Thermal behaviour and crystallization analysis of ethylene‐propylene ( <scp>EP</scp> ) copolymer and EP‐styrene terpolymer
Bibliographic record
Abstract
Abstract Polyolefin copolymers have been an important commercial product since their invention. Hence, it is crucial to study their co‐ and terpolymers due to their extensive use. In this paper, in situ synthesis of ethylene‐propylene (EP) copolymer, its terpolymer with styrene, and composites with nickel‐chromium (NiCr) layered double hydroxide (LDH) has been reported along with their thermal properties. Styrene had a significant impact on the activity, increasing the yield by 195% and 235% with an addition of 0.5 and 1.0 mL styrene, respectively, compared to neat EP. The crystallinity, melting temperature, and thermal stability decreased due to styrene; nevertheless, it performed better compared to a similar work of terpolymer where α‐olefin was the third monomer. The incorporation of NiCr LDH as a drop‐in filler during in situ polymerization affected adversely the thermal stability of the terpolymer. However, the ultrasonication treatment improved the thermal stability of the final product.
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".