Green Synthesis of High Quantum Yield Carbon Dots from Phenylalanine and Citric Acid: Role of Stoichiometry and Nitrogen Doping
Bibliographic record
Abstract
Despite a growing interest in carbon dots (CDs), notably for their potential as a more sustainable, less toxic alternative to inorganic quantum dots, the critical factors affecting their physical, chemical, and optical properties are relatively unknown, limiting their widespread use. Herein, a one-pot hydrothermal method was used to synthesize CDs from citric acid and phenylalanine. CDs were synthesized over a range of reactant ratios, from pure citric acid to pure phenylalanine and seven mixed ratios in between, achieving a quantum yield (QY) as high as 65% with a peak excitation/emission of 350/413 nm. The goal was to determine the role of stoichiometry on the chemical and structural composition of CDs, particularly its impact on nitrogen doping, and in turn its effect on QY. We showed that a wide range of reactant ratios tend toward reacting in a stoichiometric 2:1 molar ratio of phenylalanine to citric acid whereby the resulting CDs have similar chemical composition and QY. Using this ratio may lead to a more efficient and sustainable mass production process by reducing and reusing reactant waste. The QY of the CDs was found to be more dependent on the oxygen-to-carbon ratio and the relative amount of carboxyl oxygen in the CD than it was on the nitrogen-to-carbon ratio. The resulting CDs also showed Fe 3+ sensing capabilities through static fluorescence quenching with a limit of detection of 3.5 μM. This study provides new insights which may be useful for the optimization of the green synthesis of CDs for more widespread applications.
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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) | 0.000 | 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".