Microfibrous Carbon Paper Decorated with High-Density Manganese Dioxide Nanorods: An Electrochemical Nonenzymatic Platform of Glucose Sensing
Bibliographic record
Abstract
Nanorod structures exhibit a high surface-to-volume ratio, enhancing the accessibility of electrolyte ions to the electrode surface and providing an abundance of active sites for improved electrochemical sensing performance. In this study, tetragonal α-MnO2 with a large K+-embedded tunnel structure, directly grown on microfibrous carbon paper to form densely packed nanorod arrays, is investigated as an electrocatalytic material for non-enzymatic glucose sensing. The MnO2 nanorods electrode demonstrates outstanding catalytic activity for glucose oxidation, showcasing a high sensitivity of 143.82 µA cm−2 mM−1 within the linear range from 0.01 to 15 mM, with a limit of detection (LOD) of 0.282 mM specifically for glucose molecules. Importantly, the MnO2 nanorods electrode exhibits excellent selectivity towards glucose over ascorbic acid and uric acid, which is crucial for accurate glucose detection in complex samples. For comparison, a gold electrode shows a lower sensitivity of 52.48 µA cm−2 mM−1 within a linear range from 1 to 10 mM. These findings underscore the superior performance of the MnO2 nanorods electrode in both sensitivity and selectivity, offering significant potential for advancing electrochemical sensors and bioanalytical techniques for glucose monitoring in physiological and clinical settings.
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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) | 0.000 | 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 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".