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Record W1975543362 · doi:10.1039/c1jm11104c

Preparation and photo/chemical-activation of wormlike network micelles of core–shell quantum dots and block copolymer hybrids

2011· article· en· W1975543362 on OpenAlexafffund
Meng Zhang, Laetitia René-Boisneuf, Yiwei Hu, Kimia Moozeh, Yasser A. Hassan, Gregory D. Scholes, Mitchell A. Winnik

Bibliographic record

VenueJournal of Materials Chemistry · 2011
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsUniversity of OttawaUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicelleCopolymerPolymerPolymerizationMaterials sciencePhotoluminescenceChemical engineeringMacromoleculeQuantum dotPolymer chemistryChemistryNanotechnologyPhysical chemistryAqueous solutionComposite material

Abstract

fetched live from OpenAlex

Poly(styrene-b-4-vinylpyridine) diblock copolymers PS404-b-P4VP76 and PS317-b-P4VP76 (the subscripts indicate the degree of polymerization) self-assemble into spherical “crew-cut” micelles with a PS core and P4VP corona when prepared in a mixture of chloroform and 2-propanol. When the micelles are formed in the presence of quantum dots (QDs), the nature of the structures formed depends upon the polymer and the type of QDs. In our previous report [Macromolecules, 2010, 43, 5066–5074], PS404-b-P4VP76 + CdSe QDs formed stable spherical hybrid micelles, but prolonged vigorous stirring of the solutions led to a rearrangement into wormlike networks and loss of photoluminescence (PL) from the QDs. Here we report that PS317-b-P4VP76 + CdSe/ZnS core–shell QDs behave differently. Partial loss of PL intensity occurred upon addition of 2-propanol to the chloroform solution of the components, and the rearrangement to a network structure occurred spontaneously. We describe two strategies for recovery of the PL intensity for the QDs within the network, photo-activation and chemical activation with elemental sulfur.

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 score0.001
Threshold uncertainty score0.794

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 head0.017
GPT teacher head0.236
Teacher spread0.219 · 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

Citations16
Published2011
Admission routes2
Has abstractyes

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