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Investigating the Physiochemical Effect of Nitric Acid on Silicone Rubber Composites for Outdoor Insulation

2025· article· W4416183660 on OpenAlexafffund
Safi Ullah Butt, Refat Atef Ghunem, Ayman El‐Hag, Li‐Lin Tay

Bibliographic record

Venuenot available
Typearticle
Language
FieldMaterials Science
TopicPolymer Nanocomposites and Properties
Canadian institutionsNational Research Council CanadaUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermogravimetryNitric acidSilicone rubberSiloxaneScanning electron microscopeNatural rubberSiliconeComposite numberArachidic acid

Abstract

fetched live from OpenAlex

In this paper, the physiochemical effect of nitric acid on silica filled silicone rubber composites is investigated for outdoor insulation applications, by soaking the composites under study in the nitric acid solution with a pH of 3 for 1 week. Fourier Transform Infrared Spectroscopy, thermogravimetry analysis, differential thermal analysis and Scanning electron microscopy are utilized to investigate the chemical, thermal and morphological changes for the aged composites. The intensity of the <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$1100-1000 ~\text{cm}^{-1}$</tex> absorption band, which corresponds to the siloxane backbone, decreases with acidic aging indicating scission of siloxane chain. The aged composites are reported in the thermogravimetry analysis to become more prone to depolymerization as compared to the unaged composites, possibly due to the acid promoting more mobile siloxane chain within the composite matrix. Appearance of pores and filler detachment are also reported from scanning electron microscopy, suggesting the acidic aging effect on the adhesion between filler and the silicone rubber matrix.

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)0.001
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 score0.857

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.012
GPT teacher head0.272
Teacher spread0.259 · 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

Citations0
Published2025
Admission routes2
Has abstractyes

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