Photoluminescence Enhancement at Telecom Wavelengths from PbS/CdS Quantum Dots coupled to a Plasmonic Crescent Metasurface
Bibliographic record
Abstract
Efficient near-infrared (NIR) light sources are essential for a wide range of applications such as telecommunications, optoelectronic devices, biomedical sciences, infrared imaging, and machine vision. Colloidal quantum dots (QDs) have emerged as a promising platform for NIR technologies due to their tunable optical properties across the NIR spectrum, compatibility with silicon-based technology infrastructure, and ease of large-scale integration into nanophotonic systems. Coupling colloidal QDs with plasmonic structures provides an enhanced control over their optical properties. In this work, we investigate the coupling of a gold plasmonic crescent metasurface with NIR-emitting colloidal PbS/CdS QDs, at the telecommunication wavelength of 1.55 μm. The metasurface was specifically designed to allow for the selective excitation photoluminescence (PL) enhancement with polarization control, capitalizing on the anisotropic nature of the plasmonic crescents. Maximum PL enhancement factors of 1.6 were observed, with a strong dependence on the excitation wavelength and polarization. These findings, supported by full-wave three-dimensional finite-difference-time-domain (FDTD) numerical simulations, offer strategies to control and optimize the performance of colloidal QD-based NIR light sources for a wide range of applications.
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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".