Microwave-Assisted Synthesis of N, S Co-Doped Carbon Quantum Dots for Fluorescent Sensing of Fe(III) and Hydroquinone in Water and Cell Imaging
Bibliographic record
Abstract
The detection of heavy metal ions and organic pollutants from water sources remains critical challenges due to their detrimental effects on human health and the environment. Herein, a nitrogen and sulfur co-doped carbon quantum dot (NS-CQDs) fluorescent sensor was developed using a microwave-assisted carbonization method for the detection of Fe3+ ions and hydroquinone (HQ) in aqueous solutions. NS-CQDs exhibit excellent optical properties, enabling sensitive detection of Fe3+ and HQ, with detection limits as low as 3.40 and 0.96 μM. Notably, with the alternating introduction of Fe3+ and HQ, NS-CQDs exhibit significant fluorescence (FL) quenching and recovery properties. Based on this property, a reliable “on-off-on” detection mechanism was established, enabling continuous and reversible detection of Fe3+ and HQ. Furthermore, the low cytotoxicity of NS-CQDs was confirmed through successful imaging of HeLa cells, indicating their potential for real-time intracellular detection of Fe3+ and HQ. This work not only provides a green and rapid synthesis strategy for CQDs but also highlights their versatility as fluorescent probes for environmental monitoring and 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".