Surface characteristics of hydrosilylated polypropylene
Bibliographic record
Abstract
Abstract Polypropylene containing terminal unsaturation was modified with a hydride‐terminated polydimethylsiloxane (PDMS) at three different temperatures through a catalytic hydrosilylation reaction in the melt phase. A comprehensive study on the surface characteristics of hydrosilylated polypropylene (SiPP) was conducted by combining macroscopic thermodynamics, microstructure, and chemical composition measurements. Axisymmetric drop shape analysis–profile (ADSA‐P) was used to characterize the surface wettability. The morphology, roughness, and heterogeneity of the surfaces were investigated by the lateral‐force mode of atomic force microscopy (LFM). X‐ray photoelectron spectroscopy (XPS) was used to quantify the surface chemical composition. LFM images showed that all sample surfaces were rough and heterogeneous on a micrometer scale. XPS analysis showed that the surfaces investigated were complicated in composition and that various oxides existed on the surfaces. The surface wettability was well correlated to the surface microstructure and 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. For SiPPs, the lower the reaction temperature, the more PDMS incorporation was observed, the smaller the surface free energy and the work of adhesion, the more hydrophobic the surface, and the lower the permeability. © 2003 Wiley Periodicals, Inc. J Appl Polym Sci 88: 3117–3131, 2003
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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".