Integration of Carbon Nanotubes into Manganese Dioxide Nanorods for Enhanced Enzymeless Electrochemical Glucose Sensing with High Sensitivity and Selectivity
Bibliographic record
Abstract
Freestanding electrode designs, cost-effective catalysts, and enhanced electrical conductivity are crucial for improving the performance of fourth-generation non-enzymatic glucose electrochemical sensors. These factors enable more efficient, scalable, and durable sensors with better sensitivity, stability, and affordability for real-time glucose monitoring. In this study, we explore a freestanding electrode design combining carbon nanotubes (CNTs) with MnO2 nanorods to enhance charge transfer, increase surface area, and optimize catalytic activity. This CNTs/MnO2 electrode demonstrates exceptional catalytic activity for glucose oxidation, achieving a high sensitivity of 309.73 µA cm−2 mM−1 within a linear range of 0.5 to 10 mM—well above typical physiological glucose levels (3–8 mM), with a detection limit of 0.19 mM at a signal-to-noise ratio of 3. The electrode also shows excellent durability and remarkable selectivity for glucose over common interferents like ascorbic acid and uric acid, as well as antifouling properties in the presence of KCl. These attributes are essential for accurate glucose detection in complex biological samples. The integration of MnO2 nanorods with CNTs in freestanding nanostructures opens up exciting opportunities for developing high-performance, robust electrochemical sensors for diverse applications.
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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) | 0.001 | 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".