Rapid nucleation of ZnO on MoS<sub>2</sub> and WS<sub>2</sub> using an atmospheric-pressure spatial atomic layer deposition system
Bibliographic record
Abstract
Abstract Uniform deposition of metal oxides on 2D materials, while preserving their structural integrity, is a crucial step to realize the integration of 2D materials in practical devices. In this study, we demonstrate the rapid nucleation of ZnO using atmospheric-pressure spatial chemical vapor deposition (AP-SCVD) on 2D transition metal dichalcogenides MoS2 and WS2. The high precursor partial pressure and uniform precursor delivery afforded by the AP-SCVD process, as compared to conventional atomic layer deposition (ALD), led to rapid ZnO nucleation on both CVD-grown MoS2 and WS2 in as little as 5 AP-SCVD oscillations and complete film closure was achieved on CVD-grown WS2 flakes in less than 60 AP-SCVD oscillations. The ZnO nuclei formed larger interconnected clusters on MoS2, whereas more-isolated islands were formed on the WS2. Raman and photoluminescence (PL) spectroscopy revealed that AP-SCVD is a benign process that does not damage the underlying 2D materials and rather helps to passivate defects via oxygen/water adsorption from the air, when performed in an appropriate temperature window. Deposition of the ZnO was found to impact the optical and structural properties of CVD-grown MoS2 and WS2 differently. For the MoS2–ZnO heterostructure, electron doping and strain dominate, resulting in a reduction in the PL of MoS2, whereas for the WS2–ZnO, strain and dielectric screening have a larger impact, resulting in an enhanced PL.
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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".