MétaCan
Menu
Back to cohort
Record W992168341 · doi:10.3166/acsm.35.151-168

Synthesis of mesoporous silica with tailored porosity under wide-ranging conditions

2010· article· en· W992168341 on OpenAlexvenueno aff
Fatma Fakhfakh, Leila Baraket, José M. Fraile, José A. Mayoral, Abdelhamid Ghorbel

Bibliographic record

VenueAnnales de Chimie Science des Matériaux · 2010
Typearticle
Languageen
FieldMaterials Science
TopicMesoporous Materials and Catalysis
Canadian institutionsnot available
Fundersnot available
KeywordsRangingPorosityMesoporous materialDose-ranging studyMaterials scienceMesoporous silicaComputer scienceComposite materialChemistryOrganic chemistryMedicineTelecommunications

Abstract

fetched live from OpenAlex

[FR]: Le but de ce travail est de préparer des silices mésoporeuses ayant une porosité développée par la méthode sol-gel. L’effet de certains paramètres de synthèse tels que la nature du catalyseur, le solvant, le rapport molaire catalyseur/silicium, l’emploi de surfactant et la température a été étudié. L’utilisation d’une grande quantité de HCl dans le mélange réactionnel tétraéthyl orthosilicate (TEOS), propanol et H2O favorise la formation de pores de 93 Ǻ de diamètre. En présence du surfactant poly(propylène glycol) poly(éthylène glycol) poly(propylène glycol), le diamètre des pores de la silice est améliorée et atteint 122 Ǻ. Par ailleurs, quand CH3COOH est employé, la porosité est égale à 54 Ǻ. En présence du surfactant, le diamètre des pores augmente et atteint 78 Å. L’augmentation à 60 °C de la température du mélange formé d’une faible quantité de CH3COOH, propanol et TEOS conduit à la formation de pores de 120 Å de diamètre. Les pores deviennent plus larges en présence du surfactant pour atteindre 175 Ǻ. Par ailleurs, l’utilisation de l’acide propanoïque dans les mêmes conditions favorise l’obtention des pores les plus larges de diamètre 289 Ǻ.

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)0.0010.000

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 head0.013
GPT teacher head0.250
Teacher spread0.237 · 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

Citations3
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueAnnales de Chimie Science des MatériauxSame topicMesoporous Materials and CatalysisFrench-language works237,207