Nanoscopic Silicon Oxide Overlayers Improve the Performance of Ruthenium Oxide Electrocatalysts Toward the Oxygen Evolution Reaction
Bibliographic record
Abstract
RuO2 is a highly active electrocatalyst for the oxygen evolution reaction (OER) but is unstable in acidic environments. Herein we investigate the encapsulation of RuO2 nanoparticles with semipermeable, nanoscopic silicon oxide (SiOx) overlayers as a strategy to improve their stability. SiOx encapsulated RuO2 (SiOx|RuO2) electrodes were prepared by drop-casting RuO2 nanoparticles onto glassy carbon substrates followed by deposition of SiOx overlayers of varying thickness by a room temperature photochemical deposition process. The best-performing SiOx|RuO2 electrodes consisted of 2–3 nm thick SiOx overlayers on top of RuO2 particles and 3–7 nm thick SiOx on the glassy carbon substrate. Such electrodes exhibited lower overpotentials relative to bare RuO2 due to an improved electrochemically active surface area while also demonstrating an ability to retain OER activity over time, especially at higher overpotentials. Surprisingly, it was found that the SiOx coating was unable to prevent Ru dissolution, which was found to be proportional to the charge passed and independent of the presence or thickness of the SiOx coating. Thus, other possible explanations for the improved current retention of SiOx|RuO2 electrodes are discussed, including the influences of the overlayer on bubble dynamics and the stability of the underlying glassy carbon substrate.
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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 | 0.000 | 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".