Graphene-Oxide-Based Electrochemical Sensors for the Sensitive Detection of Pharmaceutical Drug Naproxen
Bibliographic record
Abstract
Here we report on a selective and sensitive graphene-oxide-based electrochemical sensor for the detection of naproxen. The effects of doping and oxygen content of various graphene oxide (GO)-based nanomaterials on their respective electrochemical behaviors were investigated and rationalized. The synthesized GO and GO-based nanomaterials were characterized using a field-emission scanning electron microscope, while the associated amounts of the dopant heteroatoms and oxygen were quantified using x-ray photoelectron spectroscopy. The electrochemical behaviors of the GO, fluorine-doped graphene oxide (F-GO), boron-doped partially reduced graphene oxide (B-rGO), nitrogen-doped partially reduced graphene oxide (N-rGO), and thermally reduced graphene oxide (TrGO) were studied and compared via cyclic voltammetry (CV) and differential pulse voltammetry (DPV). It was found that GO exhibited the highest signal for the electrochemical detection of naproxen when compared with the other GO-based nanomaterials explored in the present study. This was primarily due to the presence of the additional oxygen content in the GO, which facilitated the catalytic oxidation of naproxen. The GO-based electrochemical sensor exhibited a wide linear range (10 µM–1 mM), a high sensitivity (0.60 µAµM−1cm−2), high selectivity and a strong anti-interference capacity over potential interfering species that may exist in a biological system for the detection of naproxen. In addition, the proposed GO-based electrochemical sensor was tested using actual pharmaceutical naproxen tablets without pretreatments, further demonstrating excellent sensitivity and selectivity. Moreover, this study provided insights into the participatory catalytic roles of the oxygen functional groups of the GO-based nanomaterials toward the electrochemical oxidation and sensing of naproxen.
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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 | 0.001 | 0.000 |
| 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".