Synergistic Effect of Zinc Oxide and Magnesium Oxide Co-Cure Activators on Polybutadiene Rubber Vulcanization: Mechanical Properties and Thermal Characteristics
Bibliographic record
Abstract
Zinc oxide (ZnO) is widely recognized as an effective cure activator in the sulphur vulcanization of polybutadiene rubber (PBR). However, its high toxicity to aquatic organisms has raised environmental concerns, prompting the search for non-toxic alternatives. Despite this, no industrially viable substitute has been identified. This study explores the potential of using a combination of ZnO and magnesium oxide (MgO) to reduce ZnO levels while enhancing vulcanization performance. The crosslinking density and thermal stability of the vulcanized PBR were assessed to evaluate the efficacy of MgO. The results demonstrate that the inclusion of MgO as a co-activator significantly accelerates the vulcanization rate. Specifically, formulations with 60% MgO exhibited a tensile strength of 1.1 MPa, elongation at break of 111%, and hardness of 46 Shore A. When using MgO exclusively, the material achieved a tensile strength of 1.4 MPa, elongation at break of 212%, and hardness of 43 Shore A, with an abrasion loss of 64.82 mm³. Swelling studies revealed that crosslink density was highest in the PBR formulation with 3 phr MgO and 2 phr ZnO, exhibiting the lowest swelling index (3.10). As MgO content increased, the swelling index also rose, indicating reduced crosslink density. The highest swelling index (4.24) was observed in the formulation with 5 phr MgO, confirming weaker crosslink formation. These results highlight that MgO alone lacks the ability to form an effective sulfurating complex, but when combined with ZnO, it enhances crosslinking efficiency and vulcanization performance. The use of MgO, either alone or in combination with ZnO, presents a viable approach for developing environmentally friendly PBR compounds with potential applications in high-performance elastomers such as tires.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".