Probing the influence of graphene oxide sheets size on the performance of label-free electrochemical biosensors
Bibliographic record
Abstract
The integration of graphene materials into electrochemical biosensing platforms has gained significant interest in recent years. Bulk quantities of graphene can be synthesized by oxidation of graphite to graphite oxide and subsequent exfoliation to graphene oxide (GO). However, the size of the resultant GO sheets changes from the parent graphite yielding a polydispersed solution of sizes ranging from a few nanometers to tens of micrometers. Here, we investigate the direct effect of GO sheets sizes on biosensor performance. We separated different GO sheets sizes, and we characterized them via atomic force, scanning electron, Raman and X-ray photoelectron spectroscopies and solid state nuclear magnetic resonance (NMR). As proof of concept, the sensing performance of these GO samples was probed using a well-known ssDNA aptasensor against microcystin-LR toxin and an immunosensor against β-lactoglobulin. The resulting aptasensors and immunosensors are fabricated by using covalent attachment and physical adsorption. We found that the aptasensors fabricated using physical adsorption, the binding signal variation was dramatically increased with increasing the GO sheet size. In contrast, for the aptasensor fabricated using covalent immobilization, the binding signal variation decreased with increasing GO sheet size. However, for the β-lactoglobulin immunosensors, the optimum signals were observed at intermediate GO sheet size. GO sheet size could enhance or inhibit the sensitivity of the graphene-based electrochemical sensors. Our results demonstrate that controlling the size of GO sheets may have a profound impact in specific biosensing 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 | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".