A sensitive biosensor for cyanide detection using modified glassy carbon electrode with reduced graphene oxide and immobilization of horseradish peroxidase
Bibliographic record
Abstract
Abstract Cyanide is a highly poisonous and hazardous substance which may release into the environment from natural sources or industrial effluent; therefore, cyanide detection is a fundamental step to prevent environmental pollution and secure health and safety. In this study, we prepared a sensitive amperometric inhibition biosensor for cyanide detection by immobilization of horseradish peroxidase (HRP) enzyme and reduced graphene oxide (rGO) on the surface of glassy carbon electrode (GCE). To do so, we performed the amperometric measurement by modified GCE to test its efficiency in detecting cyanide. The optimum conditions of pH equal to 7.5, −100 mV applied potential, 0.7 μM mediator concentration, and 0.5 mM substrate concentration were found. Then, experiments were performed at different boundary conditions in a range of 0.1 to 10 μM cyanide concentration at optimal conditions and a low detection limit of 0.01 μM was obtained. Also, the possible mechanism of inhibition was analyzed based on the Michalis–Menten equation and non‐competitive inhibition was observed. Due to high sensitivity, low detection limit, and low cost, this biosensor is proposed as a useful method for cyanide determination in real 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.001 |
| 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".