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Record W4309699820 · doi:10.1002/pi.6482

Cationic bottlebrush brush polymers via sequential <scp>SI‐ROMP</scp> and <scp>SI‐ARGET‐ATRP</scp>

2022· article· en· W4309699820 on OpenAlexafffund
Jade Poisson, Cheyenne J. Christopherson, Zachary M. Hudson

Bibliographic record

VenuePolymer International · 2022
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Surface Interaction Studies
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCationic polymerizationPolymer chemistryAtom-transfer radical-polymerizationPolymerPolymerizationMonomerSide chainPolymer brushMaterials scienceROMPRing-opening polymerizationRing-opening metathesis polymerisationMetathesisChemistryComposite material

Abstract

fetched live from OpenAlex

Abstract Here we describe a facile approach to preparing cationic bottlebrush brush polymers via sequential surface‐initiated (SI) ring‐opening metathesis polymerization and SI activators regenerated by electron transfer atom transfer radical polymerization. These techniques afforded both bottlebrush polymer brushes and their linear polymer brush analogues with cationic triphenylphosphonium pendant groups. Utilization of two controlled polymerization techniques with orthogonal requirements for monomers and additives allowed the polymerization of the backbone and the pendant side chains to be controlled independently. Grafting polymer side chains to a polymer brush backbone resulted in a thickness increase of ca 70%, indicating that the polymers are forced into a stretched conformation with greater surface coverage than was afforded by the linear brush polymers. As a result of the stretched conformation, greater ionic conductivity in the bottlebrush brush architectures was observed. © 2022 Society of Industrial Chemistry.

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)0.001
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 categoriesInsufficient payload (model declined to judge)
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 score0.125
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.001

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 head0.017
GPT teacher head0.269
Teacher spread0.253 · 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; both teacher heads agree on what is shown here.

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

Citations8
Published2022
Admission routes2
Has abstractyes

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