Nucleoside-based fluorescent carbon dots for discrimination of metal ions
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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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".