Tailoring of modal losses in anisotropic 2D material ribbons by regulating material absorption
Bibliographic record
Abstract
In plasmonic guided waves, material absorption is generally an unwanted shortcoming that degrades the quality of plasmon modes by greatly curtailing their propagation distance. In this work, we explore the general features of the modal properties supported by 2D anisotropic materials and elucidate how the material’s in-plane anisotropy can offer a previously untenable level of control or tailoring over plasmonic waveguide design. In particular, we find that utilizing the in-plane anisotropy of anisotropic 2D materials in the conductivity of ribbon films, it is possible to significantly manipulate the modal loss of the plasmonic guided modes by increasing the material absorption of the 2D materials. The physical root cause of this behavior is control over the various electric field components within the film by utilizing the material dispersion of the anisotropic film. This control allows for the ability to manipulate at will, for a wide a range of structure parameters and wavelengths, the net field within the ribbon arising from the interplay between the two edge modes, which constitute the film edges. The findings thus unlock beneficial capabilities offered by using natural 2D anisotropic materials such as black phosphorous in the design of active/passive nano-scale circuits. Furthermore, when these effects are employed in gain media composed of 2D materials, the ability for the realization of low modal loss plasmonic modes concomitant with the presence of substantial material absorption can introduce a new design paradigm that promises novel and enhanced functionality.
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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".