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Record W1970387308 · doi:10.1115/smasis2008-519

Development of Two Part Porous Shape Memory Polymer Nanocomposites

2008· article· en· W1970387308 on OpenAlexaff
Steven Simkevitz, Hani E. Naguib

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMaterials Science
TopicPolymer composites and self-healing
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials sciencePorosityComposite materialShape-memory polymerSaturation (graph theory)NanocompositePolymerActuatorPorous mediumComputer science

Abstract

fetched live from OpenAlex

The porous structure and shape memory effect (SME) were studied for shape memory polymer (SMP) and SMP nanocomposites. In order to alter the density of the SMP, a batch processing technique was utilized on the creation of porous structure, where the saturation pressure and foaming time were varied and saturation time and foaming temperature were kept constant. The change in density on the time response of the SME was investigated. Also the effects of the change in density and thickness or the addition of organoclay on the recovery force of the SMP were elucidated. With the objective of designing an SMP to be used as an actuator, response time, recovery force and weight savings were found to be critical parameters.

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 categoriesInsufficient payload (model declined to judge)
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 score0.039
Threshold uncertainty score0.999

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)0.0020.000

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 head0.027
GPT teacher head0.249
Teacher spread0.223 · 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.

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

Citations2
Published2008
Admission routes1
Has abstractyes

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