Tuning Two‐Electron Oxygen‐Reduction Pathways for H<sub>2</sub>O<sub>2</sub> Electrosynthesis via Engineering Atomically Dispersed Single Metal Site Catalysts
Bibliographic record
Abstract
Abstract The hydrogen peroxide (H2O2) generation via the electrochemical oxygen reduction reaction (ORR) under ambient conditions is emerging as an alternative and green strategy to the traditional energy‐intensive anthraquinone process and unsafe direct synthesis using H2 and O2. It enables on‐site and decentralized H2O2 production using air and renewable electricity for various applications. Currently, atomically dispersed single metal site catalysts have emerged as the most promising platinum group metal (PGM)‐free electrocatalysts for the ORR. Further tuning their central metal sites, coordination environments, and local structures can be highly active and selective for H2O2 production via the 2e− ORR. Herein, recent methodologies and achievements on developing single metal site catalysts for selective O2 to H2O2 reduction are summarized. Combined with theoretical computation and advanced characterization, a structure–property correlation to guide rational catalyst design with a favorable 2e− ORR process is aimed to provide. Due to the oxidative nature of H2O2 and the derived free radicals, catalyst stability and effective solutions to improve catalyst tolerance to H2O2 are emphasized. Transferring intrinsic catalyst properties to electrode performance for viable applications always remains a grand challenge. The key performance metrics and knowledge during the electrolyzer development are, therefore, highlighted.
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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".