MétaCan
Menu
Back to cohort
Record W2162344851 · doi:10.1002/cjce.22271

Polydimethylsiloxane microspheres with poly(methyl methacrylate) coating: Modelling, preparation, and characterization

2015· article· en· W2162344851 on OpenAlexvenueno aff
Baoguang Ma, Jens Henrik Hansen, Søren Hvilsted, Anne Ladegaard Skov

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2015
Typearticle
Languageen
FieldEngineering
TopicFluid Dynamics and Mixing
Canadian institutionsnot available
Fundersnot available
KeywordsPolydimethylsiloxaneMicrosphereCharacterization (materials science)CoatingMaterials scienceMethacrylateMethyl methacrylateMonolithChemical engineeringPolymer chemistryPolymer scienceComposite materialNanotechnologyPolymerChemistryOrganic chemistryCopolymerCatalysisEngineering

Abstract

fetched live from OpenAlex

Polydimethylsiloxane (PDMS) microspheres are prepared by mixing homogeneous dispersions of vinyl‐functional PDMS and a curing agent using mechanical stirring in a series of aqueous solutions, and curing at 80 °C for 2 h. In order to verify the experimental diameter and size distributions of the PDMS microspheres, the Hinze‐Kolmogorov theory is applied to predict the mean diameter, and a population balance model as well as the maximum entropy formalism are used to describe the size distribution. Close agreement is found between experimental and theoretical results. Furthermore, vinyl functional PDMS microspheres were coated with poly(methyl methacrylate) (PMMA) by spin coating with different concentrations of PMMA solutions. The quality of the resulting PMMA shell is investigated using rheological measurements at 50 °C with a time‐sweep procedure. The results strongly suggest that PMMA‐coated PDMS microspheres react around 20 times slower than the uncoated ones, and that the PMMA shell significantly hinders the reaction between the PDMS microsphere and cross‐linker. Thus the thin PMMA shells are very efficient in protecting the reactive PDMS microspheres, since the PMMA shell forms an impermeable barrier up to 50 °C.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.280
Threshold uncertainty score0.419

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 head0.009
GPT teacher head0.182
Teacher spread0.173 · 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 designSimulation or modeling
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

Citations1
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueThe Canadian Journal of Chemical EngineeringSame topicFluid Dynamics and MixingFrench-language works237,207