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Record W4406217526 · doi:10.5772/intechopen.1008471

Sorption of Phenolic Compounds from Woodwaste Leachate by Peat Media

2024· book-chapter· en· W4406217526 on OpenAlexfundno aff
Najat Kamal, Rosa Galvez‐Cloutier, Gerardo Buelna, Abdelaziz Baçaoui

Bibliographic record

VenueIntechOpen eBooks · 2024
Typebook-chapter
Languageen
FieldEngineering
TopicSlime Mold and Myxomycetes Research
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSorptionChemistryLeachatePhenolEnvironmental chemistryLangmuirFreundlich equationChromatographyOrganic chemistryAdsorptionNuclear chemistry

Abstract

fetched live from OpenAlex

This study contributes to the clarification of sorption mechanism of phenolic compounds in woodwaste leachate by peat, and it is a part of the project which aims to clarify and contribute to determine and evaluate the sorption mechanism part of phenolic compounds in a trickling biofilter. To achieve this objective, mechanisms were studied separately by isolation of each process, and sorption mechanism was followed in the present study by inhibiting the biological process. The kinetic study showed that the maximum sorption capacity was reached between 20 and 24 h at 10°C and between 16 and 20 h at 20°C. However, it is during the first hours that the sorption process is high. The maximum sorption capacity was evaluated at 68.5 mg/kg (57.87% of the initial concentration) for the most polar compounds: 4-nitrophenol, phenol, and 2-chlorophenol and at 35.2 mg/kg of peat for the least polar compounds such as 2,4-dimethylphenol under conditions of pH 4 and at 10°C. The description of sorption results was evaluated by a kinetic and thermodynamic study and modeling by Langmuir and Freundlich isotherm.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.019
GPT teacher head0.224
Teacher spread0.205 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2024
Admission routes1
Has abstractyes

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