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Record W2017361785 · doi:10.1002/jctb.571

Impact of dissolved wastewater constituents on peroxidase‐catalyzed treatment of phenol

2002· article· en· W2017361785 on OpenAlexafffund
Monika Wagner, Jim A. Nicell

Bibliographic record

VenueJournal of Chemical Technology & Biotechnology · 2002
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsChemistryPhenolSulfideCyanideHydrogen peroxideThiosulfateInorganic chemistryDistilled waterSulfurNuclear chemistrySodium thiosulfateHydrogen sulfideCatalysisHorseradish peroxidaseOrganic chemistryChromatographyEnzyme

Abstract

fetched live from OpenAlex

Abstract The impact of dissolved wastewater constituents on the treatment of synthetic phenol solutions using horseradish peroxidase (HRP) and hydrogen peroxide was investigated under a variety of reaction conditions. The constituents studied included various inorganic salts, organic compounds and heavy metals. Higher H 2 O 2 doses were required to treat phenol in the presence of sodium sulfite, thiosulfate and sulfide; however, enhanced levels of phenol conversion were achieved once sufficient H 2 O 2 was supplied. Sulfide and cyanide inhibited phenol transformation. The inhibition of sulfide was overcome by supplying sufficient H 2 O 2 to oxidize the sulfide to sulfur. However, increasing the H 2 O 2 dose was ineffective in attempting to overcome the strong inhibiting effect of cyanide. Among the heavy metal ions tested, only Mn(II) substantially inhibited phenol removal when it was present at a concentration of 1 mmol dm −3 . The presence of inorganic salts including NaCl, CaCl 2 , MgCl 2 , NH 4 Cl and (NH 4 ) 2 SO 4 reduced phenol conversion as compared with the treatment in distilled‐deionized water. This can be attributed to the increased ionic strength of the solution. © 2002 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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.030
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.258
Teacher spread0.244 · 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.

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

Citations34
Published2002
Admission routes2
Has abstractyes

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