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Record W4308001924 · doi:10.1002/cjce.24744

Dual functions of activated carbon air‐cathode: Nitrobenzene removal and electricity production in microbial fuel cells

2022· article· en· W4308001924 on OpenAlexvenueno aff
Zhifang Zuo, Ting Huang, Xiaoting Zhu, Xiaoyu Jia, Enren Zhang

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2022
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsnot available
Fundersnot available
KeywordsMicrobial fuel cellCathodeActivated carbonAnodeElectrochemistryNitrobenzeneMaterials scienceElectrolysisChemical engineeringElectricity generationCarbon fibersAdsorptionElectrodeChemistryCatalysisOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The removal of nitrobenzene (NB) in microbial electrochemical systems generally requires electrical power to operate systems in the mode of microbial electrolysis cells (MECs). The present study demonstrates that single‐chamber microbial fuel cells (S‐MFCs) assembled with a bioanode and an activated carbon (AC) air cathode could simultaneously remove NB and generate electricity. S‐MFCs with 1 mM NB exhibit long term NB tolerance and stable electricity production, with NB removal up to 98% in an operation cycle and a maximum power of 16.2 ± 1.3 W m −3 . High NB loadings significantly inhibited the activity of anodic biofilms, but the inhibition was reversible. Investigating the removal of NB and its reduction product aniline (AN) in different operations supported the fact that the adsorption at the AC air cathode is the main pathway for the removal of NB and AN from solution, except for the partial conversion of NB to AN by anaerobic reduction in solution. In S‐MFCs, the activated carbon in the cathode plays both functions: catalyzing the oxygen reduction reaction and adsorbing NB and AN, so that the S‐MFC assembled with the AC air cathode, unlike most NB removing MECs, functions as a system with both NB removal and electricity production.

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 score0.030
Threshold uncertainty score0.657

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 head0.005
GPT teacher head0.154
Teacher spread0.149 · 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

Citations2
Published2022
Admission routes1
Has abstractyes

Explore more

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