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Record W2586669058 · doi:10.1002/cjce.22805

Enzymatic reuse of simulated dyeing process effluent using horseradish peroxidase

2017· article· en· W2586669058 on OpenAlexvenueno aff
Simone Farias, Diego A. Mayer, Débora de Olíveira, Antônio Augusto Ulson de Souza, Selene Maria de Arruda Guelli Ulson de Souza

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2017
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEnzyme-mediated dye degradation
Canadian institutionsnot available
FundersConselho Nacional de Desenvolvimento Científico e Tecnológico
KeywordsEffluentDyeingReuseHorseradish peroxidaseHydrogen peroxidePulp and paper industryChemistryProcess (computing)WastewaterWaste managementEnvironmental scienceEnvironmental engineeringComputer scienceOrganic chemistryEnzymeEngineering

Abstract

fetched live from OpenAlex

Abstract Reactive dyes are complex structures that cause environmental damage when discarded. The aim of this study was to evaluate the reuse of effluent from an enzyme‐catalyzed dyeing process. The main parameters that influence the degradation of the mixture of dyes using the enzyme horseradish peroxidase were studied using response surface methodology: enzyme, hydrogen peroxide, and dye concentrations as well as the pH. The best conditions for the synthetic effluent were then applied to the reuse of the effluent from the dyeing process in laboratory and pilot scales. In the studies in pilot scale a high colour intensity was obtained (ΔE* value of 0.6) and a good washing fastness (4.0). The results obtained indicate that peroxidases could be used for colour removal on an industrial scale to save water and energy in the post‐dyeing process, reusing part of this effluent with the same efficiency as that of a traditional process.

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)0.001
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 score0.285

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
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 head0.019
GPT teacher head0.221
Teacher spread0.202 · 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

Citations4
Published2017
Admission routes1
Has abstractyes

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