Carbon Electrodes Coated with TiO <sub>2</sub> –Cu-MOF Composites for Nonenzymatic Detection of Ascorbic Acid
Bibliographic record
Abstract
Noninvasive and real-time monitoring of antioxidants such as ascorbic acid is essential in clinical diagnostics and food quality control. In this study, 3D-structured titanium dioxide (TiO 2 )-integrated copper-based metal–organic frameworks (Cu-MOF) composite was coated onto carbon paper (CP) via the doctor blade method and employed as the sensing electrode in an extended gate field effect transistor (EGFET) configuration for ascorbic acid detection. The morphological analysis was carried out by high-resolution scanning electron microscope (HR-SEM) and high-resolution transmission electron microscope (HR-TEM), revealing that the 3D-structured, densely packed TiO 2 nanorods (∼16 nm in diameter) were integrated with octahedral Cu-MOF microstructures, further confirming the successful integration. The effective surface area of TiO 2 –Cu-MOF/CP was estimated to be 48 cm 2 . The sensing electrode (TiO 2 –Cu-MOF/CP) achieved an limit of detection (LOD) of 12 nM and a wide detection range from 15 nM to 14.38 mM for ascorbic acid detection, with a sensitivity of 193.44 μA μM –1 cm –2 . Additionally, the sensing electrode demonstrated good selectivity to interference from other mixed interfering molecules such as uric acid, dopamine, and glucose. In addition to the EGFET studies, Scanning Kelvin probe (SKP) measurements were performed to investigate the influence of ascorbic acid adsorption on the surface potential of the working electrode. The synergistic role of hierarchically structured TiO 2 –Cu-MOF nanostructures enables an effective detection of ascorbic acid, which was further validated in real sample analysis using commercial pulpy orange juice.
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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 | 0.000 |
| 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".