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Record W1935324434 · doi:10.1002/pola.27745

Nanopatterning and micropatterning of cobalt containing block copolymers via phase‐separation and lithographic techniques

2015· article· en· W1935324434 on OpenAlexafffund
Mahboubeh Hadadpour, Paul J. Ragogna

Bibliographic record

VenueJournal of Polymer Science Part A Polymer Chemistry · 2015
Typearticle
Languageen
FieldMaterials Science
TopicBlock Copolymer Self-Assembly
Canadian institutionsWestern University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsChain transferCopolymerMaterials sciencePolymer chemistryDispersityPolymerizationChemical engineeringRaftMicropatterningPolydimethylsiloxaneNanotechnologyRadical polymerizationPolymerComposite material

Abstract

fetched live from OpenAlex

ABSTRACT Reversible addition fragmentation transfer (RAFT) agent functionalized polydimethylsiloxane (PDMS‐RAFT) was used as a macro‐RAFT agent to polymerize a mixed sandwich cobaltocene containing monomer featuring η 5 ‐cyclopentadienyl‐cobalt‐ η 4 ‐cyclobutadiene. High molecular weight block copolymers (BCP) consisting of a metallic block and a PDMS block with excellent control over molecular weight and polydispersity were prepared. Solid‐state self‐assembly of this BCP resulted in hexagonal domains of metallopolymer phase‐separated from PDMS. In solution, spherical micelles with a metallic core, stabilized by a PDMS corona were prepared. Pyrolysis of the BCP resulted in magnetic nanoparticles with 30% char yield. The BCP was used as an ink material for microcontact printing (μCP) to transfer long‐ranged patterns. © 2015 Wiley Periodicals, Inc. J. Polym. Sci., Part A: Polym. Chem. 2015 , 53 , 2747–2754

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.004

Distilled classifier scores by category (both heads)

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)0.0010.001

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 head0.291
Teacher spread0.276 · 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 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

Citations12
Published2015
Admission routes2
Has abstractyes

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