The Impact of the Inclusion of MgO Nano-Fillers in a Polyethylene Matrix on Dielectric Strength and Resistance to Partial Discharges
Bibliographic record
Abstract
One commonly used strategy to enhance polymers specific properties such as the resistance to partial discharges erosion is the incorporation into the polymeric matrix of inorganic micro or nanoparticles. This study focused on the dielectric properties of Low-Density Polyethylene (LDPE) filled with nano-sized Magnesium Oxide (MgO) particles compounded by thermo-mechanical process and one of the purposes was to establish appropriate processing parameters in order to reach the desired dielectric properties. LDPE was used as a matrix and was reinforced by MgO particles having a nominal average size of 30 nm. The MgO nanoparticles were treated with a silane coupling agent (3-Glycidyloxypropyl Trimethoxysilane). The samples were initially prepared in a melt-mixing chamber with a MgO content of 1% wt. These pre-mixed samples were further treated by the means of thermo-mechanical mixing in a conical co-rotating twin-screw extruder in order to improve the dispersion and distribution of the MgO particles. In this report, both lifetime under a PD activity and AC dielectric strength of pure and nano-filled LDPE samples have been measured and compared. Nano-filled LDPE samples were found to exhibit an improve lifetime, without any detrimental impact on their short-term dielectric strength. This suggests that nano-filled LDPE may be for electric applications for which the dielectric materials may be exposed to partial discharge activities. This is significant result for the use of MgO-reinforced PE as an insulating material for HV cables since the resistance to PD is closely related to treeing resistance which is the main electrical degradation mechanism that leads to failure for shielded extruded power cables.
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) | 0.000 | 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".