Mechanically reliable and electronically uniform monolayer MoS2 by passivation and defect healing
Bibliographic record
Abstract
Monolayer molybdenum disulfide (MoS₂), a two-dimensional transition metal dichalcogenide (2D TMD), is at the forefront of logic device scaling efforts due to its semiconducting properties, good carrier mobility, and atomically thin structure. However, the high defect density of monolayer MoS2 hinders its reliability for long-term, device-scale applications. Here, we show that a superacid treatment, previously shown to enhance the photoluminescence efficiency of sulfur-based 2D TMDs by two orders of magnitude, also improves the mechanical reliability and electronic uniformity of monolayer MoS₂. Treated samples exhibit a ~2× increase in static fatigue reliability, a ~10× improvement in cyclic wear reliability, and no premature failure during mechanical testing. X-ray photoelectron spectroscopy confirms reduced defect density, while ab initio molecular dynamics and density functional theory suggest that passivation delays failure propagation and reduces vacancy-induced stress. Finally, atomic-resolution conductive atomic force microscopy shows a drastically more uniform current distribution due to elimination of midgap states. Nonoxidizing organic superacid treatments of 2D transition metal dichalcogenides have been shown to drastically boost their electrical and optical characteristics while passivating and repairing defects. Here, the authors demonstrate that these treatments can also be leveraged to boost the mechanical reliability and atomic-scale electronic uniformity of MoS2 monolayers.
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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".