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Record W2138663803 · doi:10.1039/c4sm02215g

Study of miniemulsion formulation containing 1-octyl-3-methylimidazolium hexafluorophosphate for its application in low-emitting coating products

2014· article· en· W2138663803 on OpenAlexaff
Yiyang Kong, Binjie Hu, Kwang‐Leong Choy, Xiaoyu Li, Guangdi Chen

Bibliographic record

VenueSoft Matter · 2014
Typearticle
Languageen
FieldChemical Engineering
TopicIonic liquids properties and applications
Canadian institutionsDiscovery Centre
FundersNatural Science Foundation of Ningbo
KeywordsHexafluorophosphateIonic liquidMiniemulsionCoatingMelting pointChemical engineeringMaterials scienceChemistryOrganic chemistryPolymerCatalysisPolymerization

Abstract

fetched live from OpenAlex

Room temperature ionic liquids (RTILs) are non-volatile organic salts, and few of them with low melting point may replace the conventional coalescing agents in waterborne coatings, thus preventing volatile organic compounds (VOCs) emission, caused by coalescing agents. The formation of waterborne coating containing RTILs can be achieved by the encapsulation of RTILs inside latexes via miniemulsion polymerization. Achieving a stable miniemulsion is a crucial step for further polymerization. In this study, 1-octyl-3-methylimidazolium hexafluorophosphate (C8mimPF6) was chosen, and various factors which might affect droplet size and its stability, including surfactant type, surfactant concentration, and C8mimPF6 concentration, were investigated. It was found that the presence of a small amount of C8mimPF6 coupled with the surfactant would offer marked effects on the droplet size reduction and droplet stability. Such effect may reach its maximum from 1 to 5 wt% C8mimPF6. Above the critical concentration, adding more C8mimPF6 to the oil phase may cause a larger initial droplet size as well as weaken the droplet stability. Such observations were consistent with the zeta potential measurements for miniemulsions prepared under similar conditions.

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 score0.310
Threshold uncertainty score0.535

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 head0.015
GPT teacher head0.251
Teacher spread0.236 · 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

Citations4
Published2014
Admission routes1
Has abstractyes

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