Label-Free Voltammetric Aptasensor for the Sensitive Detection of Microcystin-LR Using Graphene-Modified Electrodes
Bibliographic record
Abstract
The development of successful biosensing platforms is highly dependent upon the biorecognition properties of the recognition receptor and the sensitivity of the transducer of the binding signal. The integration of the high affinity and specificity of DNA aptamers with the unique properties of the carbon nanomaterial graphene offers an excellent avenue for sensitive and selective biosensing architectures. In this work, a highly sensitive and selective aptasensor which utilizes an unlabeled DNA aptamer assembled on a graphene electrode for microcystin-LR detection was developed. A facile strategy was used for the aptasensor fabrication on the basis of the noncovalent assembly of DNA aptamer on graphene-modified screen printed carbon electrodes. Assembly of the DNA aptamer on the graphene-modified electrodes caused a marked drop in the square wave voltammetric reduction signal of the [Fe(CN)6](4-/3-) redox couple. The presence of microcystin-LR, on the other hand, caused a dose-responsive increase in peak current, allowing the quantification of microcystin-LR through the measurement of peak current change. Under optimal conditions, the detection limit of the developed aptasensor was 1.9 pM in buffer, a concentration much lower than those offered by previously reported biosensors for microcystin-LR. The developed aptasensor also exhibited excellent selectivity for microcystin-LR with no detectable cross-reactivity to okadaic acid, microcystin-LA, and microcystin-YR. Moreover, the proposed aptasensor has been applied for the analysis of spiked tap water and fish samples showing good recovery percentages. This novel, simple, high-performance, and low-cost detection platform would facilitate the routine monitoring of microcystin-LR in real samples.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".