Simultaneous preconcentration and determination of Cu(II), Ni(II), and Co(II) in food and environmental samples by the application of chelate adsorption on Amberlite XAD-1180
Bibliographic record
Abstract
Abstract A simultaneous preconcentration and determination procedure for solid phase extraction on AXAD-1180 as 2,6-dimethlmorpholinedithiocarbamate (DMMDTC) chelates and spectrophotometric determinations of Cu (II), Ni (II), and Co (II) in food and environmental samples is proposed in the present work. The effect of some SPE parameters, such as reagent amount, sample pH, eluent type, concentration, and volume, sample and eluent flow rate, and sample volume, on trace metal ion recovery (R%) for the method developed in the standard model solution medium was investigated. Cu(II), Ni(II), and Co(II) retained as DMMDTC complexes on Amberlite XAD-1180 were eluted with 10 mL of 1 M HNO3 (in acetone). Foreign ions were also studied individually on the recovery of trace metal ions using the developed method. Cu(II), Ni(II), and Co(II) ions were preconcentrated and separated from the sample using the developed SPE method, and their concentrations were simultaneously determined using the UV-VIS spectrophotometric method. The spectrophotometric determination was made by measuring the absorbance of colored chelates of metal ions complexed with DMMDTC in a surfactant medium (1% Triton X-100) at wavelengths of 460, 328, and 342 nm for Cu(II), Ni(II), and Co(II), respectively. To test the method's accuracy, certified reference materials (CRM 1204 waste water and TMDA-70.2 Ontario lake water) were analyzed using the proposed method, and metal recoveries were calculated to be between 97.1% and 100.7%. The proposed method worked well with the wheat flour sample. Wheat flour has Cu(II) and Ni(II) contents of 2,16 µgmL− 1 and 0,56 µgmL− 1, respectively.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".