Solubilization of Multiwall Carbon Nanotubes by 3‐Aminopropyltriethoxysilane Towards the Fabrication of Electrochemical Biosensors with Promoted Electron Transfer
Bibliographic record
Abstract
Abstract A simple procedure has been described for the fabrication of multi‐wall carbon nanotube (CNT) based electrochemical sensors. 3‐Aminopropyltriethoxysilane (APTES) induced solubilization of CNTs allowed for the modification of electrode surfaces. With glucose oxidase (GOx), a flavin (FAD) containing enzyme as a model system, APTES was used as a solubilizing agent for CNTs as well as an immobilization matrix for GOx to construct a mediatorless biosensor. Our biosensor was able to efficiently monitor direct electroactivity of GOx at the electrode surface. A well‐defined glucose response was observed at −0.45 V (vs. Ag/AgCl) whereas relevant physiological levels (0.1 mM) of three common interfering species, uric acid, ascorbic acid, and acetaminophen, resulted in no response. Although CNTs modified by APTES acted as semiconductors to reduce the exposed sensing surface, we reasoned nanoscale “dendrites” of CNTs modified by APTES formed a network and projected outwards from the electrode surface and acted like bundled ultra‐microelectrodes that allowed access to the active FAD site and facilitated direct electron transfer to the immobilized enzyme. The glucose biosensor prepared using a carbon fiber (11 μm) exhibited picoamperometric current response within 5 s with detection limits of 5–10 μM.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".