Sensitive Electrochemical Detection of Caffeic Acid in Wine Based on Fluorine-Doped Graphene Oxide
Bibliographic record
Abstract
We report here a novel electrochemical sensor developed using fluorine-doped graphene oxide (F-GO) for the detection of caffeic acid (CA). The synthesized graphene oxide (GO) and F-GO nanomaterials were systematically characterized with a scanning electron microscope (SEM), and the presence of semi-ionic bonds was confirmed in the F-GO using X-ray photoelectron spectroscopy. The electrochemical behaviours of bare glassy carbon electrode (GCE), F-GO/GCE, and GO/GCE toward the oxidation of CA were studied using cyclic voltammetry (CV), and the results obtained from the CV investigation revealed that F-GO/GCE exhibited the highest electrochemically active surface area and electrocatalytic activity in contrast to the other electrodes. Differential pulse voltammetry (DPV) was employed for the analytical quantitation of CA, and the F-GO/GCE produced a stable oxidation signal over the selected CA concentration range (0.5 to 100.0 μM) with a low limit of detection of 0.018 μM. Furthermore, the acquired results from the selectivity studies revealed a strong anti-interference capability of the F-GO/GCE in the presence of other hydroxycinnamic acids and ascorbic acid. Moreover, the F-GO/GCE offered a good sensitivity, long-term stability, and an excellent reproducibility. The practical application of the electrochemical F-GO sensor was verified using various brands of commercially available wine. The developed electrochemical sensor successfully displayed its ability to directly detect CA in wine samples without pretreatment, making it a promising candidate for food and beverage quality control.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".