MétaCan
Menu
Back to cohort
Record W2279625261 · doi:10.1089/ees.2015.0383

Additive Effect on Soybean Peroxidase-Catalyzed Removal of Anilines from Water

2016· article· en· W2279625261 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnvironmental Engineering Science · 2016
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicEnzyme-mediated dye degradation
Canadian institutionsUniversity of Windsor
FundersUniversity of Windsor
KeywordsChemistrySodium dodecylbenzenesulfonateSodium perboratePolyethylene glycolPeroxidasePEG ratioAnilineSodium dodecyl sulfateCatalysisPolymerizationWastewaterNuclear chemistryChromatographyHydrogen peroxideOrganic chemistryEnzymePolymerAqueous solutionWaste management

Abstract

fetched live from OpenAlex

Soybean peroxidase has been shown to be effective in removal of aromatic compounds from wastewater, while the use of additives effectively reduces enzyme concentration requirement, hence overall treatment cost. Enzymatic treatment, an oxidative polymerization, was successful in removal of over 95% of both aniline and o-anisidine. The originality of this study lies in the findings that the additives, sodium dodecyl sulfate (SDS), sodium dodecylbenzenesulfonate (SDBS), Triton X-100, and sodium dodecanoate (SDOD), reduced enzyme concentration requirement, while polyethylene glycol (PEG, average molar mass of 3350 g/mol) had no effect on the required enzyme concentration. In addition, the presence of SDS also enhanced treatment by improving precipitation and color removal. These results are enabling advancement of soybean peroxidase-catalyzed treatment of anilines found in wastewaters as a new sustainable method.

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.

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 score0.090
Threshold uncertainty score0.434

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 head0.004
GPT teacher head0.164
Teacher spread0.159 · 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