A green one-pot synthesis of nitrogen and sulfur co-doped carbon quantum dots for sensitive and selective detection of cephalexin
Bibliographic record
Abstract
The article reports a simple, economic, and green method for preparing water-soluble, nitrogen and sulfur co-doped carbon quantum dots via a one-step hydrothermal method. Pomegranate juice served as the carbon source, and the L-cysteine provided nitrogen and sulfur. Co-doped carbon dots were characterized by X-ray photoelectron spectroscopy (XPS), transmission electron microscopy (TEM), and Fourier transform infrared (FTIR) spectroscopy techniques. The co-doped carbon dots served as fluorescent probes for sensitive and selective detection of cephalexin. Briefly, the co-doped carbon dot systems showed quenching of photoluminescence intensity in the presence of cephalexin. The decrease of fluorescence intensity made it possible to analyze cephalexin with satisfactory detection limits and linear ranges. The Sterne–Volmer plot showed a linear relationship (R2 = 0.998) between F0/F and the concentration of cephalexin over the range from 0.3 to 10 μmol L−1. The limit of detection (LOD) was estimated to be 1 × 10−7 mol L−1 (at a signal to noise ratio of 3). To validate the applicability, the described method was successfully applied for the detection of cephalexin in human urine and raw milk samples.
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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 | 0.001 | 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".