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Record W2314679564 · doi:10.1021/la1038427

A New Method for the Preparation of Concentrated Translucent Polymer Nanolatexes from Emulsion Polymerization

2010· article· en· W2314679564 on OpenAlexaff
Niels M. B. Smeets, Raul P. Moraes, Jeffery A. Wood, Timothy Fl Mckenna

Bibliographic record

VenueLangmuir · 2010
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsQueen's University
Fundersnot available
KeywordsEmulsion polymerizationPolymerNucleationEmulsionPolymerizationMaterials scienceMonomerColloidChemical engineeringParticle (ecology)Particle sizePolymer chemistryParticle-size distributionOptical transparencyChemistryComposite materialOrganic chemistry

Abstract

fetched live from OpenAlex

A novel method for the preparation of concentrated, colloidally stable, translucent polymer nanolatexes is presented. Herein nanolatexes are obtained from emulsion polymerization, utilizing the potential of catalytic chain transfer to enhance the particle nucleation efficiency. Low amounts of emulsifier are required (<8% w/w based on monomer) while the nanolatexes concentration can be increased to 40% w/w. The nanolatexes are translucent in appearance, which was correlated to the average particle size and width of the particle size distribution using Mie theory. Increasing the nanolatex concentration was found to have no deteriorating effect on either the optical or colloidal properties. Preparing translucent nanolatexes via this method is advantageous, as the amount of emulsifier is significantly reduced without sacrificing the optical transparency or the high interfacial surface area of the polymer colloids.

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: none
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0010.001

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 head0.008
GPT teacher head0.290
Teacher spread0.282 · 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

Citations18
Published2010
Admission routes1
Has abstractyes

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