Plasma Processing and Treatment of 2D Transition Metal Dichalcogenides: Tuning Properties and Defect Engineering
Bibliographic record
Abstract
Two-dimensional transition metal dichalcogenides (TMDs) offer fascinating opportunities for fundamental nanoscale science and various technological applications. They are a promising platform for next generation optoelectronics and energy harvesting devices due to their exceptional characteristics at the nanoscale, such as tunable bandgap and strong light-matter interactions. The performance of TMD-based devices is mainly governed by the structure, composition, size, defects, and the state of their interfaces. Many properties of TMDs are influenced by the method of synthesis so numerous studies have focused on processing high-quality TMDs with controlled physicochemical properties. Plasma-based methods are cost-effective, well controllable, and scalable techniques that have recently attracted researchers' interest in the synthesis and modification of 2D TMDs. TMDs' reactivity toward plasma offers numerous opportunities to modify the surface of TMDs, including functionalization, defect engineering, doping, oxidation, phase engineering, etching, healing, morphological changes, and altering the surface energy. Here we comprehensively review all roles of plasma in the realm of TMDs. The fundamental science behind plasma processing and modification of TMDs and their applications in different fields are presented and discussed. Future perspectives and challenges are highlighted to demonstrate the prominence of TMDs and the importance of surface engineering in next-generation optoelectronic applications.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".