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Record W2047361296 · doi:10.1002/macp.201400522

CO<sub>2</sub>‐Redispersible Polymer Latexes with Low Glass Transition Temperatures

2015· article· en· W2047361296 on OpenAlexafffund
Dan Gariepy, Qi Zhang, Shiping Zhu

Bibliographic record

VenueMacromolecular Chemistry and Physics · 2015
Typearticle
Languageen
FieldChemical Engineering
TopicCarbon dioxide utilization in catalysis
Canadian institutionsMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComonomerPolymerMethyl methacrylateEmulsion polymerizationPolymer chemistryMaterials scienceGlass transitionMonomerChemical engineeringMethacrylateAcrylatePolymerizationEmulsionComposite material

Abstract

fetched live from OpenAlex

In this work, acrylic latexes are prepared through surfactant‐free emulsion polymerization of methyl methacrylate (MMA) and butyl acrylate (BA). CO 2 ‐responsive 2‐(diethyl)aminoethyl methacrylate (DEAEMA) is used as a co‐monomer. The resulting latexes can be easily coagulated by adding a small amount of caustic soda. Once washed, coagulated particles could be redispersed into water to prepare stable latexes with CO 2 and ultrasonication. CO 2 ‐redispersibility of the latexes is examined as a function of glass transition temperature of the polymers having different MMA/BA ratios. It is found that, while high MMA content latexes are easily CO 2 ‐redispersed, it is challenging to re‐disperse high BA content latexes. For latex particles with T g below ambient conditions, coagulation with caustic soda leads to the fusion of individual particles, and the particles are no longer redispersible. This work provides a mechanistic insight and practical guidance for the applicability of CO 2 ‐responsive amine‐containing molecules (employed as comonomer, initiator, surfactant, and so on) in developing CO 2 ‐redispersible latex products. image

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 score0.012
Threshold uncertainty score0.868

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 head0.005
GPT teacher head0.197
Teacher spread0.192 · 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

Citations15
Published2015
Admission routes2
Has abstractyes

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