Near‐UV activated, photostable nanophosphors for in vitro dosimetry and dynamic bioimaging
Bibliographic record
Abstract
Luminescent rare earth nanoparticles exhibit superior optical stability over commonly‐used organic dyes and higher biocompatibility over quantum dots, rendering them advantageous as bioimaging nanoprobes. However, their typical excitation inhibits their broad employment with conventional fluorescence microscopes and, thus, solutions are sought to shift their activation in the long‐wavelength (near‐UV) spectral region. Here, we synthesize YVO4:Eu3+ nanophosphors by flame aerosol technology to systematically study the effect of Bi3+ codoping on their luminescence. That way, we identify an optimal Bi‐content for sufficient near‐UV activation. These nanophosphors are highly crystalline and appeared bright red under a conventional fluorescence microscope, facilitating bioimaging with HeLa cells and in vitro dosimetry correlations in the presence and absence of serum. The nanophosphor superiority over organic‐dye‐labeled silica nanoparticles is shown during dynamic imaging for 4 h without photobleaching for the former. These YVO4:Eu3+/Bi3+ nanophosphors can provide a non‐photobleaching tool for further dynamic nanoparticle‐cell interaction studies with conventional fluorescence microscopes. © 2018 American Institute of Chemical Engineers AIChE J, 64: 2947–2957, 2018
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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".