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Record W2007867492 · doi:10.1021/la049535m

Crust Effect on Multiscale Pattern Formations in Drying Micelle Solution Drops on Solid Substrates

2004· article· en· W2007867492 on OpenAlexaff
Xun Ma, Yan Xia, Er‐Qiang Chen, Yongli Mi, Xiaorong Wang, An‐Chang Shi

Bibliographic record

VenueLangmuir · 2004
Typearticle
Languageen
FieldEngineering
TopicNanomaterials and Printing Technologies
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMicellePolystyreneMaterials scienceCopolymerEvaporationPolymerSolventPerpendicularChemical engineeringComposite materialChemistryGeometryThermodynamicsOrganic chemistry

Abstract

fetched live from OpenAlex

Spherical micelles of a polystyrene-b-poly(dimethylsiloxane) (PS-b-PDMS) diblock copolymer with the number-average molecular weight of 193 000 g/mol for PS and 39 000 g/mol for PDMS were obtained by using n-dodecane or n-octane as the selective solvent for the PDMS block. The drying process of micelle solution drops with relatively high polymer concentration on solid substrates and the resultant drying patterns were studied using optical microscopy and atomic force microscopy. The drying drops exhibited an inner solution "cap" connecting with an outer gelled "foot" through a transition zone. A crust was first formed on the surface of the transition zone and remained on the top of the foot region. An inhomogeneous stress perpendicular to the radial direction within the crust, which was due to the solvent evaporation accompanied by the receding of the solution cap, induced regular 45 degrees -tilted stripes (pleats) in the transition zone and main radial cracks in the foot region. The stripes and cracks have periods of a few and tens of microns, respectively. Concave micelle "bricks" were also observed between cracks. In addition to micelle close packing, these patterns demonstrate that drying micelle solution drops may provide a potential means to manipulate fine and multiscale structures for technological applications.

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 score0.210
Threshold uncertainty score0.378

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 head0.009
GPT teacher head0.223
Teacher spread0.213 · 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

Citations22
Published2004
Admission routes1
Has abstractyes

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