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Record W589566015 · doi:10.1063/1.4918403

Development of multifunctional shape memory polymer foams

2015· article· en· W589566015 on OpenAlexafffund
Janice J. Song, Ijya Srivastava, Hani E. Naguib

Bibliographic record

VenueAIP conference proceedings · 2015
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsUniversity of Toronto
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research Chairs
KeywordsShape-memory polymerMaterials sciencePolylactic acidShape-memory alloyPolyurethaneComposite materialActuatorPolymerEpoxyGlass transitionComputer scienceArtificial intelligence

Abstract

fetched live from OpenAlex

Shape memory polymers (SMP) are a class of stimuli-responsive materials which are able to respond to external stimulus such as temperature and deformation by changing their shape, and return to their original shape upon reversal or removal of the external stimulus. Although SMP materials have been studied extensively and have been used in a wide range of applications such as medicine, aerospace, and robotics, only few studies have looked at the potential of designing multifunctional SMP foams and blends. In this study, we investigate the feasibility of a design of SMP foam materials and blends. The actuator construct will contain a core SMP epoxy and blend of polylactic acid and polyurethane. The effects of the processing parameters of shape memory polymer (SMP) foams on the shape memory effect (SME) were investigated. The solid state foaming technique was employed to obtain the desired foamed cellular structure. One particular point of interest is to understand how the processing parameters affect the SMP and its glass transition temperature (Tg). By correctly tailoring these parameters it is possible to modify the SMP to have an improved shape memory effect SME.

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 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 score0.014
Threshold uncertainty score0.600

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)0.0000.000

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 head0.052
GPT teacher head0.261
Teacher spread0.210 · 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

Citations3
Published2015
Admission routes2
Has abstractyes

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