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Record W2051936182 · doi:10.1002/cjce.21985

Optimization of the formulation of water in oil emulsions entrapping polysaccharide by increasing the amount of water and the stability

2014· article· en· W2051936182 on OpenAlexvenueno aff
Endarto Yudo Wardhono, Andrea Zafimahova‐Ratisbonne, Jean‐Louis Lanoisellé, Khashayar Saleh, Danièle Clausse

Bibliographic record

VenueThe Canadian Journal of Chemical Engineering · 2014
Typearticle
Languageen
FieldChemical Engineering
TopicRheology and Fluid Dynamics Studies
Canadian institutionsnot available
Fundersnot available
KeywordsEmulsionChemical engineeringAqueous two-phase systemChromatographyPulmonary surfactantMaterials scienceThermal stabilityPolysaccharideAqueous solutionChemistryOrganic chemistry

Abstract

fetched live from OpenAlex

Water in oil emulsions entrapping a polysaccharide have been formulated and the purpose of this study is first to increase the amount of dispersed water and the amount of polysaccharide. The second part of the study is devoted to check the stability of the obtained emulsion over 2 years. Encapsulation of polysaccharide was realized by introducing the aqueous phase (containing the required polysaccharide and glycerol) into a stirred oil phase (wherein the polyglycerol polyricinoleate (PGPR) as the surfactant has been previously dissolved). Emulsion stability tests were carried out immediately after preparation and after ageing tests. For that purpose the emulsions were submitted to cooling and heating cycles performed in a calorimeter in order to detect the freezing and melting temperatures of the dispersed water. Due to nucleation phenomena, the delay between freezing and melting is correlated to the way the water is dispersed within the emulsion and therefore gives information about the stability of the emulsions. To complete these tests other ones were performed such as bottle test, laser diffraction granulometry and rheometry. It was shown that it is possible to get emulsions containing 75% of water, showing the required stability and flow ability.

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)0.001
metaresearch head score (Gemma)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.343
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.004
GPT teacher head0.167
Teacher spread0.163 · 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

Citations18
Published2014
Admission routes1
Has abstractyes

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