Fluorescence anisotropy using highly polarized emitting dyes confined inside BNNTs
Bibliographic record
Abstract
Polarized fluorescence emission of nanoscale emitters has been extensively studied for applications such as bioimaging, displays, and optical communication. Extending the polarization properties in large assemblies of compact emitters is, however, challenging because of self-aggregation processes, which can induce depolarization effects, quenching, and cancellations of molecular dipoles. Here we use α-sexithiophene (6T) molecules confined inside boron nitride nanotubes (6T@BNNTs) to induce fluorescence anisotropy in a transparent host. The experiments first indicate that individual 6T@BNNTs exhibit a high polarization extinction ratio, up to 700, at room temperature. Using aberration-corrected HRTEM, we show that the fluorescence anisotropy is consistent with a general alignment of encapsulated 6T molecules along the nanotube axis. The molecular alignment is weakly influenced by the nanotube diameter, a phenomenon ascribed to stronger molecule-to-sidewall interactions compared to intermolecular interactions. By stretching a flexible thin film made of transparent polymers mixed with 6T@BNNTs, we induce a macroscopic fluorescence anisotropy within the film. This work demonstrates that the dyes@BNNT system can be used as an easy-to-handle platform to induce fluorescence anisotropy in photonic materials.
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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".