MétaCan
Menu
Back to cohort
Record W2089922692 · doi:10.1007/s10295-008-0391-0

Decolourization of recalcitrant dyes with a laccase from Streptomyces coelicolor under alkaline conditions

2008· article· en· W2089922692 on OpenAlexafffund
Etienne Dubé, François Shareck, Yves Hurtubise, Marc Beauregard, Claude Daneault

Bibliographic record

VenueJournal of Industrial Microbiology & Biotechnology · 2008
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEnzyme-mediated dye degradation
Canadian institutionsInstitut National de la Recherche ScientifiqueUniversité du Québec à Trois-Rivières
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsLaccaseStreptomyces coelicolorEffluentAcetosyringoneCongo redChemistryContext (archaeology)WastewaterPulp and paper industryMicrobiologyOrganic chemistryBiologyEnzymeBiochemistryEnvironmental engineeringEnvironmental science

Abstract

fetched live from OpenAlex

Colored wastewater from textile industries is a consequence of dye manufacturing processes. Two percent of dyes that are produced are discharged directly in aqueous effluent and more than 10% are subsequently lost during the textile coloration process. It is not surprising that these compounds have become a major environmental concern. In that context, we have evaluated the potential use of Streptomyces coelicolor laccase for decolourization of various dyes with and without a mediator. Results showed that in all cases the combination of laccase and the mediator acetosyringone was able to rapidly decolourize, to various degrees, all the dyes tested. In 10 min, decolourization was achieved at 94% for acid blue 74, 91% for direct sky blue 6b and 65% for reactive black 5. Furthermore, decolourization was achieved at 21% for reactive blue 19 and at 39% for the direct dye Congo red in 60 min. These results demonstrate the potential use of this laccase in combination with acetosyringone, a natural mediator, for dye decolourization.

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 score0.020
Threshold uncertainty score0.823

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.031
GPT teacher head0.220
Teacher spread0.189 · 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

Citations48
Published2008
Admission routes2
Has abstractyes

Explore more

Same venueJournal of Industrial Microbiology & BiotechnologySame topicEnzyme-mediated dye degradationFrench-language works237,207