Subtissue Thermal Sensing Based on Neodymium-Doped LaF<sub>3</sub>Nanoparticles
Bibliographic record
Abstract
In this work, we report the multifunctional character of neodymium-doped LaF₃ core/shell nanoparticles. Because of the spectral overlap of the neodymium emission bands with the transparency windows of human tissues, these nanoparticles emerge as relevant subtissue optical probes. For neodymium contents optimizing the luminescence brightness of Nd³⁺:LaF₃ nanoparticles, subtissue penetration depths of several millimeters have been demonstrated. At the same time, it has been found that the infrared emission bands of Nd³⁺:LaF₃ nanoparticles show a remarkable thermal sensitivity, so that they can be advantageously used as luminescent nanothermometers for subtissue thermal sensing. This possibility has been demonstrated in this work: Nd³⁺:LaF₃ nanoparticles have been used to provide optical control over subtissue temperature in a single-beam plasmonic-mediated heating experiment. In this experiment, gold nanorods are used as nanoheaters while thermal reading is performed by the Nd³⁺:LaF₃ nanoparticles. The possibility of a real single-beam-controlled subtissue hyperthermia process is, therefore, pointed out.
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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 | 0.001 |
| 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".