Interplay between Thermal Stress and Interface Binding on Fracture of WS<sub>2</sub> Monolayer with Triangular Voids
Bibliographic record
Abstract
The defect engineering of two-dimensional (2D) materials has become a pivotal strategy for tuning the electrical and optical properties of the material. However, the reliable application of these atomically thin materials in practical devices require careful control of structural defects to avoid premature failure. Herein, a systematic investigation is presented to delineate the complex interactions among structural defects, the role of thermal mismatch between WS 2 monolayer and different substrates, and their consequent effect on the fracture behavior of the monolayer. Detailed microscopic and Raman/PL spectroscopic observations enabled a direct correlation between thermal mismatch stress and crack patterns originating from the corner of faceted voids in the WS 2 monolayer. Aberration-corrected STEM-HAADF imaging reveals the tensile strain localization around the faceted void corners. Density functional theory (DFT) simulations on interfacial interaction between the substrate (Silicon and sapphire -Al 2 O 3 ) and monolayer WS 2 revealed a binding energy between WS 2 and Si substrate is 20 times higher than that with a sapphire substrate. This increased interfacial interaction in WS 2 and substrate-aided thermal mismatch stress arising due to difference in thermal expansion coefficient to a maximum extent leading to fracture in monolayer WS 2 . Finite element simulations revealed the stress distribution near the void in the WS 2 monolayer, where the maximum stress was concentrated at the void tip.
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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) | 0.002 | 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".