Durable nonenzymatic electrochemical sensing using silver decorated multi-walled carbon nanotubes for uric acid detection
Bibliographic record
Abstract
In this study, we demonstrate a facile, durable, and inexpensive technique of producing silver nanoparticles-decorated multi-walled carbon nanotubes (MWCNT/AgNP) on the easy-to-use screen-printed carbon electrodes (SPCE) for non-enzymatic detection of uric acid (UA) in an electrochemical sensor. The developed sensors show great durability for three months in storage, and high specificity performance for preclinical study using spiked UA in a synthetic urine sample. A simple route for this hybrid nanocomposite was proposed through an oxidation-reduction with reflux (ORR) process. A significant increase in the electroactive surface area of SPCE was achieved by modifying it with MWCNT/AgNP. Scanning electron microscopy (SEM), transmission electron microscopy (TEM), energy-dispersive X-ray spectroscopy (EDX), Fourier-transform infrared (FT-IR) spectroscopy, Raman spectroscopy, and X-ray diffraction (XRD) analysis confirmed this synthesis. The nanocomposite nanostructure electrodes achieved an outstanding UA detection with a sensitivity of 0.1021 µA/µM and a wide dynamic range of 10-1000 µM. In PBS, the measurements achieved a detection limit of 84.04 nM while in pure synthetic urine; it was 6.074 µM. The constructed sensor exhibits excellent stability and durability for several months, and great specificity against interfering compounds, including dopamine (DA), urea, and glucose. Overall, the present outcomes denote the potential of MWCNT/AgNP-decorated SPCE for early uric acid diagnostics tools in health monitoring.
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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) | 0.000 | 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 | 0.001 | 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".