Electrochemical Sensor Using ZnO-MWCNTs Modified Carbon Paste Electrode for Simultaneous Detection of Isoniazid and Levofloxacin
Bibliographic record
Abstract
The ZnO-MWCNTs-CPE, a carbon paste electrode modified with zinc oxide nanoparticles (ZnO) and multi-walled carbon nanotubes (MWCNTs), has proven to be highly effective in the simultaneous electroanalysis of isoniazid (INH) and levofloxacin (LVF). The ZnO nanoparticles were synthesized via a surfactant-assisted sol-gel method and characterized using Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), and scanning electron microscopy (SEM), revealing an average particle diameter of 32.5 nm. The surface morphology of ZnO-MWCNTs-CPE was analyzed by SEM, while its electrochemical performance was evaluated through cyclic voltammetry (CV) and differential pulse voltammetry (DPV). The ZnO-MWCNTs-CPE exhibited a significantly enhanced electrochemical response with well-defined and distinct oxidation peaks compared to the unmodified CPE. Under optimal conditions, the DPV measurements showed a linear working range of 6.25 × 10−8 M to 3.66 × 10−4 M for INH and 8.39 × 10−8 M to 7.41 × 10−4 M for LVF, with detection limits of 2.18 nM and 2.89 nM, respectively. This electrochemical sensor demonstrated high sensitivity and selectivity for simultaneously determining INH and LVF in pharmaceutical formulations, blood, and urine samples.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 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".