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Record W2887549346 · doi:10.1016/j.btre.2018.e00276

Hydrogen peroxide production in a pilot-scale microbial electrolysis cell

2018· article· en· W2887549346 on OpenAlexaff
Junyoung Sim, Robertson Reid, Abid Hussain, Junyeong An, Hyung‐Sool Lee

Bibliographic record

VenueBiotechnology Reports · 2018
Typearticle
Languageen
FieldEnvironmental Science
TopicMicrobial Fuel Cells and Bioremediation
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMicrobial electrolysis cellElectrolysisHydrogen peroxideChemistryCathodeHydrogen productionMembraneIon exchangeCarbon fibersDecompositionCathodic protectionYield (engineering)Inorganic chemistryHydrogenChemical engineeringNuclear chemistryElectrochemistryIonMaterials scienceElectrodeBiochemistryOrganic chemistryMetallurgy

Abstract

fetched live from OpenAlex

A pilot-scale dual-chamber microbial electrolysis cell (MEC) equipped with a carbon gas-diffusion cathode was evaluated for H2O2 production using acetate medium as the electron donor. To assess the effect of cathodic pH on H2O2 yield, the MEC was tested with an anion exchange membrane (AEM) and a cation exchange membrane (CEM), respectively. The maximum current density reached 0.94–0.96 A/m2 in the MEC at applied voltage of 0.35–1.9 V, regardless of membranes. The highest H2O2 conversion efficiency was only 7.2 ± 0.09% for the CEM-MEC. This low conversion would be due to further H2O2 reduction to H2O on the cathode or H2O2 decomposition in bulk liquid. This low H2O2 conversion indicates that large-scale MECs are not ideal for production of concentrated H2O2 but could be useful for a sustainable in-situ oxidation process in wastewater treatment.

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)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
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 head0.004
GPT teacher head0.184
Teacher spread0.180 · 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 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

Citations59
Published2018
Admission routes1
Has abstractyes

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