Natural Nitrogen-Doped Carbon Dots Obtained from Hydrothermal Carbonization of Chebulic Myrobalan and Their Sensing Ability toward Heavy Metal Ions
Bibliographic record
Abstract
Chebulic Myrobalan is the main ingredient in the Ayurvedic formulation Triphala, which is used for kidney and liver dysfunctions. Herein, natural nitrogen-doped carbon dots (NN-CDs) were prepared from the hydrothermal carbonization of Chebulic Myrobalan and were demonstrated to sense heavy metal ions in an aqueous medium. Briefly, the NN-CDs were developed from Chebulic Myrobalan by a single-step hydrothermal carbonization approach under a mild temperature (200 °C) without any capping and passivation agents. They were then thoroughly characterized to confirm their structural and optical properties. The resulting NN-CDs had small particles (average diameter: 2.5 ± 0.5 nm) with a narrow size distribution (1–4 nm) and a relatable degree of graphitization. They possessed bright and durable fluorescence with excitation-dependent emission behaviors. Further, the as-synthesized NN-CDs were a good fluorometric sensor for the detection of heavy metal ions in an aqueous medium. The NN-CDs showed sensitive and selective sensing platforms for Fe3+ ions; the detection limit was calculated to be 0.86 μM in the dynamic range of 5–25 μM of the ferric (Fe3+) ion concentration. Moreover, these NN-CDs could expand their application as a potential candidate for biomedical applications and offer a new method of hydrothermally carbonizing waste biomass.
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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".