Surface characteristics of hydrosilylated polypropylenes: Effect of co‐catalyst and reaction temperature
Bibliographic record
Abstract
Abstract Polypropylene (PP) was modified through a peroxide‐induced degradation and a catalytic hydrosilylation reaction in the melt phase. The effects of temperature and co‐catalysts on the reaction and the surface characteristics of the hydrosilylated polypropylene (SiPP) were studied. Axisymmetric Drop Shape Analysis‐Profile (ADSA‐P) was used to characterize the surface wettability. The morphology, roughness and heterogeneity of the surface were investigated by the lateral force mode of atomic force microscopy (LFM). Proton nuclear magnetic resonance (1H NMR) spectroscopy was employed to quantify the conversion of terminal double bonds. X‐ray photoelectron spectroscopy (XPS) was used to quantify the surface chemical composition. LFM images showed that the surfaces of the PP and DPP samples were rough but heterogeneous and that the surfaces of the SiPP samples appeared rough but homogeneous. The surface wettability was well correlated to the surface microstructure and surface chemical composition. The surfaces investigated were modeled based on the microstructure observed, and a new scheme was developed to calculate surface free energy and adhesion work. Results showed that there is an optimum reaction temperature in terms of the conversion of double bonds, and that increasing the amount of co‐catalyst promotes the conversion.
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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".