Magnetic metal organic framework for pre-concentration of ampicillin from cow milk samples
Bibliographic record
Abstract
The aim of this study is a present of a simple solvothermal synthesis approach to preparation of Cu-based magnetic metal organic framework (MMOF) and subsequently its application as sorbent for ultrasound assisted magnetic solid phase extraction (UAMSPE) of ampicillin (AMP) from cow milk samples prior to high performance liquid chromatography-Ultraviolet (HPLC-UV) determination. Characteristics of prepared MMOF were fully investigated by different techniques which showed the exclusive properties of proposed sorbent in terms of proper functionality, desirable magnetic property and also high specific surface area. Different influential factors on extraction recovery including sorbent dosage, ultrasonic time, washing solvent volume and eluent solvent volume were assessed using central composite design (CCD) based response surface methodology (RSM) as an operative and powerful optimization tool. This is the first report for determination of AMP using MMOF. The proposed method addressed some drawbacks of other methods and sorbents for determination of AMP. The presented method decreases the extraction time (4 min) and also enhances adsorption capacity (250 mg/g). Moreover, the magnetic property of presented sorbent (15 emu/g) accelerates the extraction process which does not need filtration, centrifuge and precipitation procedures. Under the optimized conditions, the proposed method is applicable for linear range of 1.0-5000.0 μg/L with detection limit of 0.29 μg/L, satisfactory recoveries (≥95.0%) and acceptable repeatability (RSD less than 4.0%). The present study indicates highly promising perspectives of MMOF for highly effective analysis of AMP in complicated matrices.
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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 | 0.000 |
| 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) | 0.001 | 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".