Improving High Voltage Cables Using LDPE/CB Conductive Composites
Bibliographic record
Abstract
In recent decades, polymeric composites have received significant interest due to several enhanced properties, such as excellent mechanical, thermal, electrical and light-weight properties. In this regard, carbon-based materials like polymeric matrix reinforced by carbon black and graphene have answered particular requirements for several electrical applications. For example, these composites are commonly used as semi-conductive materials in high voltage underground cables. The 1998 ice storm in Quebec caused catastrophic effects that led to billions of dollars in damages, which could have been prevented by using extruded-underground transmission cables instead of overhead lines. In fact, extruded high-voltage cables feature long durability, lower environmental impact and lower maintenance cost. However, improved design and development of enhanced insulating and semi-conductive composites for use in the different layers of high voltage cables still needs to be done. Therefore, considering the above-mentioned requirements, we studied the electrical, mechanical, and thermal properties of LDPE/CB (low density polyethylene/carbon black) composites and our findings led to the design of an appropriate polymer composite with suitable electrical and thermal conductivity as well as increase mechanical properties for electrical 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".