Nanocomposite of Fullerenes and Natural Rubbers: MARTINI Force Field Molecular Dynamics Simulations
Bibliographic record
Abstract
The mechanical properties of natural rubber (NR) composites depend on many factors, including the filler loading, filler size, filler dispersion, and filler-rubber interfacial interactions. Thus, NR composites with nano-sized fillers have attracted a great deal of attention for improving properties such as stiffness, chemical resistance, and high wear resistance. Here, a coarse-grained (CG) model based on the MARTINI force field version 2.1 has been developed and deployed for simulations of cis-1,4-polyisoprene (cis-PI). The model shows qualitative and quantitative agreement with the experiments and atomistic simulations. Interestingly, only a 0.5% difference with respect to the experimental result of the glass transition temperature (Tg) of the cis-PI in the melts was observed. In addition, the mechanical and thermodynamical properties of the cis-PI-fullerene(C60) composites were investigated. Coarse-grained molecular dynamics (MD) simulations of cis-PI-C60 composites with varying fullerene concentrations (0–32 parts per hundred of rubber; phr) were performed over 200 microseconds. The structural, mechanical, and thermal properties of the composites were determined. The density, bulk modulus, thermal expansion, heat capacity, and Tg of the NR composites were found to increase with increasing C60 concentration. The presence of C60 resulted in a slight increasing of the end-to-end distance and radius of the gyration of the cis-PI chains. The contribution of C60 and cis-PI interfacial interactions led to an enhancement of the bulk moduli of the composites. This model should be helpful in the investigations and design of effective fillers of NR-C60 composites for improving their 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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 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".