(Invited) Luminescence Nanothermometers: Using Light to Detect Temperature
Bibliographic record
Abstract
Rare earth doped nanoparticles have recently emerged as versatile luminescent probes for a number of biological applications resulting from their interesting photophysical properties. These nanoparticles can be excited with near-infrared (NIR) light, which is a strict requirement for biomedical applications due its light penetration capabilities. Furthermore, rare earth doped nanoparticles possess a multitude of 4f electronic energy states and excitation with NIR light can therefore lead to different excitation mechanisms. For example, following NIR excitation, the nanoparticles can undergo upconversion where multiple emissions are observed with energies higher than the excitation wavelength. Also, they can undergo conventional luminescence where emission at lower energies than the excitation wavelength can be observed (known as downshifted luminescence). Here, we show that it is possible to harness these various emissions (upconverted and downshifted) to design luminescence nanothermometers capable of detecting temperature in living organisms.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".