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Record W2560113971 · doi:10.1021/acs.langmuir.6b03358

Fabrication of Highly Porous Nonspherical Particles Using Stop-Flow Lithography and the Study of Their Optical Properties

2016· article· en· W2560113971 on OpenAlexafffund
Minggan Li, Dehi Joung, Janusz A. Koziński, Dae Kun Hwang

Bibliographic record

VenueLangmuir · 2016
Typearticle
Languageen
FieldMaterials Science
TopicPickering emulsions and particle stabilization
Canadian institutionsYork UniversityToronto Metropolitan UniversitySt. Michael's Hospital
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrofluidicsMaterials sciencePorosityLithographyNanotechnologyPorous mediumJanus particlesMicroreactorPolymerizationPhotolithographyFabricationChemical engineeringUltravioletJanusComposite materialChemistryPolymerOptoelectronics

Abstract

fetched live from OpenAlex

A microfluidic flow lithography approach was investigated to synthesize highly porous nonspherical particles and Janus particles in a one-step and high-throughput fashion. In this study, using common solvents as porogens, we were able to synthesize highly porous particles with different shapes using ultraviolet (UV) polymerization-induced phase separation in a microfluidic channel. We also studied the pore-forming process using operating parameters such as porogen type, porogen concentration, and UV intensity to tune the pore size and increase the pore size to submicron levels. By simply coflowing multiple streams in the microfluidic channel, we were able to create porous Janus particles; we showed that their anisotropic swelling/deswelling exhibit a unique optical shifting. The distinctive optical properties and the enlarged surface area of the highly porous particles can improve their performance in various applications such as optical sensors and drug loading.

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 score0.282
Threshold uncertainty score0.098

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 science0.0000.000
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 head0.035
GPT teacher head0.246
Teacher spread0.211 · 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

Citations15
Published2016
Admission routes2
Has abstractyes

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