Dipicolinate Sensitization of Europium Luminescence in Dispersible 5%Eu:LaF<sub>3</sub> Nanoparticles
Bibliographic record
Abstract
An earlier synthetic procedure was adapted to produce water-dispersible, citrate-stabilized LaF 3 nanoparticles of 3−4 nm diameter, which exhibit a high degree of crystallinity (tysonite structure). The samples, as isolated from synthesis, consist of nanoparticles with a monolayer coverage of citrate capping ligands (with no excess, unbound citrate) and ammonium ions which balance charge. In aqueous dispersions, dipicolinate (DPA) displaces citrate on the surface of 5%Eu-doped LaF 3 nanoparticles and strongly sensitizes Eu 3+ ( 5 D 0 ) emission. At low concentrations, the DPA does not adversely affect the dispersibility or the structural integrity of the nanoparticles and binds to the particle surface, selectively sensitizing Eu 3+ surface and near-surface sites. The DPA, therefore, serves both as a sensitizer of Eu 3+ luminescence and as a selective probe of the surface. The dipicolinate strongly sensitizes Eu 3+ ( 5 D 0 ) emission, increasing the 614 nm emission intensity by a factor of 100 with less than 1% citrate replacement. Addition of an LaF 3 shell (thickness ≈ 4 Å) over the 5%Eu:LaF 3 core (3−4 nm) results in dispersible nanoparticles which exhibit 4-fold reduction in dipicolinate sensitization. The DPA probe gives strong evidence that a true core−shell structure is formed and that DPA sensitization can penetrate to near-surface sites to which the DPA is not directly bound.
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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".