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Record W4367625081 · doi:10.3390/molecules28093825

Role of Silica on Clay-Catalyzed Ozonation for Total Mineralization of Bisphenol-A

2023· article· en· W4367625081 on OpenAlexafffund
Farida Boudissa, Vasilica-Alisa Aruş, Eric-Noel Foka-Wembe, Meriem Zekkari, Rachida Ouargli-Saker, David Dewez, René Roy, Abdelkrim Azzouz

Bibliographic record

VenueMolecules · 2023
Typearticle
Languageen
FieldChemistry
TopicZeolite Catalysis and Synthesis
Canadian institutionsÉcole de Technologie SupérieureUniversité du Québec à Montréal
FundersFonds Québécois de la Recherche sur la Nature et les TechnologiesMinistère du Développement Économique, de l’Innovation et de l’Exportation
KeywordsCatalysisChemistryBisphenol AAdsorptionMineralization (soil science)Aqueous solutionMontmorilloniteBentoniteMesoporous silicaChemical engineeringMesoporous materialInorganic chemistryDispersion (optics)Organic chemistryEpoxy

Abstract

fetched live from OpenAlex

Catalytic ozonation for the total mineralization of bisphenol-A (BPA) from aqueous solution was investigated in the presence of various silica-based catalysts such as mesoporous silica, acid-activated bentonite (HMt) and montmorillonite-rich materials (Mt) ion-exchanged with Na+ and Fe2+ cations (NaMt and Fe(II)Mt). The effects of the catalyst surface were studied by correlating the hydrophilic character and catalyst dispersion in the aqueous media to the silica content and BPA conversion. To the best of our knowledge, this approach has barely been tackled so far. Acid-activated and iron-free clay catalysts produced complete BPA degradation in short ozonation times. The catalytic activity was found to strongly depend on the hydrophilic character, which, in turn, depends on the Si content. Catalyst interactions with water and BPA appear to promote hydrophobic adsorption in high Si catalysts. These findings are of great importance because they allow tailoring silica-containing catalyst properties for specific features of the waters to be treated.

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 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 score0.010
Threshold uncertainty score0.316

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 head0.013
GPT teacher head0.240
Teacher spread0.227 · 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

Citations10
Published2023
Admission routes2
Has abstractyes

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