Highly sensitive determination of copper (II) ions using fluorescence and chemiluminescence emissions of modified CdS quantum dots after it’s preconcentration by dispersive liquid–liquid microextraction
Bibliographic record
Abstract
Two highly sensitive and selective methods based on fluorescence (FL) and chemiluminescence (CL) emissions of 8-mercaptoquinoline-capped CdS quantum dots (MCQ-CdS QDs) were described for the determination of copper (II) after it’s preconcentration. High fluorescent CdS QDs, synthesized in an aqueous medium, generated a relatively intense CL emission in the presence of potassium permanganate as an oxidant. Furthermore, low quantities of copper (II) ions showed a remarkable quenching effect on both of the CL and FL emissions of MCQ-CdS QDs. Based on this effect, two selective and simple methods were established for Cu2+, and the detection limits of 0.28 and 0.026 ng mL−1 were obtained for the FL and CL methods, respectively. Also, due to the high propensity of MCQ to Cu2+, good selectivity was obtained and no sensible interfering effects from other metal ions were observed. To more sensitize the developed method, an efficient preconcentration process was designed based on the high-yield ultrasound-assisted temperature-controlled ionic liquid dispersive liquid–liquid microextraction (UA-TIL-DLLME) method. Under the optimum conditions, the extracted Cu2+ showed a suppressing effect on the FL and CL emissions of CdS QDs proportional to its initial concentration over the ranges of 0.008–1.4 and 0.001–1.4 ng mL−1, respectively. The limits of detection of 3.7 and 0.37 pg mL−1, respectively, were also achieved. The established methods showed great features and were satisfactorily applied to the monitoring of ultratrace Cu2+ in some different environmental samples.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".