Raman Imaging Study of Alpha-Sexithiophene Encapsulation in Single-Walled Carbon Nanotubes
Bibliographic record
Abstract
Single-Walled Carbon Nanotubes (SWNTs) are nanosystems with a large aspect ratio that have potential in a wide range of optical applications. Notably, the hollowness of SWNTs interior allows endohedral filling, in which the SWNTs act as a template for the creation of new hybrid nanostructures. It was shown recently that dyes encapsulated inside carbon nanotubes (CNTs) are protected from degradation and present a strong Raman signature with narrow emission peaks, free of background fluorescence [1]. Further, confinement inside SWNTs was found to largely impact molecular organisation, tuning the physical and chemical properties of the encapsulated molecules. In this study, we explore the encapsulation mechanism of dye molecules in SWNTs. α-sexithiophene molecules were chosen due to their well-conjugated, rod-like structure and giant Raman signal upon encapsulation, at an excitation of 532 nm. A model system composed of long (>10 um) and aligned SWNTs was used, in which CVD-grown SWNTs are patterned by electron-beam lithography (EBL) and opened by oxygen plasma reactive ion etching (O2 RIE). The encapsulation is carried-out using a liquid-phase protocol, which allows the study of different encapsulation processes, such as dye entryways, aggregation formation and dynamics, as well as the relationship between encapsulation parameters (concentration, temperature, solvent) and yield. Due to their length, these SWNTs are well resolved by Raman imaging (RIMA, Raman Imaging system, Photon etc.) and provide a direct visual of the encapsulation process in this 1D system. [1] Gaufrès, E., et al. Giant Raman scattering from J-aggregated dyes inside carbon nanotubes for multispectral imaging. Nature Photonics 8(1), 72 (2014).
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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".