Nanomaterial-Based Electrochemical Sensor for the Monitoring of Sodium Metabisulfite
Bibliographic record
Abstract
Deoxynivalenol (DON) is a mycotoxin, which is produced by the Fusarium genus and widely found in cereal grains such as wheat and corn. Swine are sensitive to DON; even a small amount (1 ppm) can make swine sick (e.g., upset stomach, vomit, less feed intake). Sodium metabisulfite (SMBS) is a promising feed additive in swine farming to overcome the issues caused by DON. Currently, high performance liquid chromatography, mass spectroscopy, UV-Visible spectroscopy and infrared spectroscopy are used for the quantification of SMBS. However, all these techniques are expensive and time-consuming; they cannot be used in field analysis. Here, we report on an advanced electrochemical sensor based on fluorinated reduced graphene oxide modified with gold nanoparticles (Au/F-rGO) for rapid detection and monitoring of SMBS. Scanning electron microscopy and energy dispersive X-ray spectroscopy were employed to characterize the morphology and the composition of the fabricated Au/F-rGO electrode. Cyclic voltammetry, linear sweep voltammetry and differential pulse voltammetry were used to investigate the electrochemical performance of the Au/F-rGO sensor. Our study has shown that the optimized Au/F-rGO electrode exhibits a wide linear range of responses, a low limit of detection, high sensitivity and high stability for the SMBS detection. The sensor has been further tested with different digestive fluids (e.g., stimulated salivary fluid, stimulated intestinal fluid and stimulated gastric fluid), showing high selectivity and promising practical applications.
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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 | 0.000 |
| 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".