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Record W3216131608 · doi:10.1039/d1sc05937h

The role of cooling rate in crystallization-driven block copolymer self-assembly

2021· article· en· W3216131608 on OpenAlexafffund
Shaofei Song, Jingjie Jiang, Ehsan Nikbin, Jane Y. Howe, Ian Manners, Mitchell A. Winnik

Bibliographic record

VenueChemical Science · 2021
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsUniversity of VictoriaUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCopolymerCrystallizationSelf-assemblyMaterials scienceBlock (permutation group theory)Polymer chemistryChemical engineeringNanotechnologyEngineeringPolymerComposite materialMathematicsCombinatorics

Abstract

fetched live from OpenAlex

Self-assembly of crystalline-coil block copolymers (BCPs) in selective solvents is often carried out by heating the mixture until the sample appears to dissolve and then allowing the solution to cool back to room temperature. In self-seeding experiments, some crystallites persist during sample annealing and nucleate the growth of core-crystalline micelles upon cooling. There is evidence in the literature that the nature of the self-assembled structures formed is independent of the annealing time at a particular temperature. There are, however, no systematic studies of how the rate of cooling affects self-assembly. We examine three systems based upon poly(ferrocenyldimethylsilane) BCPs that generated uniform micelles under typical conditions where cooling took pace on the 1-2 h time scale. For example, several of the systems generated elongated 1D micelles of uniform length under these slow cooling conditions. When subjected to rapid cooling (on the time scale of a few minutes or faster), branched structures were obtained. Variation of the cooling rate led to a variation in the size and degree of branching of some of the structures examined. These changes can be explained in terms of the high degree of supersaturation that occurs when unimer solutions at high temperature are suddenly cooled. Enhanced nucleation, seed aggregation, and selective growth of the species of lowest solubility contribute to branching. Cooling rate becomes another tool for manipulating crystallization-driven self-assembly and controlling micelle morphologies.

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 score0.005
Threshold uncertainty score0.240

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 head0.005
GPT teacher head0.223
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 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

Citations21
Published2021
Admission routes2
Has abstractyes

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