The Role of Surface Chemistry in Impedimetric Aptasensing
Bibliographic record
Abstract
Abstract Surface chemistry is a key parameter in the choice of proper materials for electrochemical detection. It has been previously shown that the presence of oxygen containing groups (OCGs) on the surface of graphene oxide (GO) can be both effective and detrimental. This poses a question when GO materials are used as electrochemical platforms for biosensing. In this work, we study how the surface chemistry of graphene oxide nanocolloids (GONCs) affects the impedimetric biosensing of ochratoxin A (OTA), in terms of immobilization of biorecognition element and detection step. OCGs on GONCs were tuned by applying increasing reduction potentials from −0.3 V to −1.2 V, resulting in GONC platforms with decreasing amounts of oxygen functionalities. It was discovered that the sensitivity of biosensing is correlated to the residual amount of OCGs on GO surface. For a more detailed investigation, three representative materials, namely unreduced GONCs, as well as GONCs reduced at potentials of −0.8 V and −1.2 V were chosen. Results were compared in terms of calibration sensitivity, selectivity and reproducibility of the impedimetric response. GONCs reduced at −1.2 V have shown the best electroanalytical response for the impedimetric detection of OTA. These findings are anticipated to contribute to the design of novel biosensors, whereby an optimized platform is employed for the immobilization of the biorecognition element.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".