Carbon nanotube based electrochemical sensor for the sensitive detection of valacyclovir
Bibliographic record
Abstract
An electrochemical sensor for the sensitive detection of valacyclovir has been developed, which is based on single-walled carbon nanotube (SWCNT)-modified glassy carbon electrodes. The electrochemical oxidation of valacyclovir at the SWCNT-modified glassy carbon electrodes has been investigated using cyclic voltammetry and differential pulse voltammetry. Our experimental results show that the SWCNT-modified glassy carbon electrode possesses high activity toward the electrochemical oxidation of valacyclovir. In a 0.1 M phosphate buffer (pH = 7.4), valacyclovir exhibited an irreversible oxidation peak at -0.91 V. The effects of pH of and the amount of SWCNT deposited on the glassy carbon electrode on the activity of the sensor have also been studied. Under optimized conditions, the sensor demonstrates a linear response range from 5 x 10(-9) to 5.5 x 10(-8) M valacyclovir. The detection and quantification limits were found to be 1.80 x 10(-9) M and 6.02 x 10(-9) M, respectively. The selectivity, stability and reproducibility of the proposed sensor were examined as well. To validate its real world application, the electrochemical sensor has been successfully utilized in the detection of valacyclovir in human blood plasma and pharmaceutical samples. Thus, the electrochemical sensor developed in this study has strong potential to be employed in the quality control testing of pharmaceutical products and also for therapeutic drug monitoring in hospitals.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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".