Luminescence of Eu<sup>3+</sup> Activated CaF<sub>2</sub> and SrF<sub>2</sub> Nanoparticles: Effect of the Particle Size and Codoping with Alkaline Ions
Bibliographic record
Abstract
Eu 3+ doped CaF 2 and SrF 2 nanoparticles were synthesized through a facile hydrothermal technique, using citrate ions as capping agents and Na + or K + as charge compensator ions. A proper tuning of the reaction time can modulate the nanoparticle size, from few to several tens of nanometers. Analysis of EXAFS spectra indicate that the Eu 3+ ions enter into the fluorite CaF 2 and SrF 2 structure as substitutional defects on the metal site. Laser site selective spectroscopy demonstrates that the Eu 3+ ions are mainly accommodated in two sites with different symmetries. The relative site distribution for lanthanide ions depends on the nanoparticle size, and higher symmetry Eu 3+ sites are prevalent for bigger nanoparticles. Eu 3+ ions in high symmetry sites present lifetimes of the 5 D 0 level around 27 ms, among the longest lifetimes found in the literature for Eu 3+ activated materials. As a proof of concept of possible use of the Eu 3+ activated alkaline-earth fluoride nanoparticles in nanomedicine, the red luminescence generated by two-photon absorption using pulsed laser excitation at 790 nm (in the first biological window) has been detected. The long Eu 3+ lifetimes suggest that the present nanomaterials can be interesting as luminescent probes in time-resolved fluorescence techniques in biomedical imaging (e.g., FLIM) where fast autofluorescence is a drawback to avoid.
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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".