Fully atomistic molecular dynamics simulation of chemically modified natural rubber with hydrogen-bonding network
Bibliographic record
Abstract
The incorporation of sacrificial hydrogen bonds is critical for the development of rubber materials with exceptional properties. However, the molecular-level mechanism by which sacrificial hydrogen bonds affect material properties is still poorly understood, significantly hindering the advancement of high-performance rubber materials. In this study, we employ fully atomistic molecular dynamics simulations to elucidate the impact of hydrogen bonds on structure, mechanical properties and linear viscoelasticity . Increasing the modified repeating unit ratio α leads to a rise in hydrogen bond content, particularly inter-chain hydrogen bonds, and the modified groups cluster due to hydrogen bonds. This hydrogen bond crosslinking network constrains the movement of the molecular chains , increasing the glass transition temperature . Surprisingly, the mechanical properties show an initial increase followed by a decrease as α increases, and the system with α = 6 % exhibits the optimal mechanical properties. This trend is due to the regulation of mechanical properties by the non-bond energy increment and bond orientation, with the system with α = 6 % exhibiting the maximum non-bonded energy increment and bond orientation. Increasing the self-healing temperature and time improves self-healing efficiency, essentially governed by the diffusion of molecular chains . The system with higher α exhibits a higher stress relaxation modulus and more extended stress relaxation plateau, attributable to a more complex hydrogen bond crosslinking network. Additionally, higher α values result in higher energy storage modulus , loss modulus , and complex viscosity but can effectively reduce the loss factor. Therefore, adjusting α can achieve a material with robust mechanical properties and low mechanical losses . Overall, we successfully establish the relationship between structure and properties and guide the designing and synthesizing of rubber materials with even better properties.
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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 | 0.000 | 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) | 0.000 | 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".