A graphene-based electrochemical competitive immunosensor for the sensitive detection of okadaic acid in shellfish
Bibliographic record
Abstract
A novel graphene-based voltammetric immunosensor for sensitive detection of okadaic acid (OA) was developed. A simple and efficient electrografting method was utilized to functionalize graphene-modified screen-printed carbon electrodes (GSPE) by the electrochemical reduction of in situ generated 4-carboxyphenyl diazonium salt in acidic aqueous solution. Next, the okadaic acid antibody was covalently immobilized on the carboxyphenyl modified graphene electrodes via carbodiimide chemistry. Square wave voltammetry (SWV) was used to investigate the stepwise assembly of the immunosensor. A competitive assay between OA and a fixed concentration of okadaic acid-ovalbumin conjugate (OA-OVA) for the immobilized antibodies was employed for the detection of okadaic acid. The decrease of the [Fe(CN)(6)](3-/4-) reduction peak current in the square wave voltammetry for various concentrations of okadaic acid was used for establishing the calibration curve. A linear relationship between the SWV peak current difference and OA concentration was obtained up to ~5000 ng L(-1). The developed immunosensor allowed a detection limit of 19 ng L(-1) of OA in PBS buffer. The matrix effect studied with spiked shellfish tissue extracts showed a good percentage of recovery and the method was also validated with certified reference mussel samples.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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 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".