Green Synthesis of Fluorescent Carbon Dots for Selective Detection of Tartrazine in Food Samples
Bibliographic record
Abstract
A simple, economical, and green method for the preparation of water-soluble, high-fluorescent carbon quantum dots (C-dots) has been developed via hydrothermal process using aloe as a carbon source. The synthesized C-dots were characterized by atomic force microscope (AFM), transmission electron microscopy (TEM), fluorescence spectrophotometer, UV-vis absorption spectra as well as Fourier transform infrared spectroscopy (FTIR). The results reveal that the as-prepared C-dots were spherical shape with an average diameter of 5 nm and emit bright yellow photoluminescence (PL) with a quantum yield of approximately 10.37%. The surface of the C-dots was rich in hydroxyl groups and presented various merits including high fluorescent quantum yield, excellent photostability, low toxicity and satisfactory solubility. Additionally, we found that one of the widely used synthetic food colorants, tartrazine, could result in a strong fluorescence quenching of the C-dots through a static quenching process. The decrease of fluorescence intensity made it possible to determine tartrazine in the linear range extending from 0.25 to 32.50 μM, This observation was further successfully applied for the determination of tartrazine in food samples collected from local markets, suggesting its great potential toward food routine analysis. Results from our study may shed light on the production of fluorescent and biocompatible nanocarbons due to our simple and environmental benign strategy to synthesize C-dots in which aloe was used as a carbon source.
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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".