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Record W2090985154 · doi:10.1021/la803449z

Study of the Sol−Gel Reaction Mechanism in Supercritical CO<sub>2</sub> for the Formation of SiO<sub>2</sub> Nanocomposites

2009· article· en· W2090985154 on OpenAlexaff
Paul A. Charpentier, Xinsheng Li, Ruohong Sui

Bibliographic record

VenueLangmuir · 2009
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsWestern University
Fundersnot available
KeywordsNanocompositeSol-gelSupercritical fluidChemical engineeringAcetic acidSupercritical dryingFourier transform infrared spectroscopyPolymerMaterials scienceHydrolysisAerogelOligomerPolymer chemistryX-ray photoelectron spectroscopyNanoparticleSiliconSolventChemistryOrganic chemistryNanotechnology

Abstract

fetched live from OpenAlex

Direct sol-gel reactions in supercritical CO2 (scCO2) have attracted significant interest for synthesizing nanomaterials by reacting alkoxides with a carboxylic acid. In this study, the hydrolysis of silicon alkoxides (TEOS or TMOS) was carried out using scCO2 as the solvent to generate silica nanoparticles within the matrix of polyethylene for the synthesis of polymeric nanocomposites. This methodology provides advantages of combining the sol-gel reactions and drying into a one-step process for producing polymer nanocomposites. The synthesized polymer silica composites were characterized by SEM, FTIR, and XPS. When the TEOS loading was below 10 wt % Si content, nanometer-sized silica particles were formed that were well dispersed within the polyethylene matrix. The mechanism of the silicon alkoxides reacting with acetic acid in scCO2 was further studied using online GC-MS and offline 13C NMR. Oligomer structures with a bridging methoxyl group between the two silicon atoms and the acetate monodentate were observed. This study suggests a new sol-gel pathway in scCO2 that is different from the hydrolysis-condensation reactions using the conventional sol-gel process.

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 score0.087
Threshold uncertainty score0.366

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 head0.012
GPT teacher head0.232
Teacher spread0.220 · 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

Citations24
Published2009
Admission routes1
Has abstractyes

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