Synthesizing luminescent carbon from condensed tobacco smoke: bio-waste for possible bioimaging
Bibliographic record
Abstract
Used cigarette filters, a waste material and a major source of land pollution, were used as raw material to study the nature of condensed tobacco smoke (tar) using microscopy, optical, IR, photoluminescence, and Raman spectroscopy, as well as X-ray diffraction and electron and fluorescence microscopy. The tar present in the cigarette filter bud was used to synthesize luminescent low dimensional carbon using a simple methanol extraction technique. The collected material shows light blue emission under UV excitation with emission peak energy depending strongly on the excitation wavelength. Such excitation energy dependent emission is observed from the extract solution and the dried film. Careful analysis was carried out to understand its origin, which revealed the presence of a giant red-edge effect in the samples. A correlation between room temperature photoluminescence spectroscopy and fluorescence microscopy was carried out. The presence of amorphous phase carbon was established using Raman spectroscopy, and a quantum yield of more than 9% was estimated, which was moderately high in comparison with the one shown by carbon dots prepared by using other sources and can be used for bioimaging applications.
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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".