Controlled Sulfurization Process for the Synthesis of Large Area MoS<sub>2</sub> Films and MoS<sub>2</sub>/WS<sub>2</sub> Heterostructures
Bibliographic record
Abstract
Large area MoS2 films with tunable physical‐chemical properties are grown on dielectric substrates by annealing of ultrathin Mo layers in the presence of a sulfur‐containing gaseous precursor. Different growth conditions are found to have a significant impact on material properties, including chemical composition, roughness, and grain sizes, thus shedding light on critical parameters that govern sulfurization processes for the synthesis of large area 2D transition metal dichalcogenides. Optimized growth conditions in combination with the use of single crystal sapphire substrates with atomically flat interface result in the formation of oriented MoS2 films with improved quality and electrical performance. On the basis of this versatile synthesis technique, an original double‐step process is presented for the synthesis of WS2/MoS2 vertical heterostructures. Good uniformity of layers over large area has enabled first isolation of defects by electron spin resonance spectroscopy with densities correlated with mobility degradation and the first experimental characterization of the band alignment at the interfaces of MoS2, WS2, and their vertical stacks with the underlying SiO2 insulator.
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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".