Effect of <scp>POSS</scp>‐grafted titanium dioxide on the electrical and thermal properties of <scp>LDPE</scp>/<scp>T</scp>i<scp>O</scp><sub>2</sub> polymer nanocomposite
Bibliographic record
Abstract
ABSTRACT Two types of nanocomposites have been fabricated by a ball‐milling technique. The first type consists of untreated titanium dioxide (TiO2) incorporated into low‐density polyethylene (LDPE). For the second one, TiO2 filler chemically treated with trisilanol phenyl–polyhedral oligomeric silsesquioxane (TP–POSS) as compatibilizing agent was ball‐milled with LDPE. All specimens were tested by microstructure analysis and thermal, dielectric characterization techniques. Microstructure analysis by atomic force microscopy and scanning electron microscopy show clearly an increased dispersion in presence of POSS. Scanning electron microscopy even shows the formation of a particular structure due possibly to interactions between functionalization. It was observed that the modification of the surface of TiO2 by the POSS decreased the dielectric loss. All nanocomposites containing treated TiO2 revealed an improvement in thermal conductivity, with the most distinct value of 19% in case of LDPE containing 5 wt % treated TiO2. The incorporation of TiO2 fillers seems to reduce the dielectric breakdown strength of the nanocomposites. However, nanocomposites containing 3 and 5 wt % treated TiO2 have exhibited a slightly enhancement in dielectric breakdown strength up to 5%. The improvement in surface resistance to partial discharge was found in all nanocomposites specimens, especially for both types of composite containing 7 wt % untreated and treated TiO2. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018, 135, 46095.
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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".