Enhanced Luminescence, Collective Heating, and Nanothermometry in an Ensemble System Composed of Lanthanide‐Doped Upconverting Nanoparticles and Gold Nanorods
Bibliographic record
Abstract
A combined system of gold nanorods and NaGdF4:Er3+/Yb3+ upconverting nanoparticles with double functionality, luminescence enhancement, and monitored heating is introduced. The paired nanostructures could become an excellent optical heater with thermal probe incorporated. To study their interaction, the longitudinal surface plasmon resonance of the gold nanorods is tuned to 980 nm, in resonance with the Yb3+ absorption wavelength, so they can be simultaneously excited. Gold nanorods create a localized electromagnetic field that enhances the emission intensity from upconverting nanoparticles. This luminescence enhancement is shown to depend on the interparticle distance and excitation power and, in this system, reaches a maximum enhancement of 9 for the green emission of Er3+ ions. At the same time, evidence of strong collective heating from the gold nanorods is demonstrated. The temperature can be controlled by changing the excitation power and measured in situ via the Er3+ thermally sensitive luminescence. At high excitation powers, the heating can trigger a deformation of the gold nanorods, which limits the maximum temperature achievable in the system to 160 °C. Combining these nanostructures provides an all‐optical heating system with improved emission intensity that can monitor the temperature achieved.
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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".