MétaCan
Menu
Back to cohort
Record W2914108229 · doi:10.1039/c8cc10061f

Scalable synthesis of core–shell microgel particles using a ‘dry water’ method

2019· article· en· W2914108229 on OpenAlexaff
Yang Yang, Wanfen Pu, Xingguang Xu, Bing Wei, Colin D. Wood

Bibliographic record

VenueChemical Communications · 2019
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsKensington Health
FundersSouthwest Petroleum UniversityCommonwealth Scientific and Industrial Research Organisation
KeywordsScalabilityShell (structure)NanotechnologyCore (optical fiber)Materials scienceMicrometerChemical engineeringComputer scienceComposite materialEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

This proof-of-concept study demonstrates a facile and scalable 'dry water' method for producing micrometer-sized microgel particles by use of 'water-in-air' droplets as micro-reactors. Solid microgel particles could be easily produced by this method with no further purification. The microgel particles comprise of porous hydrophobic shells and hydrophilic cores and could absorb both oil and water. The swelling of the particles could be triggered by a surfactant under a wide range of conditions.

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)0.000
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: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.107
Threshold uncertainty score0.268

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 studies0.0000.000
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 head0.072
GPT teacher head0.331
Teacher spread0.260 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueChemical CommunicationsSame topicPickering emulsions and particle stabilizationFrench-language works237,207