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Record W2414499363 · doi:10.1021/acsami.5b05093

Synthesis of Surface-Responsive Composite Particles by Dehydration of Water-in-Oil Emulsions

2015· article· en· W2414499363 on OpenAlexafffund
Chen Liang, Qingxia Liu, Zhenghe Xu

Bibliographic record

VenueACS Applied Materials & Interfaces · 2015
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsUniversity of Alberta
FundersNatural Sciences and Engineering Research Council of CanadaAlberta Innovates
KeywordsMaterials scienceComposite numberEmulsionChemical engineeringCarboxymethyl celluloseAdsorptionAqueous solutionPolymerComposite materialOrganic chemistrySodiumChemistry

Abstract

fetched live from OpenAlex

Organic composite particles were prepared by first emulsifying an aqueous sodium carboxymethyl cellulose (CMC) solution in a nonaqueous ethylcellulose (EC) solution, followed by dehydrating emulsified water droplets. CMC and EC are both biodegradable nontoxic materials, but have contrasting properties. CMC is a charged water-soluble polymer, while EC is an uncharged interfacially active water-insoluble polymer. The simple preparative method does not consume unnecessary chemical reagents and produces no waste material. The composite particles prepared by dehydrating emulsion droplets are readily dispersed in organic media due to its biwettable surface terminated with interfacially active EC molecules, which allows composite particles to preferentially adsorb at the oil-water droplet interface. The surface of composite particles, furthermore, is water-permeable, which allows water to be absorbed from emulsified droplets. The size, composition, and structure of the synthesized composite particles are ideally suited for absorption of stabilized water droplets from oil-continuous emulsions. The use of the composite absorbent particles, described herein, presents another viable strategy for dewatering water-in-oil 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 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)0.000
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 score0.004
Threshold uncertainty score0.527

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.020
GPT teacher head0.253
Teacher spread0.233 · 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
Published2015
Admission routes2
Has abstractyes

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