Reduced Graphene Oxide-Cadmium Sulfide Quantum Dots Nanocomposite Based Dispersive Solid Phase Microextraction for Ultra-Trace Determination of Carbamazepine and Phenobarbital
Bibliographic record
Abstract
This research presents a fast, sensitive, and selective ultrasound-assisted dispersive solid phase microextraction technique for simultaneous preconcentration and determination of ultra-trace amount of carbamazepine and phenobarbital. Reduced graphene oxide sheets decorated with cadmium sulfide quantum dots was synthesized, characterized, and used as a high capacity adsorbent. A high performance liquid chromatography with UV detector (HPLC-UV) instrument with 58:42 composition of methanol:acetic acid/acetate buffer (pH = 5; 0.05 mol L-1) as the mobile phase and set on the wavelength of 230 nm was used to separate and quantify the analytes. In this regard, different parameters affecting adsorption and desorption of the analytes on the surface of the nanocomposite were studied and optimized to maximize the efficiency of the method. The method was linear in the ranges of 0.5-180 and 0.5-140 ng mL−1 (correlation coefficient (r) > 0.999) with limits of detection of 0.19 and 0.24 ng mL−1 for carbamazepine and phenobarbital, respectively. Eventually, to evaluate the efficiency of the proposed method for the determination of pharmaceuticals in biological samples, different real samples including breast milk, urine and human plasma were tested. Obtained recoveries values were within the range of 96.3 ± 2 to 103.7 ± 3.3% which showed satisfactory efficiency.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".