Graphene oxide for electrochemical sensing applications
Bibliographic record
Abstract
By exploiting the presence of abundant carboxylic groups (–COOH) on graphene oxide (GO) and using EDC–NHS (1-ethyl-3-(3-dimethylaminopropyl) carbodiimide hydrochloride–N-hydroxysuccinimide) chemistry to covalently conjugate protein molecules, we demonstrate a novel electrochemical immunosensor for detection of antibody–antigen (Rabbit IgG–AntiRabbit IgG) interactions. The interactions were verified using Electrochemical Impedance Spectroscopy (EIS). Although GO is known to be a poor conductor, the charge transfer resistance (RP) of a GO modified glassy carbon electrode (GCE) was found to be as low as 1.26 Ω cm2. This value is similar to that obtained for reduced graphene oxide (RGO) or graphene and an order of magnitude less than bare GCE. The EIS monitored antibody–antigen interactions showed a linear increase in RP and the overall impedance of the system with increase of antibody concentration. Rabbit IgG antibodies were detected over a wide range of concentrations from 3.3 nM to 683 nM with the limit of detection (LOD) estimated to be 0.67 nM. The sensor showed high selectivity towards Rabbit IgG antibody as compared to non-complementary myoglobin. RGO modified GCE showed no sensing properties due to the removal of carboxylic groups which prevented subsequent chemical functionalization and immobilization of antigen molecules. The sensitivity and selectivity achievable by this simple label free technique hint at the possibility of GO becoming the electrode material of choice for future electrochemical sensing protocols.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".