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 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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".