Atomic Engineering of Chalcogenide Layer in Transition Metal Chalcogenides
Bibliographic record
Abstract
Atomically thin transition metal chalcogenides (TMDs), members of two-dimensional (2D) material family, have received great attention due to their novel optoelectronic and electronic properties. CVD technique have been well developed to synthesize monolayer TMDs, such as MoS2, WS2, WSe2, and MoSe2 where transition metals are sandwiched by the same chalcogenide layers1. Band gas of those intrinsic materials can be further manipulated by precisely compositional control of TMD alloy structure2. For example, band gap of MoSxSe2-x sat in between MoS2 and MoSe2. Although great effort had paid in fabricating such complex 2D structures had been fabricated, compositional control of TMD along the vertical direction remains challenging. Such asymmetrical composition distribution could result in additional polarity along the vertical direction and extend its application to spintronics3. Here, we develop a method that can precisely manipulate arrangement of chalcogenide atoms (S and Se) along the vertical direction of TMD by applying plasma thinning along with selenization (or sulfurization). This new strategy is not only to control the composition of upmost chalcogenide layer but also able to fabricate MoSSe Janus structure. In this Janus structure, the transition metals are sandwiched by selenium at upmost and sulfur at bottom. Raman spectra is applied to track and investigate the mechanism. Furthermore, this approach can be generalized to other 2D materials. Janus WSSe is also successfully achieved by applying similar plasma treatment and corresponding chalcogenization. References: 1. Nat. Nanotechnol. 2012, 7, 699. 2. Adv. Mater. 2014, 26, 2648. 3. EPL, 2013, 102, 57001.
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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".