Universal high-efficiency electrocatalytic olefin epoxidation via a surface-confined radical promotion
Bibliographic record
Abstract
Production of epoxides via selective oxidation of olefins affords a fundamental source of key intermediates for the industrial manufacture of diverse chemical stocks and materials. Current oxidation strategy generally works under harsh conditions including high temperature, high pressure, and/or request for potentially hazardous oxidants, leading to substantial challenges in sustainability and energy efficiency. To this end, direct electrocatalytic epoxidation poses as a promising solution to these issues, yet their industrial applications are limited by the low selectivity, low yield, and poor stability of the electrocatalysts. Here we report a universal electrochemical epoxidation approach via a kinetically confined surface radical pathway. High epoxidation efficiency can be achieved under mild working conditions (e.g., >99% selectivity, >80% yield and >80% Faraday efficiency for cyclohexene-to-cyclohexene oxide conversion), which can be extended to broad scope of olefin substrates. The catalytic performance originated from a surface bimolecular (L-H) reaction mechanism involving formation and surface confinement of bromine radicals due to kinetic restriction, which effectively activates inert C=C bonds while avoiding the homogenous radical side reactions. With the use of renewable energy and water as green oxygen source, successful implementation of this approach will pave the way for more sustainable chemical production and manufacturing.
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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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".