Arraying faceted manganese oxides for selective ethylene electro-oxidation to ethylene glycol in aqueous electrolytes
Bibliographic record
Abstract
The electro-oxidation of ethylene to ethylene glycol (EG) offers a sustainable pathway for chemical manufacturing, but demands selective non-precious-metal (NPM) electrocatalysts. Here, we design and fabricate a class of arrayed Mn2O3 electrode, which shows a high EG selectivity in practically-favorable aqueous electrolytes among NPM catalysts. By screening various manganese oxides, we first pinpoint Mn2O3 to be the most selective to EG. Density functional theory calculations further reveal that the (111) facet facilitates the second OH* addition to *C2H4OH, the rate-limiting step toward EG. These fundamental findings motivate us to controllably synthesize the (111)-dominant Mn2O3 nanoarrays, which deliver a 52.6 % Faradaic efficiency for EG—the highest for NPM electrocatalysts in aqueous media. Electrochemical and operando spectral studies verify that stabilizing moderately oxidized Mn (III) state under operational anodic bias is essential to the high selectivity of EG. Our findings highlight the crucial role of Mn surface chemistry in steering alkene oxidation and advance the electrosynthesis of EG closer to practicability. The electrooxidation of ethylene to ethylene glycol (EG) offers a sustainable pathway for chemical manufacturing, but demands selective non-precious-metal electrocatalysts. Here, the authors report a class of (111)-rich Mn2O3 nanoarray electrode, which achieves a high selectivity of 52.6% for for ethylene-to-EG conversion in aqueous electrolytes.
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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".