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Record W4283021606 · doi:10.1021/acs.macromol.2c00489

Amphiphilic Block-Random Copolymer Stabilizers: A “Seeded-Coagulative” Emulsion Polymerization Mechanism

2022· article· en· W4283021606 on OpenAlexaff
Sean R. George, Connor A. Sanders, Gary A. Deeter, J. D. Campbell, Bernd Reck, Michael F. Cunningham

Bibliographic record

VenueMacromolecules · 2022
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsQueen's University
FundersBASF Corporation
KeywordsEmulsion polymerizationCopolymerPolymer chemistryPolymerizationDispersion polymerizationChemical engineeringPolystyreneEmulsionMaterials scienceStabilizer (aeronautics)Particle (ecology)Particle sizeChemistryPolymerOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Polystyrene-b-[polystyrene-r-poly(acrylic acid)] block-random copolymers are effective stabilizers of emulsion polymerizations but exhibit unusual dispersion behavior, self-assembling with low aggregation numbers or possibly self-folding into single-chain nanoparticles. In emulsion polymerizations stabilized by these block-random copolymers, the total final particle surface area was directly proportional to the stabilizer concentration and was unaffected by kinetically driven processes such as initiation and particle growth rate; this behavior is not explained by any known emulsion polymerization mechanism. A “seeded-coagulative” emulsion polymerization mechanism is proposed, in which particles grow by polymerization and continuous coagulation, conserving total surface area. Mathematical equations for this proposed mechanism fit the experimental data well and explain the observed dependence of particle number on stabilizer concentration (Np ∼ [S]3) and solids content (Np ∼ τ–2). The novel emulsion polymerization mechanism is attributed to the unique properties of the stabilizers used.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
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 score0.011
Threshold uncertainty score1.000

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 studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.000

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 head0.222
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.

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
Published2022
Admission routes1
Has abstractyes

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