Polypropylene fiber–matrix bonds in cementitious composites
Bibliographic record
Abstract
It is known that for creating advanced polyolefin/cement-based composites the polymer surface should be converted into a layer which is compatible with the inorganic component. In this respect, plasma chemistry offers additional solutions to the wet chemistry approach. It has been demonstrated during the last decade that cold plasma-mediated reactions are suitable for etching and surface functionalizing even the most inert polymeric substrates, including Teflon, polypropylene (PP), and polyethylene (PE). In this paper composite preparations from SiCl4-cold plasma and chromic acid-treated fibrillated PP substrates and cement are described. The nature of plasma- and wet chemistry-induced surface functionalization and etching processes was monitored using survey and high-resolution X-ray photoelectron spectroscopy (XPS), attenuated total reflectance Fourier transform infrared (ATR-FTIR) spectroscopy, atomic force microscopy (AFM), and dynamic water contact angle measurements. It has been demonstrated that the plasma-exposed surfaces result in increased adhesion between the fibers and the cementitious matrix in comparison with the chromic acid-modified fibers. It has been shown that the improved tensile strength values can be related to the treatment-generated polar surface functionalities as well as roughness.
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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".