Functionalized CVD monolayer graphene for label-free impedimetric biosensing
Bibliographic record
Abstract
Recent advances in large area graphene growth have led to many applications in different areas. In the present study, chemical vapor deposited (CVD) monolayer graphene supported on glass substrate was examined as electrode material for electrochemical biosensing applications. We report a facile strategy for covalent functionalization of CVD monolayer graphene by electrochemical reduction of carboxyphenyl diazonium salt prepared in situ in acidic aqueous solution. The carboxyphenyl-modified graphene is characterized using Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), and atomic force microscopy (AFM), as well as electrochemical impedance spectroscopy (EIS). We also show that the number of grafted carboxyphenyl groups on the graphene surface can be controlled by the number of cyclic voltammetry (CV) scans used for electrografting. We further present the fabrication and characterization of an immunosensor based on immobilization of ovalbumin antibody on the graphene surface after the activation of the grafted carboxylic groups via EDC/NHS chemistry. The binding between the surface-immobilized antibodies and ovalbumin was then monitored using Faradaic EIS in [Fe(CN)6]3−/4− solution. The percentage change of charge transfer resistance (R ct) after binding exhibited a linear dependence for ovalbumin concentrations ranging from 1.0 pg·mL−1 to 100 ng·mL−1, with a detection limit of 0.9 pg·mL−1. Our results indicate good sensitivity of the developed functionalized CVD graphene platform, paving the way for using CVD monolayer graphene in a variety of electrochemical biosensing devices.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".