Effect of Graphene Oxide Sheet Size on the Response of a Label‐free Voltammetric Immunosensor for Cancer Marker VEGF
Bibliographic record
Abstract
Abstract Graphene and graphene oxide (GO) materials have attracted enormous attention in the biosensing field. Here, we report an evaluation of electrochemically reduced graphene oxide (ERGO) of different sheet sizes (0.45–0.7 μm, 0.7–2.5 μm and >300 μm) towards the voltammetric biosensing of vascular endothelial growth factor (VEGF) and the effect of the sheet size on the sensitivity of graphene‐based electrochemical biosensors. The degree of GO reduction and flakes sizes differ from one chemical route to another and ERGO with different sheet sizes have different amounts of edge and basal planes defects derived from oxygen containing functional groups. These could enhance or inhibit the sensitivity of the graphene‐based electrochemical sensors. The extent of reduction of GO to ERGO was found to vary with the sheet size. The decrease of the square wave voltammetry peak current of the [Fe(CN)6]3−/4− couple upon VEGF binding to the immunosensor was employed as the sensor signal. It was found that the ERGO (0.7–2.5 μm) platform has the best performance for the detection of VEGF when compared to the other ERGO materials. The immunosensor showed a wide linear range of 0.1 pg mL−1 to 100 ng mL−1 and LOD of ∼0.1 pg mL−1. The VEGF immunosensor was tested in human serum as a real sample application. The fabricated immunosensor exhibited high selectivity for VEGF against other protein interferences.
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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 | 0.001 | 0.001 |
| 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 | 0.000 |
| 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".