Synergistic Lewis and Brønsted Acid Sites Promote OH* Formation and Enhance Formate Selectivity: Towards High‐efficiency Glycerol Valorization
Bibliographic record
Abstract
Abstract As a sustainable valorization route, electrochemical glycerol oxidation reaction (GOR) involves in formation of key OH* and selective adsorption/cleavage of C−C(O) intermediates with multi‐step electron transfer, thus suffering from high potential and poor formate selectivity for most non‐noble‐metal‐based electrocatalysts. So, it remains challenging to understand the structure–property relationship as well as construct synergistic sites to realize high‐activity and high‐selectivity GOR. Herein, we successfully achieve dual‐high performance with low potentials and superior formate selectivity for GOR by forming synergistic Lewis and Brønsted acid sites in Ni‐alloyed Co‐based spinel. The optimized NiCo oxide solid‐acid electrocatalyst exhibits low reaction potential (1.219 V@10 mA/cm 2 ) and high formate selectivity (94.0 %) toward GOR. In situ electrochemical impedance spectroscopy and pH‐dependence measurements show that the Lewis acid centers could accelerate OH* production, while the Brønsted acid centers are proved to facilitate high‐selectivity formation of formate. Theoretical calculations reveal that NiCo alloyed oxide shows appropriate d ‐band center, thus balancing adsorption/desorption of C−O intermediates. This study provides new insights into rationally designing solid‐acid electrocatalysts for biomass electro‐upcycling.
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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".