Influence of Crosslink Density on Mechanical Properties of Natural Rubber Vulcanizates
Bibliographic record
Abstract
Crosslink density is an important structural parameter for cured rubber. Natural rubber (NR) vulcanizates with different crosslink densities were obtained through using different sulfur and accelerator amounts and different accelerator types. The crosslink density was characterized by an 1 H-NMR technique and its influence on mechanical properties, such as Shore A hardness, 300% modulus, tensile strength, and elongation at break, of NR vulcanizates was investigated. The results showed that both the sulfur amount and the accelerator type and amount had an influence on the crosslink density of the NR networks. The relationship between total crosslink density and mechanical properties was also studied. The results, by changing either the sulfur or the accelerator amount, showed that tensile strength of NR vulcanizates reached maximum value when the total crosslink density was around 13.5 × 10−5 mol/cm3, equivalently the average molecular weight of the intercrosslink chains (Mc) was around 7000 g/mol. The maximum value of tensile strength came from the balance between contributions of crosslink joints and stretch-induced orientation and/or crystallization of intercrosslink chains. The study on influence of total crosslink density on Shore A hardness and 300% modulus of NR vulcanizates showed that they both increased linearly with the crosslink density, the slopes were 2.7 ∼ 3.0 cm3/10−5 mol and 0.27 ∼ 0.31 MPa cm3/10−5 mol for Shore A hardness and 300% modulus, respectively, whether the crosslink density was varied by sulfur or accelerator.
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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".