Recent Progress in the Synthesis of MoS2 Thin Films for Sensing, Photovoltaic and Plasmonic Applications: A Review
Bibliographic record
Abstract
In the surge of recent successes of 2D materials following the rise of graphene, molybdenum disulfide (2D-MoS2) has been attracting growing attention from both fundamental and applications viewpoints, owing to the combination of its unique nanoscale properties. For instance, the bandgap of 2D-MoS2, which changes from direct (in the bulk form) to indirect for ultrathin films (few layers), offers new prospects for various applications in optoelectronics. In this review, we present the latest scientific advances in the field of synthesis and characterization of 2D-MoS2 films while highlighting some of their applications in energy harvesting, gas sensing, and plasmonic devices. A survey of the physical and chemical processing routes of 2D-MoS2 is presented first, followed by a detailed description and listing of the most relevant characterization techniques used to study the MoS2 nanomaterial as well as theoretical simulations of its interesting optical properties. Finally, the challenges related to the synthesis of high quality and fairly controllable MoS2 thin films are discussed along with their integration into novel functional devices.
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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 | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".