Synthesis and Luminescent Properties of Graphene Quantum Dots (GQDs) for Fluorescence Quenching by Titanium Dioxide Nanoparticles in Water Analysis
Bibliographic record
Abstract
Graphene quantum dots (GQDs) have many excellent properties such as strong fluorescence, chemical stability and facile synthesis. In this work, GQDs were prepared by pyrolysis of citric acid. And optimization of the pyrolysis temperature, pyrolysis time, and dispersion pH attained 1.04 times stronger fluorescence intensity.The appearance of metal oxide nanoparticles in potable water has attracted much public attention. A simple method for quantitative analysis of titanium dioxide (TiO2) nanoparticles was developed by fluorescence quenching of GQDs in this research. Dopamine (DA) was first added to coat TiO2 nanoparticles with polydopamine (PDA) under ultrasonication. GQDs were next added as a fluorescent sensor probe to measure the quenching of its emission intensity by the PDA-coated nanoparticles. Data analysis by the Stern-Volmer equation followed a third-order polynomial fit that indicated static, dynamic and absorptive contributions to the total quenching. Detection of TiO2 nanoparticles down to 0.02 mg/mL was validated.
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How this classification was reachedexpand
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) | 0.001 | 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 teacher head, 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".