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Record W4210734371 · doi:10.1016/j.ces.2022.117493

A multiscale approach for the integrated design of emulsified cosmetic products

2022· article· en· W4210734371 on OpenAlexaff
Fernando Calvo, Jorge M. Gómez, Luis Ricardez‐Sandoval, Óscar Álvarez

Bibliographic record

VenueChemical Engineering Science · 2022
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsProcess engineeringBiochemical engineeringComputer scienceChemistryEngineering

Abstract

fetched live from OpenAlex

This study applies a multiscale approach to the integrated design of emulsified cosmetic products to gain insight on the relationships between phenomena taking place at different scales. This work links the elastic modulus, textural properties, product formulation, and emulsification energy with the emulsion's microscopic structure. Also, this study establishes functional relationships between cosmetic emulsions' rheological and textural properties to gain deeper insight on these systems. Oil-in-water cosmetic emulsions were manufactured by varying the thickener type and concentration, dispersed phase concentration, and agitation rate during the emulsification process. The results indicate that when a specific polymer's concentration is reached, the macroscopic and microscopic properties of the cosmetic emulsions do not exhibit significant variations due to the polymeric matrix that thickeners generate in the continuous phase. According to these results, there is a critical thickener concentration for the design of cosmetic emulsions, which depends on the polymer selected to formulate the product.

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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.394
Threshold uncertainty score0.214

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.030
GPT teacher head0.239
Teacher spread0.209 · 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

Citations19
Published2022
Admission routes1
Has abstractyes

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