Simultaneous crosslinking and foaming of ethylene‐propylene diene terpolymers (<scp>EPDM</scp>) organoclay composite foams
Bibliographic record
Abstract
Abstract This study used ethylene‐propylene diene monomers (EPDM), an elastomer, to develop foams incorporating clay as filler using simple compounding, chemical foaming, and peroxide‐mediated light crosslinking methods. The low‐temperature batch mixer was employed for compounding azodicarbonamide (ADC) as a foaming agent, dicumyl peroxide (DCP) as a crosslinker, followed by compression molding for sheet preparation and the high‐temperature foaming process. Fourier transform infrared spectroscopy (FTIR) equipped with a hot stage attenuated total reflection (ATR) was employed to elucidate the ADC decomposition and foaming. The DCP (0.25 phr) content was optimized based on the foaming expansion ratio (>4) and stability, which was further optimized in conjuncture with clay to generate high‐performance composite foams. The incorporation of 1% organoclay enhances the expansion ratio and specific tensile strength by >7 and ~ 270%, respectively, compared with EPDM foams without clay while displaying a density of 0.11 g/cm3. Overall, the developed foaming process can be extended to other elastomeric polymers for various applications such as protective gears (e.g., helmets and shin guards), thermal/sound insulation, packaging/containment, footwear soles, oil absorption, etc.
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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".