Elucidating the Quenching Mechanism in Carbon Dot-Metal Interactions–Designing Sensitive and Selective Optical Probes
Bibliographic record
Abstract
Overexposure to metals has significant adverse effects on human and animal health coupled with nefarious consequences to the environment. Sensitive tools to measure low contaminant levels exist, but often come at a high cost and require tedious procedures. Thus, there exists a need for the development of affordable metal sensors that can offer high sensitivity and selectivity while being accessible on a global scale. Here, carbon dots, prepared in a one-pot synthesis using glutathione and formamide, have been developed as dual fluorescent metal sensing probes. Following extensive characterization of their physico-chemical properties, it is demonstrated that dual fluorescence can be exploited to build a robust ratiometric sensor with low-ppb detection sensitivity in water. This investigation shows that these optical probes are selective for Pb2+ and Hg2+ ions. Using steady-state and dynamic optical characterization techniques, coupled with hard and soft acid-base theory, the underlying reason for this selective behavior was identified. These findings shed light on the nature of metal-carbon dot interactions, which can be used to tailor their properties to target specific metal ions. Finally, these findings can be applicable to other fluorescent nanoparticle systems that are targeted for development as metal sensors.
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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".