(Invited) Kinetics and Thermodynamics of Swcnts and Bnnts Encapsulation with α-Sexithiophene in Liquid Phase
Bibliographic record
Abstract
Single-Walled Carbon Nanotubes (SWNTs) and Boron Nitride Nanotubes (BNNTs) come with hollow 1D centers, which can serve to define narrow spaces for encapsulating small organic dyes. The encapsulation process is of scientific interest because it can be used to tailor the optical properties of the resulting dyes@NTs nanohybrids. Past works have shown that the dyes@SWCNT exhibits a strongly enhanced Raman scattering cross section, while the dyes@BNNTs do emit robust, generally red-shifted, luminescence at wavelengths down to the near-IR. In both cases, the nanotube protects the encapsulated dyes from photobleaching, provides high confinement, and reinforces intermolecular interactions between dyes into specific aggregation states. Here, we compare the liquid phase encapsulation process of α-sexithiophene (6T), which is a conjugated rod-like dye, inside SWCNTs and BNNTs. Raman and luminescence imaging experiments are used to monitor the 6T encapsulation process of a large ensemble of individual nanotubes in liquid-phase. This method probes statistically the encapsulation kinetics using hundreds of individual nanotubes according to various parameters (dye concentration and temperature). The results highlight a kinetic model in which single and double aggregates is formed sequentially. This kinetics and the associated thermodynamic parameters for the 6T encapsulation will be presented and discussed so as to gain a better control over the emission properties of the nanohybrids.
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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 | 0.001 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".