Electrochemical determination of Penicillin G in biological matrix based on the carbon paste electrode modified with a molecular imprinted polymer
Bibliographic record
Abstract
• Design of an electrochemical sensor based on MIP for PNCG in biological matrices. • The developed MIP for PNCG works selectively with an acceptable LOD in blood plasma. • The analytical method has been validated based on the bioanalytical validation guidance. • The developed MIP-CPE sensor is highly reliable, cost-effective, durable, and rapid. • The sensor can replace chromatographic methods for testing pharmaceutical compounds. An accurate and sensitive electrochemical sensor was developed based on a carbon paste electrode modified with a molecularly imprinted polymer (CPE-MIP) for the determination of Penicillin G (PNCG). A suitable MIP for PNCG was synthesized and characterized using spectroscopic and electrochemical methods. The electrochemical behavior of PNCG was investigated, and the composition, extraction efficiency, and electrochemical parameters of the electrode were thoroughly assessed. The developed CPE-MIP sensor was tested in human blood plasma, demonstrating reproducibility, repeatability, stability, selectivity, minimal matrix effects, and satisfactory recovery. Two dynamic linear ranges were observed: 5.0 × 10 −8 –1.0 × 10 −6 M and 1.0 × 10 −6 –1.0 × 10 −4 M, with a limit of detection (LOD) of 3.8 × 10 −8 M. The results demonstrate that the developed CPE-MIP sensor is highly reliable, cost-effective, rapid, environmentally friendly, and selective for PNCG determination.
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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 | 0.001 |
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".