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Record W4392966298 · doi:10.1177/00037028241238248

Surface Structural Changes in Silicone Rubber Due to Electrical Tracking

2024· article· en· W4392966298 on OpenAlexafffund
Harpreet Kaur, Kavin Bhuvan, Rajkumar Padmawar, Dennis K. Hore

Bibliographic record

VenueApplied Spectroscopy · 2024
Typearticle
Languageen
FieldMaterials Science
TopicHigh voltage insulation and dielectric phenomena
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSilicone rubberAttenuated total reflectionMaterials scienceContact angleAbsorbanceFourier transform infrared spectroscopyPolymerComposite materialInfraredInfrared spectroscopyTracking (education)Natural rubberChemical engineeringAnalytical Chemistry (journal)ChemistryOpticsOrganic chemistry

Abstract

fetched live from OpenAlex

There is a growing interest in the use of silicone composite insulators for electrical power transmission and distribution applications. However, such materials are susceptible to degradation as they are exposed to electrical and environmental stresses during operating conditions. Therefore, it is crucial to gain a thorough understanding of the degradation mechanism through changes in the material structure that may provide insight into potential failures in the electrical grid. Attenuated total reflection Fourier transform infrared spectroscopy and two-dimensional correlation spectroscopy (2D-COS) were used along with contact angle measurements to characterize changes in silicone rubber samples from actual insulators subjected to tracking wheel testing. The results showed a decrease in absorbance of different infrared bands representing different functional groups, such as Si-O-Si, methyl functional groups, and both Al-O and hydroxyl groups of alumina trihydrate as a function of the number of tracking cycles. The sequence of changes in the functional groups was determined by 2D-COS as Al-O and OH followed by Si-O-Si polymer backbone modes, followed by polymer methyl side chains. An enhancement in the average contact angle with the number of tracking cycles revealed a concomitant increase in surface roughness with electrical tracking.

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 categoriesInsufficient payload (model declined to judge)
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 score0.022
Threshold uncertainty score1.000

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)0.0010.001

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 head0.013
GPT teacher head0.276
Teacher spread0.263 · 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; both teacher heads agree on what is shown here.

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

Citations1
Published2024
Admission routes2
Has abstractyes

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