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Record W4399208037 · doi:10.1680/jemmr.23.00116

Novel constitutive models, challenges and opportunities of shape memory polymer composites

2024· article· en· W4399208037 on OpenAlexaff
Merve Uyan, Melih Soner Çeliktaş

Bibliographic record

VenueEmerging Materials Research · 2024
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsShape-memory polymerMaterials scienceAerospaceFabricationSmart materialPolymerComputer scienceNanotechnologyComposite materialAerospace engineering

Abstract

fetched live from OpenAlex

Shape memory polymers (SMPs) are smart materials that have been widely accepted in potential applications ranging from biomedical devices to aerospace. Despite their limitations in engineering applications, SMPs have been utilized for strengthening, innovation and improvement of driving methods, leading to an increase in research on SMP composites (SMPCs). In this review, SMPs used in the production, actuation mechanisms, fabrication and testing methods of SMPCs are presented. Current and potential applications and novel constitutive models that have a very important place in elucidating the interface of SMPCs are introduced. Finally, challenges and opportunities are presented to provide future prospects for SMPCs.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.092
Threshold uncertainty score0.727

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.208
GPT teacher head0.359
Teacher spread0.151 · 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.

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

Citations5
Published2024
Admission routes1
Has abstractyes

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