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Record W1996111005 · doi:10.1002/cjce.21939

Non‐aqueous, surfactant‐free antifoam emulsions: Properties and triggered release

2013· article· en· W1996111005 on OpenAlexvenueno aff
Tatiana D. Dimitrova, Séverine Cauvin, Jean‐Paul Lecomte, Annick Colson

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2013
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsnot available
Fundersnot available
KeywordsDefoamerEmulsionSiliconePulmonary surfactantChemical engineeringAqueous solutionAqueous two-phase systemStabilizer (aeronautics)ChemistryPhase (matter)Materials scienceChromatographyOrganic chemistryDispersant

Abstract

fetched live from OpenAlex

Abstract Processing convenience and formulation flexibility frequently require the delivery of the silicone oils as emulsions. The shelf life of the latter is achieved kinetically, in the most cases via the addition of surfactants. On the other hand, surfactants are the subject of increasing scrutiny with regard to their environmental impact. The goal of this study is to formulate silicone oils in surfactant‐free emulsions and to demonstrate the controlled release of the active silicone material. A non‐aqueous silicone emulsion comprising of a continuous phase of a polar organic liquid, having droplets of silicone antifoam compound dispersed therein, have been developed. These systems are stabilised by (fractal) waxy particles which play a dual role. They act as Pickering stabilisers and in the same time they form an elastic network in the continuous phase, providing a creaming stability of more than a year. A triggered release of the (antifoam) silicone active can be achieved via heating above the melting temperature of the waxy particles. This is demonstrated by the fact that no antifoam activity has been observed at temperatures below ca. 60°C, while at temperature of above 65–70°C a strong antifoam effect has been obtained.

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 score0.041
Threshold uncertainty score0.311

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 head0.183
Teacher spread0.168 · 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

Citations6
Published2013
Admission routes1
Has abstractyes

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