MétaCan
Menu
Back to cohort
Record W2326890574 · doi:10.1021/am5007113

Surfactant-Free Switchable Emulsions Using CO<sub>2</sub>-Responsive Particles

2014· article· en· W2326890574 on OpenAlexaff
Chen Liang, Qingxia Liu, Zhenghe Xu

Bibliographic record

VenueACS Applied Materials & Interfaces · 2014
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsWettingMaterials scienceEmulsionChemical engineeringPulmonary surfactantPickering emulsionParticle (ecology)Air spargingContact anglePhase (matter)Chemical stabilityNanotechnologyOrganic chemistryComposite materialChemistry

Abstract

fetched live from OpenAlex

Surfactant-free emulsions are prepared using bi-wetting particles which occupy the oil-water interface to effectively reduce the oil-water interfacial area. The equilibrium position of the particle at the interface is determined by its wettability. CO2-reponsive chemical functional groups are grafted onto the surface of silica particles. Particles with only CO2-switchable functional groups are capable of stabilizing oil-in-water emulsions. Particles prepared with both CO2-responsive and hydrophobic chemical functional groups on its surface are capable of stabilizing water-in-oil emulsions. Emulsion stability is disturbed when the wettability of the stabilizing particle is altered by introducing CO2 gas to the biphasic mixture, leading to phase separation of emulsions prepared using the functionalized particles. The emulsion stability can be re-established by the removal of CO2 through air sparging. The presence of CO2 imposes positive surface charge to the responsive particles, increasing wettability and, consequently, the ability of the particles to destabilize emulsions.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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 communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.259
Teacher spread0.238 · 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 source (direct Gemma or distilled Codex), 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

Citations72
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueACS Applied Materials & InterfacesSame topicPickering emulsions and particle stabilizationFrench-language works237,207