Sustainable H <sub>2</sub> O <sub>2</sub> production via solution plasma catalysis
Bibliographic record
Abstract
Clean production of hydrogen peroxide (H 2 O 2 ) with water, oxygen, and renewable energy is considered an important green synthesis route, offering a valuable substitute for the traditional anthraquinone method. Currently, renewable energy–driven production of H 2 O 2 mostly relies on soluble additives, such as electrolytes and sacrificial agents, inevitably compromising the purity and sustainability of H 2 O 2 . Herein, we develop a solution plasma catalysis technique that eliminates the need for soluble additives, enabling eco-friendly production of concentrated H 2 O 2 directly from water and O 2 . Screening over 40 catalysts demonstrates the superior catalytic performance of carbon nitride interacting with discharge plasma in water. High-throughput density functional theory calculations for 68 models, along with machine learning using 29 descriptors, identify cyano carbon nitride (CCN) as the most efficient catalyst. Solution plasma catalysis with the CCN achieves concentrated H 2 O 2 of 20 mmol L −1 , two orders of magnitude higher than photocatalysis by the same catalyst. Plasma diagnostics, isotope labeling, and COMSOL simulations collectively validate that the interplay of solution plasma and the CCN accounts for the significantly increased production of singlet oxygen and H 2 O 2 thereafter. Our findings offer an efficient and sustainable pathway for H 2 O 2 production, promising wide-ranging applications across the chemical industry, public health, and environmental remediation.
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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".