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Record W1982709171 · doi:10.5539/jfr.v3n6p70

Advanced Oxidation of Olive Mill Wastewater OMW by an Oxidative Free- Radical Process Induced With Zero Valent Iron

2014· article· en· W1982709171 on OpenAlexvenueno aff
Jamil Rima, Kamil Rahme, Karrine Assaker

Bibliographic record

VenueJournal of Food Research · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicAdvanced oxidation water treatment
Canadian institutionsnot available
Fundersnot available
KeywordsRadicalChemistryHydrogen peroxideZerovalent ironOxygenHydroxyl radicalChemical oxygen demandWastewaterInorganic chemistryNuclear chemistryOrganic chemistryWaste management

Abstract

fetched live from OpenAlex

The degradation of olive mill wastewater (OMW) with hydroxyl radicals generated from zero-valent iron and hydrogen peroxide has been investigated by means of chemical oxygen demand (COD) and phenolic compounds analyses. The mechanism uses zero-valent iron activated by oxygen at room temperature to generate Hydroxyl-free radicals that subsequently oxidize organic constituents and clean wastewater. The effects of the iron mass, the pH and the organic matter concentration have been studied. Our experimental results demonstrate that the continuous presence of oxygen and iron in an acidic solution with a pH 2-4 allows the generation of maximum hydroxyl free radicals. The lack of oxygen prevents oxidative reaction, and then the generation of free radicals cannot occur. Coloration of OMW disappeared and phenolic compound decreased after 40 minutes of reaction time. The results show that Fe0/H+ /air or oxygen, could be considered as an effective alternative solution for the treatment of OMW.

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 score0.002

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)0.0000.000

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 head0.029
GPT teacher head0.324
Teacher spread0.295 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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