Naturally occurring halloysite nanotubes for enhanced durability of natural rubber/ethylene propylene diene monomer rubber vulcanizate
Bibliographic record
Abstract
Abstract A facile approach of using halloysite nanotubes (HNTs) was proposed to address the durability performance demands of natural rubber (NR)/ethylene propylene diene monomer rubber (EPDM) blends and to protect them from the deleterious effects of the service environment including ozone, chemicals, abrasion, and cyclic loading. The introduction of HNTs substantially improved the stability of NR/EPDM when exposed to ozone (over fourfold enhancement with the addition of 5 phr HNTs). Moreover, the HNT‐filled NR/EPDM vulcanizates offered approximately 66% reduction in the solvent‐mediated swelling in comparison to the unfilled sample. Fatigue life studies showed that the HNT‐reinforced NR/EPDM composite could withstand 30% more cycles to failure than the un‐reinforced NR/EPDM blend. The effect of various HNT loading on the morphological, mechanical, physical, and rheological properties of nanocomposite vulcanizates based on NR/EPDM was also investigated. The morphological investigations revealed that the introduction of HNT into the NR/EPDM rubber matrix caused a rough morphology in fracture surface and a well‐dispersed structure was obtained with the addition of up to 5 phr of HNTs. These findings were further supported by rheological, mechanical, and thermodynamical results.
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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".