Synergistic enhancement of photoluminesent intensity in monolayer molybdenum disulfide embedded with plasmonic nanostructures for catalytic sensing
Bibliographic record
Abstract
Abstract Enhancing photoluminescence (PL) of semiconducting 2D materials is proven essential for many applications related to optoelectronics and sensing. Here, we demonstrate synergistic PL enhancement in 2D materials by incorporating silver plasmonic nanodiscs in defect-induced monolayer molybdenum disulfide (MoS 2 ) for luminescent quench sensing of dopamine with high accuracy and selectivity. We develop a hole-array perforated 2D MoS 2 embedded with plasmonic silver dimers to harmonize the surface plasmon resonance with the PL wavelength of monolayer MoS 2 , which enhance it by 56-fold. We implemented the optimized perforated MoS 2 /dimers platform as a catalytic assay for on-chip PL quenching detection of dopamine, achieving a low limit of detection of 9.3 nM. This approach opens avenues of high-performance molecular sensing applications by improving the PL emission of 2D materials.
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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".