Confinement of Dyes inside Boron Nitride Nanotubes: Photostable and Shifted Fluorescence down to the Near Infrared
Bibliographic record
Abstract
Abstract Fluorescence is ubiquitous in life science and used in many fields of research ranging from ecology to medicine. Among the most common fluorogenic compounds, dyes are being exploited in bioimaging for their outstanding optical properties from UV down to the near IR (NIR). However, dye molecules are often toxic to living organisms and photodegradable, which limits the time window for in vivo experiments. Here, it is demonstrated that organic dye molecules are passivated and photostable when they are encapsulated inside a boron nitride nanotube (dyes@BNNT). The results show that the BNNTs drive an aggregation of the encapsulated dyes, which induces a redshifted fluorescence from visible to NIR‐II. The fluorescence remains strong and stable, exempt of bleaching and blinking, over a time scale longer than that of free dyes by more than 104. This passivation also reduces the toxicity of the dyes and induces exceptional chemical robustness, even in harsh conditions. These properties are highlighted in bioimaging where the dyes@BNNT nanohybrids are used as fluorescent nanoprobes for in vivo monitoring of Daphnia Pulex microorganisms and for diffusion tracking on human hepatoblastoma cells with two‐photon imaging.
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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 | 0.000 |
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".