Green Approach Using RuO<sub>2</sub>/GO Nanocomposite for Low Cost and Highly Sensitive pH Sensing
Bibliographic record
Abstract
Rapid and inexpensive monitoring the real-time status of food products using pH sensors is critical for food quality and safety to determine if pathogens are present and growing. A promising material for pH sensors is ruthenium dioxide (RuO2) due to its chemical stability and excellent performance including: high sensitivity, low drift and hysteresis, and good selectivity. Furthermore, graphene oxide (GO) provides an electrode with large surface area, and good electrical properties. In this work, the in situ sol-gel deposition of RuO2 nanoparticles on the surface of GO as a facile, cost-effective, and environmentally friendly approach is used for the fabrication of a flexible pH sensor. The as-synthesized GO-RuO2 nanocomposites with a low volume were applied on the surface of screen printed carbon paste. The obtained GO-RuO2 nanocomposite pH sensor achieved high pH sensitivity (55.3 mV pH−1) in the pH range of 4–10, up to 4 times higher than the unmodified carbon electrode. The increased sensitivity of the modified electrode could be attributed to the uniform anchoring of small, crystallized RuO2 nanoparticles on the surface of GO sheets, resulting in synergistic effects between them. It also shows low drift (0.36 mV h−1) and low hysteretic width (0.8 mV). Considering the novel method of deposition and also sensing material with the cost-effective green synthesis approach, as well as excellent pH sensing properties, GO-RuO2 can be considered as a promising material for production of high-performance electrochemical pH sensors for food quality 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) | 0.001 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".