Integrated pH Sensors Based on RuO<sub>2</sub>/GO Nanocomposites Fabricated Using the Aerosol Jet Printing Method
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide An aerosol jet printing (AJP) process for depositing ruthenium dioxide (RuO 2 ) as a promising material for pH sensing is reported. Graphene oxide (GO) with a large surface area was used for the in situ sol–gel deposition of RuO 2 nanoparticles on its surface. The cosolvent ratio and solid loading of the solution are adjusted to form a printable and stable ink. The monodispersed aerosol was atomized on the surface of the screen-printed carbon electrode in order to develop an integrated pH sensor. The RuO 2 –GO pH sensor demonstrates excellent performance, with a rapid response time of less than 5 s and high sensitivity in the pH range of 4–10. Compared to traditional carbon electrodes, the RuO 2 –GO sensor shows up to four times higher sensitivity. The increased sensitivity is a result of the consistent attachment of small-crystallized RuO 2 nanoparticles onto the surface of GO sheets, leading to a synergistic effect. Thanks to the AJP method as a facile and cost-effective integration technique, the fabricated electrodes can serve as an alternative to traditional rigid pH electrodes for accurate pH measurement.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".