A Multipurpose and Multilayered Microneedle Sensor for Redox Potential Monitoring in Diverse Food Analysis
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Bibliographic record
Abstract
This work presents a multipurpose and multilayered stainless steel microneedle sensor for the in situ redox potential monitoring in food and drink samples, termed MN redox sensor. The MN redox sensor was fabricated by layer-by-layer (LbL) approach. The in-tube multilayer coating comprised carbon nanotubes (CNTs)/cellulose nanocrystals (CNCs) as the first layer, polyaniline (PANI) as the second layer, and the ferrocyanide redox couple as the third layer. Using cyclic voltammetry (CV) as a transduction method, the MN redox sensor showed facile electron transfer for probing both electrical capacitance and redox potential, useful for both analyte specific and bulk quantification of redox species in various food and drink samples. The bulk redox species were quantified based on the anodic/cathodic redox peak shifts (Ea/Ec) on the voltammograms resulting from the presence of redox-active species. The MN redox sensor was applied to detect selected redox species including ascorbic acid, H2O2, and putrescine, with capacitive limits of detection (LOD) of 49.9, 17.8, and 263 ng/mL for each species, respectively. For the bulk determination of redox species, the MN redox sensor displayed LOD of 5.27 × 103, 55.4, and 25.8 ng/mL in ascorbic acid, H2O2, and putrescine equivalents, respectively. The sensor exhibited reproducibility of ~1.8% relative standard deviation (%RSD). The MN redox sensor was successfully employed for the detection of fish spoilage and antioxidant quantification in king mushroom and brewed coffee samples, thereby justifying its potential for food quality and food safety applications. Lastly, the portability, reusability, rapid sampling time, and capability of in situ analysis of food and drink samples makes it amenable for real-time sensing applications.
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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 | 0.001 |
| 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 it