Green Synthesis of High Quantum Yield Carbon Dots from Phenylalanine and Citric Acid: Role of Stoichiometry and Nitrogen Doping
Why this work is in the frame
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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 Fe3+ 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 it