Designed electrochemical sensor based on metallocene modified conducting polymer composite for effective determination of tramadol in real samples
Bibliographic record
Abstract
A novel composite for the electrochemical sensing of tramadol (Tr) was developed by the inclusion of a metallocene mediator between two layers of conducting poly(3,4-ethylenedioxythiophene) (PEDOT) polymer, in the presence of sodium dodecyl sulfate (SDS), i.e., P/mediator/P⋯SDS. Three charge transfer mediators were evaluated: ferrocene carboxylic acid (FC1), ferrocene (FC2), and cobaltocene (CC) for Tr electrocatalytic oxidation. The FC1 charge mediator showed a relatively higher current response that was assisted by the electronic conduction of the polymer film. Moreover, SDS presented a great impact, resulting in the enhancement of the preconcentration and (or) accumulation of Tr ions at the interface, leading to faster electron transfer. In addition, the practical application of the proposed FC1 composite for the determination of Tr in real urine and serum samples was successfully achieved with adequate recovery results. Very low detection limits of 18.6 nM and 16 nM in the linear dynamic ranges of 7–300 µM and 5–280 µM, respectively, were obtained at the proposed sensor. Furthermore, the simultaneous determination of Tr with common interfering species, paracetamol (APAP), morphine (MO), dopamine (DA), ascorbic acid (AA) and uric acid (UA), proved excellent, with good resolution and large potential peaks separation. The excellent characteristics of the proposed composite such as high reproducibility, good sensitivity, selectivity, anti-interference ability, and good stability enhanced its application for determination of other narcotic drugs.
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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) | 0.001 | 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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".