Annealing of pigmented low density polyethylene with titanium dioxide nanoparticules and its Influence on mechanical and thermal Properties
Bibliographic record
Abstract
In the recent years, composites using nanoparticles (nanocomposites) have gained much attention and been intensively investigated owing to their remarkable enhanced properties including mechanical, optical, thermal properties and their wide spread, potential applications [1] . Different inorganic nanoparticles have been used to improve polymer properties, such as titanium dioxide (TiO 2 ), silicon dioxide (SiO 2 ) and aluminum trioxide (Al 2 O 3 ). Polymer-based TiO 2 composites have been extensively studied in the literature in order to improve mechanical and thermal properties of the polymer [2]. In this work, effect of annealing process on the mechanical and thermal properties of pigmented Low density polyethylene with titanium dioxide nanoparticles was investigated. It shows that at lower annealing temperatures, the improvement of …… can be well correlated to the increased crystallinity induced by lamellar rearrangement for Low density polyethylene. By using differential scanning calorimetry (DSC) techniques we show that two kinds of endotherms arise in low density polyethylene (LDPE pigmented with titanium dioxide nanoparticles annealed for two different annealing temperatures 60 and 110 °C respectively. Of particular importance is the endotherm II, which reflects the melting of the crystallites generated at the annealing temperature by the partial melting/recrystallization mechanism. KEY WORDS: Recrystallization / Annealing / pigmented LDPE / Titanium dioxide/ Mechanical properties/ DSC. References [1] V.G.Nguyen, Composites: Part B, 45 (2013), 1192–1198. [2] O.Yahia Bakher, M.Al-harthi, The Canadian journal of chemical engineering, 93 (2015), 2184-2189.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 teacher head, 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".