Plasmon–Exciton–Driven Modulation of the Electronic and Optical Properties of MoS <sub>2</sub> via Cayley Tree Fractal Nanostructures
Bibliographic record
Abstract
ABSTRACT Two‐dimensional transition metal dichalcogenide (TMD) materials, such as molybdenum disulfide (MoS 2 ), have been explored as potential candidates for the next generation of semiconducting devices. This widespread interest arises from TMD's high transparency, large charge carrier mobility, and tunable electronic structure. One approach of enhancing their performance and modulating their electronic properties can be achieved through the coupling of the exciton modes of the 2D materials with the plasmons modes from a nanostructured metal, thus yielding the formation of an exciton–polariton quasiparticle. In this work, gold fractal patterns were inscribed onto monolayer MoS 2 via electron beam lithography (EBL). A Cayley tree plasmonic structure was selected because it enables versatile control over the plasmon frequency and its matching with the excitonic modes of MoS 2 . The hybrid structure was investigated using a combination of tip‐enhanced Raman spectroscopy (TERS), tip‐enhanced photoluminescence (TEPL), and Kelvin probe force microscopy. These measurements reveal an enhanced photoluminescence and spectral shift in the excitonic modes associated with an increased bandgap and a charge transfer between the plasmonic and excitonic structure.
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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) | 0.000 | 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".