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Record W2132747256 · doi:10.1039/c0sm00127a

Formation of crystalline colloidal arrays by anionic and cationic polystyrene particles

2010· article· en· W2132747256 on OpenAlexafffund
Gwénaëlle Bazin, X. X. Zhu

Bibliographic record

VenueSoft Matter · 2010
Typearticle
Languageen
FieldPhysics and Astronomy
TopicPhotonic Crystals and Applications
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCationic polymerizationDispersityPolystyreneIonic strengthChemical engineeringParticle sizeColloidMaterials scienceIonic bondingParticle (ecology)ComonomerEmulsion polymerizationPolymer chemistryPolymerizationChemistryAqueous solutionIonComposite materialOrganic chemistryPolymer

Abstract

fetched live from OpenAlex

Cross-linked polystyrene microspheres bearing positive and negative charges have been prepared by surfactant-free emulsion polymerization by the use of various amounts of vinylbenzyltrimethylammonium chloride and sodium styrenesulfonate as comonomers, respectively. Increasing the amount of the ionic comonomers tends to decrease the particle size due to better surface stabilization, but a high concentration of an ionic comonomer leads to a competitive mechanism that increases the polydispersity of the particle size. The amount of cationic and anionic comonomers leads to differences in size, shape and uniformity of the particles. The cationic particles can self-assemble into crystalline colloidal arrays with intense visible light diffraction just like the anionic ones. It is particularly interesting to observe that good packing can be obtained even for particles not quite uniform in size. To better understand the packing behavior, the properties and stability of the colloidal crystals have been studied as a function of the particle concentration and ionic strength of the media. The presence of charges helps in the formation of periodic structure over a wide range of particle concentrations at low ionic strength.

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 score0.074
Threshold uncertainty score0.802

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)0.0010.000

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 head0.004
GPT teacher head0.217
Teacher spread0.213 · 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
Published2010
Admission routes2
Has abstractyes

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