Recent advances in the mechanics of 2D materials
Bibliographic record
Abstract
Abstract The exceptional physical properties and unique layered structure of two-dimensional (2D) materials have made this class of materials great candidates for applications in electronics, energy conversion/storage devices, nanocomposites, and multifunctional coatings, among others. At the center of this application space, mechanical properties play a vital role in materials design, manufacturing, integration and performance. The emergence of 2D materials has also sparked broad scientific inquiry, with new understanding of mechanical interactions between 2D structures and interfaces being of great interest to the community. Building on the dramatic expansion of recent research activities, here we review significant advances in the understanding of the elastic properties, in-plane failures, fatigue performance, interfacial shear/friction, and adhesion behavior of 2D materials. In this article, special emphasis is placed on some new 2D materials, novel characterization techniques and computational methods, as well as insights into deformation and failure mechanisms. A deep understanding of the intrinsic and extrinsic factors that govern 2D material mechanics is further provided, in the hopes that the community may draw design strategies for structural and interfacial engineering of 2D material systems. We end this review article with a discussion of our perspective on the state of the field and outlook on areas for future research directions.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".