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Record W3175244128 · doi:10.3390/catal11070793

Electrochemical Degradation of Methylene Blue Using a Ni-Co-Oxide Anode

2021· article· en· W3175244128 on OpenAlexafffund
Emmanuel Onyekachi Nwanebu, Xiaocheng Liu, Elmira Pajootan, Viviane Yargeau, Sasha Omanovic

Bibliographic record

VenueCatalysts · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsMcGill University
FundersPetroleum Technology Development FundNatural Sciences and Engineering Research Council of CanadaFaculty of Engineering, McGill University
KeywordsMethylene blueAnodeElectrochemistryChlorideDegradation (telecommunications)ChemistryInorganic chemistryOxideChemical oxygen demandAqueous solutionChlorineNuclear chemistryElectrodeWastewaterEnvironmental engineeringOrganic chemistryCatalysisPhotocatalysis

Abstract

fetched live from OpenAlex

The potential of using thermally prepared Ni0.6Co0.4-oxide for the electrochemical degradation of organic contaminants was investigated using methylene blue (MB) in an aqueous solution, as a model pollutant. The results of UV spectroscopy obtained during galvanostatic electrolyses at the anode indicated the complete removal of the methylene blue dye. The high removal of chemical oxygen demand (COD) and total organic carbon (TOC) suggested a high level of mineralization of its intermediates. It was found that the electrocatalytic performance of the electrode in the anodic degradation of the organic pollutant was significantly enhanced by the presence of chloride ions in the solution. The improvement in the degradation rate of MB was attributed to the in situ electrogeneration of chlorine active species. The results show that Ni0.6Co0.4-oxide anode can be employed as a stable energy-efficient electrocatalyst in the electrochemical purification of wastewater.

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 score0.016
Threshold uncertainty score0.377

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 head0.017
GPT teacher head0.270
Teacher spread0.253 · 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

Citations20
Published2021
Admission routes2
Has abstractyes

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