Synthesis and application of a novel magnetic metal-organic framework nanocomposite for determination of Cd, Pb, and Zn in baby food samples
Bibliographic record
Abstract
This work describes the synthesis and application of a novel magnetic metal-organic framework (MOF) [(Fe 3 O 4 -2,5-dimercapto-1,3,4-thiadiazole)/Cu 3 (benzene-1,3,5-tricarboxylate) 2 ] to preconcentrate trace amounts of Cd(II), Pb(II), and Zn(II) ions. A Box–Behnken design was used to find the parameters affecting the preconcentration procedure through response surface methodology. Three variables including sorption time, amount of the magnetic sorbent, and sample pH were selected as affecting factors in the sorption step, and four parameters including type, volume, concentration of the eluent, and elution time were selected in the elution step for the optimization study. These values were 33 mg, 11 min, 5.7 EDTA, 4.3 mL, 0.64 mol L –l EDTA solution, 16 min, for amount of the magnetic sorbent, sorption time, sample pH, type, volume, and concentration of the eluent, and elution time, respectively. Following the sorption and elution steps, the ions were quantified by FAAS. The limits of detection were 0.10, 0.15, and 0.75 ng mL −1 for Cd(II), Zn(II), and Pb(II) ions, respectively. The relative standard deviations (RSD) of the method were less than 8.3% for five separate batch experiments in the determination of 25 μg L −1 of Cd(II), Zn(II), and Pb(II) ions. The sorption capacity of the magnetic MOF nanocomposite was 155 mg g −1 for Cd(II), 173 mg g −1 for Pb(II), and 190 mg g −1 for Zn(II). Finally, the magnetic MOF nanocomposite was successfully applied to rapidly extract trace amounts of heavy metal ions in baby food samples.
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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) | 0.000 | 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 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".