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Record W2756118115 · doi:10.5254/rct.18.83700

ISOBUTYLENE-RICH IMIDAZOLIUM IONOMERS: INFLUENCE OF ION-PAIR DISTRIBUTION AND COUNTER-ANION STRUCTURE

2017· article· en· W2756118115 on OpenAlexaff
Adam A. Ozvald, Monika R. Klezcek, Antonio C. Rodrigo, J. Scott Parent

Bibliographic record

VenueRubber Chemistry and Technology · 2017
Typearticle
Languageen
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsQueen's University
Fundersnot available
KeywordsBromideElastomerIsobutyleneMaterials scienceIonomerPolymer chemistryPolymerThermosetting polymerPropargyl bromideChemical engineeringChemistryComposite materialOrganic chemistryCopolymer

Abstract

fetched live from OpenAlex

ABSTRACT New chemistry for overcoming the limitations of nonpolar elastomers is detailed, with particular emphasis on improving interfacial adhesion and the intensity of polymer–filler interactions. The chemical modification of brominated poly(isobutylene-co-isoprene) and brominated poly(isobutylene-co-para-methyl styrene) is used to introduce small amounts of imidazolium bromide functionality. Unlike conventional isobutylene-rich elastomers, ionomer derivatives bearing vinylimidazolium bromide groups are peroxide curable. The ultimate cross-link density, along with accompanying thermoset properties, can be tailored by changing the amount and distribution of N-vinylimidazolium and N-butylimidazolium functionality. Moreover, the counteranion can be exchanged from bromide to sulfonate or vinylsulfonate to further optimize adhesive, tensile, and stress relaxation properties. Bromide exchange anionic montmorillonite clay platelets can yield thermoset ionomer nanocomposites with a high degree of reinforcement despite a relatively low filler loading.

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)0.0010.000
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 head0.004
GPT teacher head0.209
Teacher spread0.205 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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