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 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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".