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Record W2887110591 · doi:10.1002/marc.201800397

Visualizing Nanoscale Coronal Segregation in Rod‐Like Micelles Formed by Co‐Assembly of Binary Block Copolymer Blends

2018· article· en· W2887110591 on OpenAlexafffund
Menandro Cruz, Jiangping Xu, Qing Yu, Gérald Guérin, Ian Manners, Mitchell A. Winnik

Bibliographic record

VenueMacromolecular Rapid Communications · 2018
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicelleCopolymerMaterials scienceNanoscopic scalePolymerTransmission electron microscopyChemical engineeringCorona (planetary geology)Polymer chemistryNanotechnologyChemistryComposite materialAqueous solutionOrganic chemistry

Abstract

fetched live from OpenAlex

Abstract Mixed micelles formed by co‐assembly of pairs of block copolymers (BCPs) can develop novel morphologies and generate useful properties not accessible from homomicelles. For micelles consisting of two different polymers in the corona, identifying the location of the corona chains is a critical part of morphology characterization. Coronal segregation in mixed micelle is often characterized by transmission electron microscopy in combination with selective staining of individual polymers. In this study, Karstedt’s catalyst is used for selective Pt(0)‐olefin coordination staining of polyisoprene (PI) and poly(methylvinylsiloxane) (PMVS) corona chains in the presence of poly(dimethylsiloxane) (PDMS) corona chains in cylindrical mixed micelles with a crystalline poly(ferrocenyldimethylsilane) (PFS) core. Previous experiments using OsO 4 as a stain did not enable visualization of nanoscale coronal segregation in mixed micelles obtained from co‐assembly of PFS‐ b ‐PI and PFS‐ b ‐PDMS, as well as PFS‐ b ‐PMVS and PFS‐ b ‐PDMS.

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 categoriesMeta-epidemiology (narrow)
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 score0.018
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 science0.0010.000
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 head0.015
GPT teacher head0.284
Teacher spread0.269 · 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.

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

Citations10
Published2018
Admission routes2
Has abstractyes

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