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Record W2749338834 · doi:10.1515/epoly-2017-0102

Synthesis and thermal properties of poly(vinylcyclohexane)- <i>b</i> -poly(4-vinylpyridine) diblock copolymers prepared via RAFT polymerization

2017· article· en· W2749338834 on OpenAlexaff
Yinghua Qi, Iryna I. Perepichka, Zhengji Song, Sunil K. Varshney

Bibliographic record

Venuee-Polymers · 2017
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsPolymer Source (Canada)
Fundersnot available
KeywordsCopolymerMaterials sciencePolystyrenePolymer chemistryRaftSize-exclusion chromatographyDifferential scanning calorimetryPolymerizationReversible addition−fragmentation chain-transfer polymerizationPolymerRadical polymerizationChemical engineeringChemistryOrganic chemistryComposite material

Abstract

fetched live from OpenAlex

Abstract A series of novel poly(vinylcyclohexane)- b- poly(4-vinylpyridine) (PVCH-b-P4VP) diblock copolymers have been synthesized through a combination of anionic and RAFT polymerization techniques. Using this approach, end functionalized ω-hydroxy-polystyrene was used to yield ω-hydroxy-PVCH by hydrogenation followed by end-functionalization via an esterification reaction with 2-(dodecylthiocarbonothioylthio)-2-methylpropionic acid to yield PVCH-RAFT agent. The crossover propagation from PVCH-RAFT to 4VP displays living character and allowed generating diblock copolymers with well-defined molecular compositions. The characterization of the resulted polymers was performed using 1 H nuclear magnetic resonance (NMR) and Fourier-transform infra-red (FT-IR) spectroscopies, size-exclusion chromatography with light-scattering detector (SEC-LS), and the thermal properties were studied using differential scanning calorimetry (DSC).

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 score0.003

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)0.0010.000

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 head0.012
GPT teacher head0.220
Teacher spread0.208 · 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

Citations3
Published2017
Admission routes1
Has abstractyes

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