Silica‐Coated Ln<sup>3+</sup>‐Doped LaF<sub>3</sub> Nanoparticles as Robust Down‐ and Upconverting Biolabels
Bibliographic record
Abstract
The preparation of nearly monodisperse (40 nm), silica-coated LaF(3):Ln(3+) nanoparticles and their bioconjugation to FITC-avidin (FITC=fluorescein isothiocyanate) is described in this report. Doping of the LaF(3) core with selected luminescent Ln(3+) ions allows the particles to display a range of emission lines from the visible to the near-infrared region (lambda=450-1650 nm). First, the use of Tb(3+) and Eu(3+) ions resulted in green (lambda=541 nm) and red (lambda=591 and 612 nm) emissions, respectively, by energy downconversion processes. Second, the use of Nd(3+) gave emission lines at lambda=870, 1070 and 1350 nm and Er(3+) gave an emission line at lambda=1540 nm by energy downconversion processes. Additionally, the Er(3+) ions gave green and red emissions and Tm(3+) ions gave an emission at lambda=800 nm by upconversion processes when codoped with Yb(3+) (lambda(ex)=980 nm). Bioconjugation of avidin, which has a bound fluorophore (FITC) as the reporter, was carried out by means of surface modification of the silica particles with 3-aminopropyltrimethoxysilane, followed by reaction with the biotin-N-hydroxysuccinimide activated ester to form an amide bond, imparting biological activity to the particles. A 25-fold or better increase in the FITC signal relative to the non-biotinylated silica particles indicated that there is minimal nonspecific binding of FITC-avidin to the silica particles.
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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) | 0.000 | 0.001 |
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".