Nanoscale toughening of ultrathin graphene oxide-polymer composites: mechanochemical insights into hydrogen-bonding/van der Waals interactions, polymer chain alignment, and steric parameters
Bibliographic record
Abstract
This paper describes a systematic study on the nanoscale toughening of monolayer graphene oxide (GO) by an ultra-thin polymer adlayer, which impedes the propagation of cracks during intraplanar fracture. Using molecular dynamics simulations, the crack-bridging capabilities of a library of five hydrogen-bonding-capable polymers are explored against an epoxide-rich GO substrate. The best crack-bridging effect is found in polymers with functional groups that can both donate/accept hydrogen atoms and have better capability to form cooperative hydrogen bonds. Aligning the chains of poly(acrylic acid) orthogonally to the crack propagation direction significantly enhances the fracture toughness of monolayer GO (by 310%) in comparison to that for an adlayer with randomly arranged chains (180% enhancement). Notably, van der Waals interactions, which are seldom highlighted in the fabrication of strong GO-polymer interfaces, are found to also provide significant crack-bridging capabilities when the polymers possess large side groups. These results pave the way for a set of design criteria that can help in remediating the intrinsically brittle mechanical behavior of two-dimensional materials, a barrier that currently restricts their potential applications.
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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".