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Record W2328042270 · doi:10.1021/mz3003165

Morphology, Thickness, and Composition Evolution in Supramolecular Block Copolymer Films over a Wide Range of Dip-Coating Rates

2012· article· en· W2328042270 on OpenAlexafffund
Sébastien Roland, Robert E. Prud’homme, C. Géraldine Bazuin

Bibliographic record

VenueACS Macro Letters · 2012
Typearticle
Languageen
FieldMaterials Science
TopicBlock Copolymer Self-Assembly
Canadian institutionsUniversité de Montréal
FundersNatural Sciences and Engineering Research Council of CanadaFonds Québécois de la Recherche sur la Nature et les Technologies
KeywordsCopolymerDip-coatingCoatingMaterials scienceMorphology (biology)PolystyreneChemical engineeringSupramolecular chemistrySolventThin filmPolymer chemistryMoleculeComposite materialNanotechnologyOrganic chemistryChemistryPolymer

Abstract

fetched live from OpenAlex

Dip-coating, an important industrial technique, has been underexploited for preparing block copolymer (BC) thin films, such that the knowledge regarding their general characteristics is limited. Here, we present an overview of the crucial factors that determine how BC film morphology evolves as a function of dip-coating rate (withdrawal speed) over a wide range, illustrated using THF solutions of a polystyrene- b -poly(4-vinyl pyridine) (PS-P4VP) diblock copolymer mixed with two small molecules, naphthol and naphthoic acid, which are hydrogen-bonders with P4VP. Key factors in determining the film morphology are the systematic variation in film thickness and, for supramolecular BCs, in film composition with dip-coating rate. The former shows a general V-shaped dependence, related to the so-called capillarity and draining regimes identified previously for dip-coated sol–gel films. The relative small molecule content in the films studied is shown to increase in the capillarity regime from low to that of the dip-coating solution and thereafter to remain constant. Together, these changes, in addition to solvent and other effects, determine the film morphology and its evolution with dip-coating rate.

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 score0.004
Threshold uncertainty score0.835

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 head0.007
GPT teacher head0.237
Teacher spread0.230 · 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

Citations42
Published2012
Admission routes2
Has abstractyes

Explore more

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