A surface ion imprinted magnetic silica sorbent for the separation and determination of leaching silver in antibacterial food contact products
Bibliographic record
Abstract
The objective of the present study was to develop a novel method for determining trace silver in leaching solution of antibacterial food contact products. For this purpose, a novel silver ion imprinted magnetic silica sorbent was synthesized by a surface imprinting technique. A new method, combining magnetic solid-phase extraction with flame atomic absorption spectroscopy, was developed, and the synthesized sorbent was used for detecting and extracting trace silver using the newly developed method. The main factors, such as pH, elution condition, ultrasonic time, and coexisting ions, affecting the magnetic solid-phase extraction procedure were investigated. The adsorption capacity of the new sorbent was 91.4 mg g−1 for silver. Under the optimized conditions, our method showed a detection limit of 0.26 ng mL−1 for silver with an enrichment factor of 91.8. The analytical results obtained from a certified reference water sample (GBW00809) were in good agreement with the certified value. Our method, when applied to determine trace silver present in the leaching solutions of different antibacterial food contact products, showed satisfactory results.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".