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Record W1759940643 · doi:10.1002/mats.201100042

Critical Micelle Concentration of Micelles with Different Geometries in Diblock Copolymer/Homopolymer Blends

2011· article· en· W1759940643 on OpenAlexaff
Jiajia Zhou, An‐Chang Shi

Bibliographic record

VenueMacromolecular Theory and Simulations · 2011
Typearticle
Languageen
FieldChemistry
TopicSurfactants and Colloidal Systems
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMicelleCopolymerLamellar structureMonomerCritical micelle concentrationMaterials scienceAggregation numberPolymer chemistryChemical engineeringHydrodynamic radiusRADIUSChemical physicsChemistryPhysical chemistryPolymerComposite materialAqueous solutionComputer science

Abstract

fetched live from OpenAlex

Abstract It is well‐known that A‐B diblock copolymers in selective solvents or A‐homopolymers can form micelles of different shapes. The critical micelle concentration (CMC) of three basic micelle shapes (lamellar, cylindrical, and spherical) are calculated using the self‐consistent field theory formulated in the grand canonical ensemble. For a given set of molecular parameters, the stable micelle morphology is determined by a comparison of the CMC. The results confirm that micelles undergo a sequence of shape transitions, lamellar → cylindrical → spherical, when the A‐block of the diblock copolymer becomes longer. The results also reveal details about the micelle structure, such as the core radius and corona thickness. This information can be used to understand the effect of homopolymer molecular weight and monomer–monomer interaction on the micelle morphologies. magnified image

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.010
GPT teacher head0.228
Teacher spread0.218 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations27
Published2011
Admission routes1
Has abstractyes

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