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Response Surface Methodology for Optimization of Enzyme-Catalyzed Azo Dye Decolorization

2019· article· en· W2917929428 on OpenAlexafffund
Laura G. Cordova-Villegas, Alejandra Y. Cordova-Villegas, Keith E. Taylor, Nihar Biswas

Bibliographic record

VenueJournal of Environmental Engineering · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEnzyme-mediated dye degradation
Canadian institutionsUniversity of Windsor
FundersNatural Sciences and Engineering Research Council of CanadaConsejo Nacional de Ciencia y Tecnología
KeywordsResponse surface methodologyChemistryBox–Behnken designCatalysisEnzymeChromatographyNuclear chemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Azo dyes are a water-pollution problem causing damage to ecosystems and human health. Soybean peroxidase–catalyzed reactions of azo dyes, Acid blue 113 (AB113) and Direct black 38 (DB38), were optimized for color removal using response surface methodology on a Box–Behnken design (BBD). Parameters optimized were H2O2 concentration (mM), pH, and enzyme concentration (U/mL; U is a standard unit of catalytic activity). Optimum conditions for AB113 were pH 4.49, 2.57 mM H2O2, and 1.52 U/mL of enzyme for a predicted 5.6% color remaining (experimental value of 8.1%) and R2 value of 99.68%; and for DB38 the conditions were pH 3.68, 2.92 mM H2O2, and 2.84 U/mL of enzyme for a predicted 3.6% color remaining (experimental value of 5.1%) and R2 value of 99.07%. In addition, the agreement with the one-factor-at-a-time approach was checked. The BBD is a less time-consuming approach that allows identification of interactions between parameters. Kinetic studies (Michaelis–Menten model) quantitatively confirmed the efficiency and effectiveness of enzymatic dye treatment.

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)0.001
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 score0.246
Threshold uncertainty score0.178

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.016
GPT teacher head0.211
Teacher spread0.195 · 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
Published2019
Admission routes2
Has abstractyes

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