Neodymium‐Doped LaF<sub>3</sub> Nanoparticles for Fluorescence Bioimaging in the Second Biological Window
Bibliographic record
Abstract
The future perspective of fluorescence imaging for real in vivo application are based on novel efficient nanoparticles which is able to emit in the second biological window (1000-1400 nm). In this work, the potential application of Nd(3+) -doped LaF(3) (Nd(3+) :LaF(3) ) nanoparticles is reported for fluorescence bioimaging in both the first and second biological windows based on their three main emission channels of Nd(3+) ions: (4) F(3/2) →(4) I(9/2) , (4) F(3/2) →(4) I(11/2) and (4) F(3/2) →(4) I(13/2) that lead to emissions at around 910, 1050, and 1330 nm, respectively. By systematically comparing the relative emission intensities, penetration depths and subtissue optical dispersion of each transition we propose that optimum subtissue images based on Nd(3+) :LaF(3) nanoparticles are obtained by using the (4) F3/2 →(4) I11/2 (1050 nm) emission band (lying in the second biological window) instead of the traditionally used (4) F(3/2) →(4) I(9/2) (910 nm, in the first biological window). After determining the optimum emission channel, it is used to obtain both in vitro and in vivo images by the controlled incorporation of Nd(3+) :LaF(3) nanoparticles in cancer cells and mice. Nd(3+) :LaF(3)nanoparticles thus emerge as very promising fluorescent nanoprobes for bioimaging in the second biological window.
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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 | 0.001 | 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".