Mn, B, N co-doped graphene quantum dots for fluorescence sensing and biological imaging
Bibliographic record
Abstract
The fluorescent and quantum yield (QY) of graphene quantum dots has been improved in recent years by doped atoms, which have good application prospects in fluorescence sensors and biological imaging. Here, a one-step hydrothermal synthesis method was used to synthesize manganese ions bonded with boron and nitrogen-doped graphene quantum dots (Mn-BN-GQDs). Compared with the boron and nitrogen co-doping graphene quantum dots (BN-GQDs), the fluorescence properties and quantum yield of Mn-BN-GQDs are significantly improved. Meanwhile, Mn-BN-GQDs exhibit low toxicity and good fluorescence imaging in living cells and has high selectivity to Fe3+ ions. Therefore, this experiment design Mn-BN-GQDs as a fluorescence sensor to detect Fe3+ ions, providing strong evidence for the advanced high sensitivity, selectivity and wide detection range of the Mn-BN-GQDs as a fluorescence sensor. These results indicate a dual linear relationship with good linear relationships in the 10–100 μM and 100–800 μM ranges, and limit of detection are 0.78 μM and 9.08 μM, respectively. Cellular imaging results demonstrate that Mn-BN-GQDs can be used as fluorescence sensors in biological imaging. Mn-BN-GQDs can be used for fluorescence sensing in biological imaging in combination with low toxicity, QY and quantum dot lifetime.
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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".