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Record W2160900690 · doi:10.1109/tdei.2006.1593410

Erosion resistance of nano-filled silicone rubber

2006· article· en· W2160900690 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.

Bibliographic record

VenueIEEE Transactions on Dielectrics and Electrical Insulation · 2006
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsSilicone rubberMaterials scienceComposite materialFiller (materials)Nano-SiliconeErosionNatural rubberFumed silicaMass fraction

Abstract

fetched live from OpenAlex

The paper presents the experimental results obtained on the erosion resistance of silicone rubber (SIR) filled with 12 nm size fumed silica (nano filler) to those filled with 5 /spl mu/m size silica filler (micro filler). The ASTM 2303 inclined plane tracking and erosion test was used in the comparison as well as an infrared laser as the source of heat to erode the SIR samples. The erosion resistance of the SIR materials increased with increasing percentage of the fillers, and it was observed that 10% by weight of nano-filled SIR gives a performance that is similar to that obtained with 50% by weight of micro-filled SIR. The low frequency components of leakage current and the eroded mass are used to evaluate the relative erosion resistance of the composites and the third harmonic component of the leakage current shows good correlation to the measured eroded mass. The paper discusses the possible reasons for the improvement in the erosion resistance of nano-filled silicone composites.

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 score0.205
Threshold uncertainty score0.845

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.011
GPT teacher head0.223
Teacher spread0.213 · 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