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Record W1966716072 · doi:10.1002/jctb.754

Impact of the presence of solids on peroxidase‐catalyzed treatment of aqueous phenol

2003· article· en· W1966716072 on OpenAlexaff
Monika Wagner, Jim A. Nicell

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2003
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEnzyme-mediated dye degradation
Canadian institutionsMcGill University
Fundersnot available
KeywordsBentonitePhenolChemistryPeatAqueous solutionCellulosePeroxidaseNuclear chemistryHydrogen peroxidePhenolsOrganic chemistryChemical engineeringEnzyme

Abstract

fetched live from OpenAlex

Abstract The impact of the presence of solids on the treatment of aqueous solutions of phenol using horseradish peroxidase (HRP) and hydrogen peroxide was investigated. The solids studied were silica gel, kaolin, bentonite, cellulose and peat moss. Kaolin, bentonite, cellulose and peat moss enhanced phenol transformation at pH 5.0 and 7.0 starting at concentrations of 100, 1000 or 10 000 mg dm −3 . At pH 9.0, bentonite and kaolin had negative impacts when present at 10 000 mg dm −3 and peat moss when present at 1000 mg dm −3 and 10 000 mg dm −3 . In the case of bentonite and peat moss, the enhancing effects at pH 7.0 were associated with the dissolved or colloidal constituents, while in the case of kaolin, the enhancing effects were due to the solid material. Freshly made bentonite suspensions inactivated the peroxidase enzyme; however aged bentonite suspensions and their supernatants did not affect enzyme stability. H 2 O 2 was unstable in solutions containing peat moss constituents. Phenolic solutions treated in the presence of bentonite, kaolin and peat moss were significantly less toxic than the controls, indicating that these materials were able to interact with and partially neutralize precursors of toxic reaction products. Copyright © 2003 Society of Chemical Industry

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 score0.026
Threshold uncertainty score0.707

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
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 head0.012
GPT teacher head0.242
Teacher spread0.230 · 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

Citations21
Published2003
Admission routes1
Has abstractyes

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