Boosting Neutral Water Oxidation through Surface Oxygen Modulation
Bibliographic record
Abstract
Abstract Developing efficient electrocatalysts for oxygen evolution reaction (OER) in pH‐neutral electrolyte is crucial for microbial electrolysis cells and electrochemical CO2 reduction. Unfortunately, the OER kinetics in neutral electrolyte is sluggish due to the low concentration of adsorbed reactants, with overpotentials of neutral OER at present much higher than that in acidic or alkaline electrolyte. Here, hydrated metal cations (Ca2+) are sought to be incorporated into the state‐of‐the‐art Ru–Ir binary oxide to tailor the surface oxygen environments (lattice‐oxygen and adsorbed oxygen species) for efficient neutral OER. Using a sol–gel method, ternary Ru–Ir–Ca oxides are synthesized in atomically homogenous manner, and the obtained catalyst on glassy carbon electrode achieves 10 mA cm−2 at a low overpotential of 250 mV, with no degradation for 200 h of operation. In situ X‐ray absorption spectroscopy, in situ 18O isotope‐labeling differential electrochemical mass spectrometry, and 18O isotope‐labeling secondary ion mass spectroscopy studies are carried out. The results reveal that incorporation of Ca2+ can enhance the covalency of metal–oxygen bonds and the electrophilic nature of surface metal‐bonded oxygen sites; and simultaneously facilitate the adsorption of water molecules on catalyst surface, which accelerates the lattice‐oxygen‐involved reaction, thus improving the overall OER performance of RuIrCaOx catalyst.
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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".