Atomistic modeling of graphene/hexagonal boron nitride polymer nanocomposites: a review
Bibliographic record
Abstract
Due to their exceptional properties, graphene and hexagonal boron nitride (h‐BN) nanofillers are emerging as potential candidates for reinforcing the polymer‐based nanocomposites. Graphene and h‐BN have comparable mechanical and thermal properties, whereas due to high band gap in h‐BN (~5 eV), have contrasting electrical conductivities. Atomistic modeling techniques are viable alternatives to the costly and time‐consuming experimental techniques, and are accurate enough to predict the mechanical properties, fracture toughness, and thermal conductivities of graphene and h‐BN‐based nanocomposites. Success of any atomistic model entirely depends on the type of interatomic potential used in simulations. This review article encompasses different types of interatomic potentials that can be used for the modeling of graphene, h‐BN, and corresponding nanocomposites, and further elaborates on developments and challenges associated with the classical mechanics‐based approach along with synergic effects of these nano reinforcements on host polymer matrix. This article is categorized under: Molecular and Statistical Mechanics > Molecular Mechanics Structure and Mechanism > Computational Materials Science Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".