Surface-Sensitive Raman Response of Metal-Supported Monolayer MoS<sub>2</sub>
Bibliographic record
Abstract
The Raman spectrum of monolayer (ML) MoS 2 is remarkably affected by the interaction with metals. In this work, we studied ML-MoS 2 supported by the Ag(111) and Ag(110) surfaces by using a combined experimental and theoretical approach. The MoS 2 layer was directly grown on atomically clean Ag(111) and Ag(110) surfaces by pulsed laser deposition, followed by in situ thermal annealing under ultrahigh vacuum conditions. The morphology and structure of the two systems were characterized in situ by scanning tunneling microscopy, providing atomic-scale information on the relation between the MoS 2 lattice and the underlying surface. Raman spectroscopy revealed differences between the two MoS 2 –metal interfaces, especially concerning the behavior of the out-of-plane A 1 ′ vibrational mode, which splits into two contributions on Ag(110). The metal-induced effects on MoS 2 vibrational modes are further evidenced by transferring MoS 2 onto a more inert substrate (SiO 2 /Si), where the MoS 2 Raman response displays a more “freestanding-like” behavior. The experimental data were interpreted with the support of ab initio calculations of the vibrational modes, which provided insight into the effect of interface properties, such as strain and out-of-plane distortion. Our results highlight the influence of the interaction with metals on MoS 2 vibrational properties and show the high sensitivity of MoS 2 Raman modes to the surface structure of the supporting metal.
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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".