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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 OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

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.

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 categoriesInsufficient payload (model declined to judge)
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 score0.189
Threshold uncertainty score1.000

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)0.0010.000

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