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Record W3172615417 · doi:10.3390/polym13111831

Characterization of Mechanical and Dielectric Properties of Silicone Rubber

2021· article· en· W3172615417 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenuePolymers · 2021
Typearticle
Languageen
FieldEngineering
TopicDielectric materials and actuators
Canadian institutionsUniversity of TorontoToronto Metropolitan UniversitySt. Michael's Hospital
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSilicone rubberCharacterization (materials science)Materials scienceComposite materialDielectricNatural rubberSiliconePolymer scienceChemical engineeringNanotechnologyOptoelectronicsEngineering

Abstract

fetched live from OpenAlex

Silicone rubber's silicone-oxygen backbones give unique material properties which are applicable in various biomedical devices. Due to the diversity of potential silicone rubber compositions, the material properties can vary widely. This paper characterizes the dielectric and mechanical properties of two different silicone rubbers, each with a different cure system, and in combination with silicone additives. A tactile mutator (Slacker™) and/or silicone thickener (Thi-vex™) were mixed with platinum-cured and condensation-cured silicone rubber in various concentrations. The dielectric constants, conductivities, and compressive and shear moduli were measured for each sample. Our study contributes novel information about the dielectric and mechanical properties of these two types of silicone rubber and how they change with the addition of two common silicone additives.

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.

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 score0.011
Threshold uncertainty score0.183

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 head0.007
GPT teacher head0.169
Teacher spread0.162 · 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