MétaCan
Menu
Back to cohort
Record W2330349169 · doi:10.1021/ma202506b

Multishape Memory Effect of Norbornene-Based Copolymers with Cholic Acid Pendant Groups

2012· article· en· W2330349169 on OpenAlexafffund
Yu Shao, Christine Lavigueur, X. X. Zhu

Bibliographic record

VenueMacromolecules · 2012
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsUniversité de Montréal
FundersKorea Astronomy and Space Science InstituteFonds Québécois de la Recherche sur la Nature et les TechnologiesNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsCopolymerNorborneneShape-memory polymerGlass transitionPolymer chemistryCholic acidMaterials scienceMonomerPolymerTriethylene glycolChemical engineeringComposite materialChemistry

Abstract

fetched live from OpenAlex

Multishape memory copolymers were prepared through copolymerization of two norbornene derivatives: one based on cholic acid and the other on triethylene glycol monomethyl ether. The glass transition temperature ( T g ) of the copolymers can be tuned over a temperature range from −58 to 176 °C. Most of these copolymers displayed a very broad T g over a 20 °C range which can allow a multishape memory effect. The shape memory properties of the copolymer incorporating an equal molar amount of both monomers have been studied in detail. The multishape memory effect was investigated by dynamic mechanical analysis using a thermomechanical programming process, in which multiple steps created two, three, and four temporary shapes. The polymer displayed good shape fixing and recovery in different thermal processing stages over the broad glass transition range. This series of copolymers with broad and tunable T g ’s may be useful as functional materials with multishape memory effect.

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 score0.002

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 head0.007
GPT teacher head0.229
Teacher spread0.222 · 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

Citations79
Published2012
Admission routes2
Has abstractyes

Explore more

Same venueMacromoleculesSame topicPolymer composites and self-healingFrench-language works237,207