Reinforced PDMS Elastomer Nanocomposites: Effectiveness of In Situ Nano-Silica Content on Flashover Voltage and Treeing Phenomena
Bibliographic record
Abstract
Large interfacial surface area between the inorganic particles and the polymer matrix has made nanocomposites an interesting composition for a wide range of applications including high-voltage insulating materials. However, nanofiller dispersion is challenging and there is always a specific percolation threshold that narrows the particle content in a polymer dispersion. This research attempt to benefit the in situ silica precipitation technique to avoid all problems associated with nanoparticle dispersion and furthermore introduce a technique to increase the nano-silica content. In this regard, elastomeric silicone nanocomposites loaded with various amount of nano-silica, low to high, is fabricated. It is revealed that the nanocomposite loaded with a high quantity of nano-silica particles exhibited high thermal stability and heat resistivity. Utilized as a coating, this nanocomposite could successfully increase the flashover voltage and present less damage after electrical discharges. Moreover, the electrical treeing investigation illustrates a different distribution of tree structure depending on nano-silica content which could be beneficial in diverse electrical insulating applications.
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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".