Copper-chitosan modified with Graphene oxide adsorbent for dispersive micro solid phase extraction of traces nickel from water and food samples
Bibliographic record
Abstract
The current study presents a new insight into separation of Ni (II) by preconcentration method from water and food samples prior to atomic flame absorption spectrometry analysis. A Cu-chitosan modified Graphene oxide adsorbent was used for micro-solid phase extraction of Ni (II). The synthesized nanocomposite was subjected to characterization by FTIR (Fourier transform infrared spectroscopy), XRD (X-ray diffraction), FESEM (Field emission scanning electron microscope), SEM-EDX (Scanning electron microscopy-energy dispersive X-ray analysis), TGA (thermogravimetric analysis) and BET (Brunauer-Emmett-Teller) analysis to confirm the qualitative characteristics of the material prior to extraction process. Crucial analytical parameters such as pH, adsorbent dosage, adsorption studies and sample volume were investigated and optimized. The quantitative recovery and Relative standard deviation below 5% were obtained. The Limit of detection (LOD) and Limit of quantification (LOQ) were 0.072 mg kg -1 and 0.239 mg kg -1 respectively. To determine the greenness of the process two analytical parameters were tested, namely, analytical GREENness and AGREEprep, resulting in 0.73 and 0.70 respective grades. The proposed method presents a moderate eco-friendly and facile process. The preconcentration factor obtained for Ni (II) was 40. To validate the effectiveness of this work, the dµ-SPE method was applied to two different standard reference materials, BCR-701 lake sediment (Belgium) and TMDA-54-6 (Canada) certified water reference material.
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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".