Electrical Breakdown Properties of Clay-Based LDPE Blends and Nanocomposites
Bibliographic record
Abstract
Microstructure and electrical breakdown properties of blends and nanocomposites based on low-density polyethylene (LDPE) have been discussed. A series of LDPE nanocomposites containing different amount of organomodified montmorillonite (clay) with and without compatibilizer have been prepared by means of melt compounding. Two sets of blends of LDPE with two grades of Styrene-Ethylene-Butylene-Styrene block copolymers have been prepared to form cocontinuous structure and host the nanoreinforcement. A high degree of dispersion of oriented clay was observed through X-ray diffraction, scanning, and transmission electron microscopy. This was confirmed by the solid-like behavior of storage modulus in low frequencies in rheological measurement results. An alteration in the morphology of blends was witnessed upon addition of clay where the transportation phenomenon to the copolymer phase resulted in a downsizing on the domain size of the constituents of the immiscible blends. The AC breakdown strength of nanocomposites significantly increased when clay was incorporated. The partially exfoliated and intercalated clay platelets are believed to distribute the electric stress and prolong the breakdown time by creating a tortuous path for charge carriers. However, the incorporation of clay has been shown to diminish the DC breakdown strength of nanocomposites, mostly due to the thermal instability brought by clay.
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".