Differential scanning calorimetry of copolymer of isotactic polypropylene backbone with grafted poly(ethylene‐<i>co</i>‐propylene) branches
Bibliographic record
Abstract
Abstract A series of graft polymers having polypropylene (PP) backbone and poly(ethylene‐co‐propylene) (EPR) side chains was prepared. PP backbone molecular weight (Mn) was 28–98 kg/mol, EPR side chain Mn was 2.6–17 kg/mol, and EPR content was 0–16 wt %. In this work, thermal analysis of the copolymers was performed using differential scanning calorimetry (DSC). Nonisothermal crystallization was performed at different cooling rates. The DSC thermograms revealed multiple melting peaks for slowly cooled samples, most likely the result of the melting of thinner tangential lamellae followed by the melting of thicker radial lamellae. Equilibrium melting temperature (Tm0) was determined using the linear Hoffman–Weeks method. Another approach was also used for determining Tm0: melting temperature (Tm) and crystallization temperature (Tc) were plotted as functions of logarithmic cooling rate. Linear relationships were observed for all samples with the cross points as Tm0's. As cooling rate decreased, Tc, Tm, and enthalpy of fusion (ΔHf) increased. Tm and Tm0 increased with increasing PP Mn. Tc and Tm were unaffected by the grafting of EPR onto the PP backbone. Tm0 and ΔHf decreased as EPR content increased. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 99: 3380–3388, 2006
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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".