Non-cytotoxic lanthanum and nitrogen co-doped lignin-based carbon dots for selective detection of ions in biological imaging
Bibliographic record
Abstract
Owing to their versatility, fluorescent carbon quantum dots (CQDs) have attracted significant attention for applications in sensors, bioimaging, microfluidics, photodynamic therapy, drug delivery, light-emitting diode, etc. Herein, nitrogen and lanthanum co-doped multifunctional lignin-based carbon quantum dots ((N, La)-CQDs) were prepared from enzymatic hydrolysis lignin using a simple one-step hydrothermal method . The diameter of the CQDs obtained was about 2.2 nm with a good water solubility at the excitation wavelength of around 365 nm and emission wavelength of around 465 nm. This is the first discovery of (N, La)-CQDs to detect Sn 2+ . (N, La)-CQDs were successfully used to detect Fe 3+ , Sn 2+ , and ClO - ions in the range of 0–100 μM with the detection limits of 0.99 μM, 1.1 μM and 1.1 μM sequentially, and the linear fit R 2 of 0.9997, 0.9943 and 0.9941 respectively. Non-cytotoxic (N, La)-CQDs can be used for labeling cells and detecting Sn 2+ in zebrafish. These attractive features make these non-toxic, environmentally friendly CQDs material highly promising for applications in a wide range of areas, such as biomedicine, biosensing, disease diagnosis, and environmental monitoring.
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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".