Photonics and optoelectronics of two-dimensional materials beyond graphene
Bibliographic record
Abstract
Apart from conventional materials, the study of two-dimensional (2D) materials has emerged as a significant field of study for a variety of applications. Graphene-like 2D materials are important elements of potential optoelectronics applications due to their exceptional electronic and optical properties. The processing of these materials towards the realization of devices has been one of the main motivations for the recent development of photonics and optoelectronics. The recent progress in photonic devices based on graphene-like 2D materials, especially topological insulators (TIs) and transition metal dichalcogenides (TMDs) with the methodology level discussions from the viewpoint of state-of-the-art designs in device geometry and materials are detailed in this review. We have started the article with an overview of the electronic properties and continued by highlighting their linear and nonlinear optical properties. The production of TIs and TMDs by different methods is detailed. The following main applications focused towards device fabrication are elaborated: (1) photodetectors, (2) photovoltaic devices, (3) light-emitting devices, (4) flexible devices and (5) laser applications. The possibility of employing these 2D materials in different fields is also suggested based on their properties in the prospective part. This review will not only greatly complement the detailed knowledge of the device physics of these materials, but also provide contemporary perception for the researchers who wish to consider these materials for various applications by following the path of graphene.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".