Sulfur Free Crosslinking of EPDM Composites via Silane Grafting and Silica Incorporation
Bibliographic record
Abstract
This study aims at evaluating and developing an environmental-friendly and sulfur-free cured ethylene propylene diene monomer (EPDM) composites. Silane grafted EPDM (SiEPDM) composites incorporated with silica is prepared via a solvent-free, one-step reactive mixing process. The silane grafting and silica filler bonding are characterized using Fourier transform infrared spectroscopy and X-ray photoelectron spectroscopy. The mechanical properties of the developed composites are examined. The fracture morphology is observed using an environmental scanning electron microscopy. The rheology and thermomechanical properties are evaluated by using a rotational rheometer and dynamic mechanical analyzer. Notably, a robust bonding between silica and the grafted silane is established, yielding a crosslinking network within the composite structure. This phenomenon is substantiated by the observed gel efficiency and rheology behavior. Consequently, a pronounced augmentation of up to 75% in tensile strength and 29% in tear strength are observed in the optimized SiEPDM-silica composites, distinguishing them from their EPDM-silica counterparts. The introduction of paraffin oil contributes to enhanced processability; however, it is concomitant with a reduction in gel efficiency and associated mechanical properties. Furthermore, subsequent UV weathering test unveils that the SiEPDM-silica composites exhibit the highest levels of residual tensile strength and modulus, indicative of their exceptional UV stability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".