Electrocatalytic synthesis of hydrogen peroxide: insights from mechanism to material design
Bibliographic record
Abstract
Hydrogen peroxide (H 2 O 2 ) represents an important inorganic chemical product. It finds extensive applications across diverse sectors such as textiles, papermaking, chemical industry and water treatment. For industrial production of H 2 O 2 , the anthraquinone-based process, being extremely energy-consuming, fails to meet the requirements of sustainability imposed by huge market demands. The electrochemical synthesis approaches, two-electron Oxygen Reduction Reaction (2e-ORR) and two-electron Water Oxidation Reaction (2e-WOR), are capable to produce H 2 O 2 on-site. Notably, in the whole process, the reactants are merely oxygen, water, and electric energy. These electrochemical processes have emerged as a focal point in research, prompting substantial efforts to be directed towards the development of highly efficient and stable electrocatalysts. This review centered on the latest research progress regarding 2e-ORR/WOR from the perspective of theoretical calculations along with corresponding experimental results. The details of basic principles, impact factors (e.g. pH and electrolyte ions) and catalyst developments of the electrochemical H 2 O 2 production as well as the advanced characterization techniques are summarized and discussed. We emphasized the relationships between the electronic structure of catalysts and key adsorption intermediates (e.g. ∗OOH for 2e-ORR and ∗OH for 2e-WOR) by using computational methods. The design principles, challenges, and future work for the electrochemical H 2 O 2 production are also proposed.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".