Electrochemically Exfoliated Graphene Oxide for Simple Fabrication of Cocaine Aptasensors
Bibliographic record
Abstract
Transducers made from graphene-type materials are widely used in sensing applications. However, utilization of graphene oxide obtained from electrochemical exfoliation of graphite (EGO) has remained relatively unexplored. In this study, electrochemical cocaine aptasensors based on large-size EGO flakes were investigated. In particular, the influence of the following parameters on the sensor performance was examined: (i) aptamer’s terminal group (−NH 2 vs −OH), (ii) functionalization of EGO with the aptamer via physical adsorption and covalent immobilization, and (iii) intrinsic electrochemical properties of EGO such as the electrochemical surface area (ESA) and standard rate constant of electron transfer ( k 0 ). The results demonstrate that EGO-based electrochemical aptasensors fabricated by physical adsorption with an NH 2 -modified aptamer have very good reproducibility, shelf-life stability, and high sensitivity for detecting cocaine with a detection limit of 50 nM. Their performance is comparable to that of the aptasensors prepared using the covalent immobilization. Additionally, it is shown that EGO materials with high ESA and k 0 can enhance the sensing performance. The fast (less than 10 min) and strong adsorption of the NH 2 -modified cocaine aptamer on the surface of large EGO flakes makes the fabrication of the sensing platform simple and rapid. This simple approach has the potential to simplify the fabrication of sensors.
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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 | 0.000 | 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".