Sustainable electrochemical biosensor using polyol-protected probes and magnetic MOFs for thermally robust coronavirus gene detection
Bibliographic record
Abstract
Magnetic metal–organic frameworks (MMOFs) offer a promising, environmentally conscious platform for electrochemical sensing due to their high surface area, easy functionalization, and compatibility with green synthesis routes. In this study, MMOFs were synthesized using ferric chloride, imidazole, and histidine, then functionalized with covalently bound DNA capture probes for the electrochemical detection of the coronavirus nucleocapsid gene. Target-probe hybridization was transduced using neutral red intercalation and differential pulse voltammetry. To enhance thermal robustness, polyol-based protectants, polyethylene glycol and glycerol, were evaluated for their ability to preserve DNA functionality under high-temperature exposure (50–90 °C). Kinetic degradation behavior was modeled using a first-order Arrhenius approach, revealing that glycerol significantly reduced probe degradation and increased sensor half-life from 0.23 to 1.36 days at 90 °C. The probe density on the MMOF surface was shown to influence thermal stability, with moderate densities yielding optimal retention of the detection signal. The genosensor demonstrated ultra-low detection limits (down to 0.38 fM) and excellent linearity across six orders of magnitude in various biological fluids, including saliva, urine, and serum. This work demonstrates a sustainable sensing strategy that integrates green materials, benign thermal protection, and reusable nanostructures—advancing the development of eco-friendly diagnostic technologies.
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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".