Quantum Yield Enhancement of Carbon Quantum Dots Using Chemical-Free Precursors for Sensing Cr (VI) Ions
Bibliographic record
Abstract
Quantum yield illustrates the efficiency that a fluorophore converts the excitation light into fluorescence emission. The quantum yield of carbon quantum dots (CQDs) can be altered via precursors, fabrication conditions, chemical doping, and surface modifications. In this study, CQDs were first fabricated from whole-meal bread using a chemical-free hydrothermal route, and a low quantum yield (0.81%) was obtained. The combination of whole-meal bread, soybean flour, and lemon juice generated CQDs with almost four folds of enhancement in quantum yield. Detailed characterization suggested that these CQDs were subjected to more complete hydrothermal reactions and had zwitterionic surfaces. The CQDs could selectively detect Cr (VI) ions with a limit of detection (LOD) of 8 ppm. This study shows that the enhancement of the quantum yield of CQDs does not need chemicals, and it is achievable with food precursors.
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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".