Mechanical instability driven self-assembly and architecturing of 2D materials
Bibliographic record
Abstract
Abstract Two-dimensional (2D) materials have been well studied for their diverse and impressive properties and superlative mechanical strength. Their atomic thinness and weak van der Waals interaction, while fascinating and unique, dictate their tendency to exhibit out of plane morphologies such as bending, buckling, folding, rippling, scrolling, and wrinkling, etc. In this review, we discuss the mechanisms behind these instability driven morphologies and the resultant phenomena that arise. We then survey methods to manipulate them especially in a scalable manner, and elucidate some interesting applications uniquely enabled by these structures. Contrary to conventional wisdom, the deterministic control of these features has great implications for the local and overall material properties due to heterogeneous distribution of stresses and strains. The introduction of deformable and shape memory substrates especially allow for facile and large scale synthesis of various types of out of plane morphologies. We show that a variety of exciting phenomena and applications arise, including tunable surfaces and coatings, robust devices and electronics, adaptive optoelectronics, material toughening, energy storage, and chemical sensing. This new perspective on these otherwise nuisance thin-film phenomena enable new tools for future materials discovery, design, and synthesis with the ever growing library of 2D atomically thin materials.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".