Urea–choline chloride deep eutectic solvent-assisted synthesis of luminescent nitrogen-doped carbon dots from chitin and their photocatalytic application in decolourizing malachite green
Bibliographic record
Abstract
Luminescent carbon dots (CDs) are emerging carbon nanomaterials whose tunable and exceptional optoelectronic properties have found applications as alternatives to traditional fluorophores and metal-based catalysts. Further, they can be easily prepared from biomass and using green solvents. Biomass sources of CDs, however, often result in the formation of thousands of products, which are difficult to separate. In this study, CDs were synthesized using a urea–choline chloride deep eutectic solvent and shrimp shell-derived chitin as raw materials through a low-temperature solvothermal treatment. This was followed by a facile liquid–liquid extraction with acetone to improve the optical properties and narrow the size distribution of the CDs. The acetone-extracted CDs (ACDs) showed good performance in photocatalytic degradation of the aquatic pollutant, malachite green. They also show good potential in monitoring various toxic water pollutants through the quenching effect of selected pesticides, antibiotics, heavy metals, and anions on the fluorescence of ACDs.
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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".