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 of graphene and quantum dots such as strong fluorescence, chemical stability and facile synthesis.In this work, GQDs were prepared by pyrolysis of citric acid.Compared with the conventional pyrolysis method, optimization of the pyrolysis temperature, pyrolysis time, and dispersion pH attained 1.04 times stronger fluorescence emission intensity compared with the previous GQDs.The appearance of metal oxide nanoparticles in potable water has attracted much public attention concerning their potential adverse health risks.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, which broke up all coagulated nanoparticles and prevented any re-aggregation to merit an accurate analysis.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 indicated static, dynamic and absorptive contributions to the total quenching.Detection of TiO2 nanoparticles down to 0.02 mg/mL was validated, under optimal DA and GQDs concentrations, with a linear dynamic range up to 2.0 mg/mL.
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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".