Resonant Plasmon-Enhanced Upconversion in Monolayers of Core–Shell Nanocrystals: Role of Shell Thickness
Bibliographic record
Abstract
The upconversion luminescence (UCL) of colloidal lanthanide-doped upconversion nanocrystals (UCNCs) can be improved either by precise encapsulation of the surface by optically inert shells around the core, by an alteration of the nearby environment via metal nanoparticles, or by a combination of both. Considering their potential importance in crystalline silicon photovoltaics, the present study investigates both effects for two-dimensional arrangements of UCNCs. Using excitation light of 1500 nm wavelength, we study the variation in the upconversion luminescence from an Er 3+ -doped NaYF 4 core as a function of the thickness of a NaLuF 4 shell in colloidal solutions as well as in spin-cast-assisted self-assembled monolayers of UCNCs. The observed UCL yields and decay times of Er 3+ ions of the UCNCs increase with increasing shell thickness in both cases, and nearly no variation in decay times is observed in the transition of the UCNCs from solution to film configurations. The luminescence efficiency of the UCNC monolayers is further enhanced by electron-beam-lithographic-designed Au nanodiscs deposited either on top of or buried within the monolayer. It is observed that the improvement by the nanocrystal shells is greater than that of the Au nanodiscs.
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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".