Optical Properties of Dyes Confined into Carbon and Boron Nitride Nanotubes for Multimodal Bio-Imaging
Bibliographic record
Abstract
Nanotubes attracted a lot of interest as 1D nano-porous materials for the encapsulation and aggregation control of organic dyes molecules. Indeed, the 1D confinement drives the stacking of the molecules inside and enables original aggregation effects on their optical properties (1,2). When encapsulated inside carbon nanotube, organics dyes such as 6T, exhibits for example a strong and specific Raman scattering but their luminescence is efficiently quenched by the nanotube (3). Here we show that the same dyes, when encapsulated inside boron nitride nanotubes (6T@BNNT) exhibits strong luminescence (4). Photoluminescence imaging experiments on individualized 6T@BNNT show that the encapsulation also circumvent the photobleaching issue over days under continuous photo-excitation. Finally we show that these hybrid materials, with specific luminescence and/or Raman fingerprints, can act as robust nanoprobes with reduced toxicity in living system such as Daphnia Pulex, for multimodal imaging from the visible to the near infrared range. (4) (1) E. Gaufrès et al Nature Photon. (2014) (2) S. Cambré et al Nature Nano (2015) (3) E. Gaufrès et al ACS Nano (2016) (4) E. Gaufrès et al (submitted)
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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".