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Record W1985148033 · doi:10.1080/09593330.2012.758644

Kinetic study of electro-Fenton oxidation of azo dyes on boron-doped diamond electrode

2012· article· en· W1985148033 on OpenAlexaff
Fares Almomani, Elena A. Baranova

Bibliographic record

VenueEnvironmental Technology · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsElectrochemistryChemistryAnodeDegradation (telecommunications)KineticsDiamondReagentElectrodeChemical oxygen demandCurrent densityReaction rate constantOxygenCatalysisInorganic chemistryNuclear chemistryEnvironmental engineeringOrganic chemistryWastewater

Abstract

fetched live from OpenAlex

The present work compares electrochemical degradation of red and blue azo textile dyes in single- and two-compartment electrochemical cells in the presence of Fenton reagent (Fe2+) and using a boron-doped diamond anode. Degradation of both dyes was related to the concentration of dye, applied current density and the concentration of FeSO4 catalyst. Complete colour removal and approximately 91% of organic matter oxidation was achieved in a two-compartment electrochemical cell at an applied current density of 20 mA x cm(-2), pH of 3 and Fe(2+) ion concentration of 0.02 mM. Higher current density and reaction time were required to achieve the same removals in a one-compartment electrochemical cell. Dye degradation kinetics as well as chemical oxygen demand removal rate were successfully modelled to pseudo first-order kinetics. The apparent first-order rate constants (k(o)) for degradation of red dye with an initial concentration of 20, 40 and 60 ppm were found to be 2.67 +/- 0.16, 2.19 +/- 0.09 and 1.5 +/- 0.03 min(-1), and for blue dye at the same initial concentrations were 1.99 +/- 0.2, 0.95 +/- 0.02 and 0.71 +/- 0.030 min(-1), respectively.

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 score0.079
Threshold uncertainty score0.962

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)0.0010.000

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 head0.006
GPT teacher head0.222
Teacher spread0.216 · 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

Citations27
Published2012
Admission routes1
Has abstractyes

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