Thermometric Analysis of Nanoaperture-Trapped Erbium-Containing Nanocrystals
Bibliographic record
Abstract
Temperature changes in plasmonic traps can affect biomolecules and quantum emitters; therefore, several works have sought out the capability of measuring the local temperature. Those works used ionic nanopore currents, fluorescence emission variations, and fluorescence-based diffusion tracking to measure the temperature dependence of shaped nanoapertures in metal films. Here, we make use of a stable erbium-containing NaYF 4 nanocrystal that gives local temperature dependence while trapped in the nanoaperture hot spot. Ratiometric analysis of the emission at different wavelengths gives local temperature variation. Since the gold film dominates the thermal characteristic, we find that films of thickness 70, 100, and 130 nm give 0.64, 0.37, and 0.25 K/mW temperature change with laser power. Therefore, using thicker films can be effective in reducing the heating when it is not desired.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.005 |
| 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 teacher head, 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".