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Record W2081015898 · doi:10.1139/s04-007

Electrolytic treatment of azo dyes containing o,o′-dihydroxyazo complexation sites

2004· article· en· W2081015898 on OpenAlexvenueno aff
Margaret J. Kupferle, Ahmed Galal, Paul L. Bishop

Bibliographic record

VenueJournal of Environmental Engineering and Science · 2004
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEnzyme-mediated dye degradation
Canadian institutionsnot available
Fundersnot available
KeywordsChemistryAbsorbanceReaction rate constantChlorineCopperAcid dyeNuclear chemistryElectrochemistryALIZARIN REDInorganic chemistrySupporting electrolyteElectrolyteThiosulfateSpectrophotometryTriarylmethane dyeKineticsChromatographyElectrodeDyeingOrganic chemistry

Abstract

fetched live from OpenAlex

The impact of structure on decolorization rate was studied for two azo dyes containing the o,o′-dihydroxyazo complexation site, Direct Red 83 and Acid Alizarin Violet N. Both dyes were investigated with and without complexed copper; the methoxylated pre-cursor of Direct Red 83 was also investigated. The dye solutions were electrolytically treated and sample conductivity, pH, and temperature were measured before and after each 60-min run. The UV–VIS absorbance spectra of samples were measured at 10 min intervals with and without quenching of chlorine residuals with sodium thiosulfate. The decolorization rate data calculated from the absorbance spectra were plotted as fraction of dye remaining, C t /C 0 , versus time, t, in seconds. First-order curves were fitted to the data, and the apparent first-order rate constant, k, was calculated for each dye. In general, the Acid Alizarin Violet N forms were decolorized more rapidly than the Direct Red 83 forms, the presence of copper protected the dyes from the continuation of indirect oxidation, and methoxylation reduced the first-order rate constant, but it did not protect the azo bond from continued indirect oxidation as was the case with copper. Key words: azo dye, structure, electrolytic treatment, electrochemical, color, decolorization, rate constant.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.688
Threshold uncertainty score0.123

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 head0.009
GPT teacher head0.182
Teacher spread0.173 · 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

Citations6
Published2004
Admission routes1
Has abstractyes

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