Permanent antistatic polypropylene based on polyethylene wax/polypropylene wax grafting sodium acrylate
Bibliographic record
Abstract
Abstract Two types of permanent antistatic agents, polyethylene wax grafted with sodium acrylate (PEW‐g‐AAS) and polypropylene (PP) wax grafted with sodium acrylate (PPW‐g‐AAS), were prepared using a solution grafting method and applied to PP for enhancing antistatic properties. The grafting degree was determined using back titration method and structures were confirmed by Fourier transform infrared spectroscopy. The antistatic properties of PEW‐g‐AAS/PP blends and PPW‐g‐AAS/PP blends were characterized by surface resistivities (ρs) and volume resistivities (ρv), and a combination of contact angle measurements, scanning electron microscope, permittivity, and dielectric loss were used to investigate the surface and inner structures of the blends. Results showed ρs and ρv of PEW‐g‐AAS/PP blends dropped significantly (4–7 magnitudes) above a critical addition at 10%, where a electrostatic dissipative network formed; PPW‐g‐AAS revealed an inferior antistatic performance than PEW‐g‐AAS due to its better compatibility and smaller dispersed phase in the matrix. Further, the antistatic blends treated in 80°C water, 80°C air, and room temperature were investigated, and the results were interpreted from surface energy. Moreover, the addition of antistatic agent had little impact on tensile strength of the PP matrix. © 2012 Wiley Periodicals, Inc. J Appl Polym Sci, 2012
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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) | 0.001 | 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".