The Cationic Enhancement Effect on the Two-Electron Oxygen Reduction Reaction in Acidic Conditions at Carbon-Based Cathodes
Bibliographic record
Abstract
Hydrogen peroxide (H2O2) is a green oxidant, widely used in industry. To turn its synthesis green, research focused on the development of efficient catalysts for the two-electron oxygen reduction reaction (2e-ORR) to produce H2O2 from water and molecular oxygen. Despite recent progress, electrolyte effects of the electrochemical H2O2 production have remained little understood. We report a significant effect of alkali metal cations (AMCs) on the electrocatalytic H2O2 production on carbon catalysts in acidic environments. The presence of AMCs at the electrified carbon interface shift the half wave potential of the 2e-ORR from -0.48 V to -0.22 VRHE. This cationic induced enhancement effect exhibits a uniquely sensitive on/off switching behaviour depending on the voltammetric protocol. Voltammetric and direct in situ X-ray photoemission spectroscopic evidence is presented that supports a controlling role of the potential of zero charge (PZC) of the catalytic enhancement. Depending whether the electrode potential is kept cathodic or even just briefly reaches values anodic of the PZC, AMCs accumulate at the electrified interface and enhance the 2e-ORR or get repelled away from it, respectively. Density functional theory calculations associate the enhancement by the stabilization of the *OOH key intermediate. Based on this finding, we developed a refined reaction mechanism for the H2O2 production in presence of AMCs.
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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".