Optimization of growth parameters to obtain epitaxial large area growth of molybdenum disulfide using pulsed laser deposition
Bibliographic record
Abstract
2D transition metal dichalcogenides (TMDCs) are promising materials for device applications owing to their electronic, optical, and material properties varying with the number of monolayers. Synthesis of large area crystalline TMDC thin films is still challenging with techniques such as exfoliation and chemical vapor growth owing to the uncontrollability of deposition area and high temperature growths with toxic precursors, respectively. Pulsed laser deposition (PLD) is a technique that can overcome these challenges owing to stoichiometric layer by layer growth control by optimizing the growth parameters. In this study, we optimize parameters such as temperature, post-growth annealing, inert gas pressure, and substrate–target distance during PLD growth of MoS2 to obtain uniform and highly crystalline thin films on an ∼1 in.2 substrate. The optimized growth conditions are 800 °C with a 30 min post-growth annealing at a laser fluence of 2.2 J/cm2 with a substrate–target distance of 5 cm and 0.5 mTorr of argon partial pressure. An RMS roughness of 0.17 nm was obtained for 3 nm (4 monolayers) thick MoS2 films with a thin film conductivity of ∼4000 S/m.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".