Nucleoside-based fluorescent carbon dots for discrimination of metal ions
Why this work is in the frame
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Bibliographic record
Abstract
Carbon dots (Cdots) play an important role in many biological and chemical applications. To prepare strongly fluorescent Cdots, the starting material should contain nitrogen in addition to carbon. Nucleobases are nitrogen rich with interesting metal binding properties. In this work, we prepared a series of Cdots with citrate as the carbon source, and ethylenediamine, adenosine, cytidine, thymidine or guanosine as the respective nitrogen sources. The resulting Cdots were all fluorescent with the ethylenediamine sample being the most strongly emissive. These Cdots were then tested for their metal sensitivity and all tested metal ions can quench their fluorescence. The fluorescence of the G-Cdots prepared with guanosine was quenched most efficiently by Cu2+, while the Cdots prepared with ethylenediamine were more sensitive to Hg2+. With the differential quenching by different metal ions, we prepared a sensor array to discriminate multiple metal ions, and quantified Cu2+ and Hg2+ at the same time. Our work has expanded the range of starting materials for preparing Cdots and showed that by tuning the precursor composition, Cdots with different optical and metal binding properties can be obtained, which is useful in constructing a sensing platform for a large number of metal ions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it