Local pH Gradients Alter Product Selectivity of Electrocatalytic Oxygen Reduction Reaction on La‐Cr‐Co Perovskites
Bibliographic record
Abstract
Abstract The electrochemical production of hydrogen peroxide through the oxygen reduction reaction (ORR) presents the interesting situation where the fully reduced form, H2O, is significantly favored by thermodynamics. Perovskite oxide based catalysts with slow kinetics, or large overpotentials, have been shown to enable production of H2O2 with good selectivity, but this comes at the expense of energy efficiency and production rates. We analyze the structure and electrocatalytic ORR capabilities of a perovskite oxide series based on LaCr1‐xCoxO3. We find that the crystal lattice distorts in two ways as Co content increases – anisotropic compression of the lattice is accompanied by a pinching of angles within the structure, which gives way to compression of B−O bonds at higher Co contents. Product selectivity is observed to change as a function of both catalyst composition and rotation rates of rotating ring‐disk electrode. The rotation‐dependent behavior is attributed to accelerated displacement of surface‐bound peroxide intermediates by a pH gradient at the catalyst surface, and composition‐dependent changes in selectivity are found to correlate to the angular distortion of the crystal lattice. These results highlight the importance of local environments in electrocatalyst reactions and demonstrate that structure and local pH can be used to manipulate product selectivity.
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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".