Graphene oxide for electrochemical sensing applications
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| 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 it