity produced by inherent eletrogens in benthic mud that were maintained at different operating pH using microbial fuel cell was studied. A two-chamber microbial fuel cell model with proton exchange membrane was adopted for this study applying electrogene sourced from benthic mud collected from local lake and pond in Ontario. The objective of this study is to investigate the effects of different pH of 6, 7 and 8 maintained within the anode cell in a microbial biofuel cell (MFC) containing microbial communities found in a benthic mud medium over 192 hours. The outcomes of the study demonstrated that in acidic conditions, there was an initial decrease in output whereas an alkaline condition allowed for the acclimatization and eventual increase of electric current and microbial activity of the study period. MFC operated at pH 7 generated consistently higher electric power during the study duration exhibiting ideal conditions for the inherent exoelectrogenic bacteria. On average 249.8 mV of electricity were measured from the MFC with pH 7. The average current density calculated to be 9.76 (±2.02) X 10-5 µA/cm2 . The average power density during the study period was calculated to be 2.49 (± 0.77) X 10-5 µW/cm2

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)0.001
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.325
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0030.001

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 head0.013
GPT teacher head0.196
Teacher spread0.183 · 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

Citations0
Published2020
Admission routes2
Has abstractyes

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