Aging Characteristics of Ceramifiable Silicone Rubber Nanocomposites Exposed to Electrolytic Solution
Bibliographic record
Abstract
The capability to transform into a flame-retardant ceramic body when encountering high temperatures makes low melting glass (LMG) powder an effective additive for room temperature vulcanized (RTV) silicone rubber composites, which are used as coating materials for outdoor high-voltage insulators operating in harsh environments. In this context, the present study examines the impact of nano LMG powder (LMGP) on the aging characteristics of 5 wt% boron nitride (BN)-doped silicone rubber nanocomposites exposed to a strong electrolytic aqueous ammonium chloride (NH4Cl) solution. LMG nanofiller was added in varying weight percentages of 3 and 7 wt%. Except for the 7 wt% LMG-doped composite, a distinct dip in weight gain, indicating higher degradation and detachment of filler particles, was noted in all the composites during diffusion test. Aging-induced deterioration of surface morphology is well evident in 3-D microscopy, contact angle, and Fourier transform infrared (FTIR) spectroscopic test results, which clearly demonstrate increased surface roughness and scissioning of side chains (Si-CH$_{{3}}\text {)}$and backbone chains (Si-O–Si) in silicone rubber matrix of all the composites postaging. Furthermore, antipollution performance and thermal properties of all the test specimens were evaluated through inclined plane tracking and erosion test, thermogravimetric analysis (TGA), and IR thermography. The test results demonstrated that LMG-doped composites showed better tracking and erosion resistance, higher thermal stability, and lower surface temperature distribution during inclined plane tracking (IPT) compared to the only BN specimen before and after aging. The experimental results revealed that the 7 wt% LMG composite exhibited better resistance to degradation caused by the NH4Cl solution.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".