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Record W4283755597 · doi:10.1002/slct.202201040

Synthesis and Characterization of Core‐Shell Bottlebrush Polymers via Controllable Polymerization

2022· article· en· W4283755597 on OpenAlexaff
Yuying Yang, Shaohui Lin, Xianshe Feng, Qinmin Pan

Bibliographic record

VenueChemistrySelect · 2022
Typearticle
Languageen
FieldChemistry
TopicAdvanced Polymer Synthesis and Characterization
Canadian institutionsUniversity of Waterloo
FundersNational Natural Science Foundation of China
KeywordsMaterials sciencePolymerizationPolymer chemistryNorbornenePolymerCopolymerSide chainPolystyreneChain transferRing-opening metathesis polymerisationRing-opening polymerizationReversible addition−fragmentation chain-transfer polymerizationChemical engineeringMetathesisRadical polymerizationComposite material

Abstract

fetched live from OpenAlex

Abstract This research was focused on the synthesis of well‐defined bottlebrush polymers (BBPs) comprising of polynorbornene (PNB) backbones and side‐chains of the polylactide (PLA)‐b‐polystyrene (PS). The PNB backbones were synthesized via ring‐opening metathesis polymerization of norbornene, and then L‐lactide (LA) was first grafted to the backbones to form PLA side‐chains by ring‐opening polymerization. Afterwards, styrene was further grafted to the PLA cantilever with triothiocarbonate modification via reversible addition‐fragmentation chain transfer polymerization to form PLA‐b‐PS side‐chains. Controllable polymerization was achieved by introducing each block in stages. A novel structure with PLA and PS block arranged in tandem was constructed, and their morphologies of self‐assembly in bulk were investigated by transmission electron microscopy imaging. The results demonstrated that the compositions and molecular weight of the side‐chains in the BBPs affected the self‐assembling morphologies of the BBPs significantly, and revealed that the polymers were with core‐shell structures.

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), Insufficient payload (model declined to judge)
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 score0.014
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0060.000

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 head0.007
GPT teacher head0.201
Teacher spread0.194 · 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

Citations6
Published2022
Admission routes1
Has abstractyes

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