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Record W2022684467 · doi:10.1080/09593332708618757

Development of an Integrated Enzymatic Treatment System for Phenolic Waste Streams

2006· article· en· W2022684467 on OpenAlexafffund
Xian-zhong Mao, Ian Buchanan, Johan Stanley

Bibliographic record

VenueEnvironmental Technology · 2006
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEnzyme-mediated dye degradation
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPhenolChemistryAqueous solutionChromatographyFiltration (mathematics)PeroxidaseEnzyme assayWastewaterChemical oxygen demandEnzymeOrganic chemistryWaste management

Abstract

fetched live from OpenAlex

An integrated enzymatic treatment system, which includes Coprinus cinereus peroxidase (CIP) production, processing, and usage in batch or plug flow reactors, is being developed to remove phenolic compounds from the aqueous waste streams. CIP production at bench scale yielded a maximum growth medium activity of approximately 60 U CIP ml(-1). A CIP enzyme solution was prepared for use in treatment by successive filtration steps. This yielded a 4.5-fold increase in enzyme activity, with 87% enzyme activity recovery, and 83% reduction in the solution's Chemical Oxygen Demand. The purity of CIP was observed to have no effect on the ability of the enzyme to remove phenol from the aqueous solutions within the range of enzyme solution purities tested. Contrary to observations reported for phenol removal from buffered solutions, the addition of polyethylene glycol to non-buffered reaction solutions had no positive effect on the phenol removal accomplished at pH 7 in these experiments. The efficiency of enzyme use in a plug flow reactor was improved by step additions of CIP and H2O2.

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 score0.232
Threshold uncertainty score0.263

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 head0.008
GPT teacher head0.184
Teacher spread0.176 · 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

Citations7
Published2006
Admission routes2
Has abstractyes

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