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Record W1985366291 · doi:10.1002/pen.22126

Nanoparticle encapsulation by a polymer via <i>in situ</i> polymerization in supercritical conditions

2011· article· en· W1985366291 on OpenAlexafffund
Babak Esmaeili, Jamal Chaouki, Charles Dubois

Bibliographic record

VenuePolymer Engineering and Science · 2011
Typearticle
Languageen
FieldEngineering
TopicPhase Equilibria and Thermodynamics
Canadian institutionsPolytechnique Montréal
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials sciencePolymerNanoparticlePolymerizationX-ray photoelectron spectroscopyThermogravimetric analysisSupercritical fluidChemical engineeringEmulsion polymerizationIn situ polymerizationTransmission electron microscopyComposite materialPolymer chemistryNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract The encapsulation of aluminum nanoparticles by polyvinylidene fluoride (PVDF) was carried out in supercritical conditions via in situ polymerization. The aluminum particles possessed an average diameter of 43.7 nm. The presence of PVDF on the particles was validated by thermogravimetric analysis (TGA). This result was further approved by X‐ray photoelectron spectroscopy (XPS), which showed high intensity peaks of fluorine and carbon on the particles after the encapsulation process, which are associated with the presence of hydrocarbon‐based PVDF. As observed by transmission electron microscopy (TEM) images, the nanoparticles were uniformly coated by a polymer of a few nanometers in thickness. The results showed that there is a good consistency between the calculated thickness of the polymer coating and the results obtained by TEM. In addition, the effect of polymerization time on the kinetics of the reaction was investigated. Finally, it was found that the thickness of the polymer layer can be controlled by the duration of the encapsulation process. POLYM. ENG. SCI., 2012. © 2011 Society of Plastics Engineers

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 score0.355
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.007
GPT teacher head0.198
Teacher spread0.191 · 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

Citations12
Published2011
Admission routes2
Has abstractyes

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