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Record W2781160989 · doi:10.1098/rsta.2017.0039

Investigation of the formation mechanisms in high internal phase Pickering emulsions stabilized by cellulose nanocrystals

2017· article· en· W2781160989 on OpenAlex
Chuanwei Miao, Mani Tayebi, Wadood Y. Hamad

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePhilosophical Transactions of the Royal Society A Mathematical Physical and Engineering Sciences · 2017
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsFPInnovations
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPickering emulsionEmulsionChemical engineeringPhase (matter)Materials scienceCelluloseOil dropletNanocrystalMorphology (biology)ChemistryNanotechnologyOrganic chemistry

Abstract

fetched live from OpenAlex

Medium and high internal phase Pickering emulsions stabilized by cellulose nanocrystals (CNCs) have been prepared and the effects of CNC concentration and type of oil phase on the properties of emulsions were studied. The maximum oil phase volume that can be stabilized by CNCs is 87% when the CNC concentration is 0.6 wt.%; this slightly decreases to 83% when the CNC concentration is increased to 1.2 wt.% or higher. In addition, the oil droplets stabilized with 0.6 wt.% CNC suspensions have a larger size than those stabilized with higher concentration CNC suspensions. As evidenced by the change in oil droplet morphology and size, two different emulsion formation mechanisms are proposed. For a CNC concentration of 0.6 wt.%, the extra oil added into the emulsion is accommodated by the expansion of oil droplet size, whereas for CNC concentrations of 1.2 wt.% and higher, the oil is stabilized mainly by the formation of new oil droplets.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.606
Threshold uncertainty score0.300

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 studies0.0000.001
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 head0.024
GPT teacher head0.254
Teacher spread0.230 · 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