Revealing Real Active Sites in Intricate Grain Boundaries Assemblies on Electroreduction of CO<sub>2</sub> to C<sub>2+</sub> Products
Bibliographic record
Abstract
Abstract Although intricate structural assemblies contribute to enhancing the activity of electrocatalytic CO2 reduction (ECR) to C2+ products, blindly coupling multiple design strategies may not yield the expected results, and even inhibit the activity of intrinsic catalytic sites. Therefore, elucidating the promoting or inhibitory effects of each design strategy on the CO2‐to‐C2+ conversion to clarify the real active sites and dynamic oxidation processes is of paramount importance. Here, commonly used grain boundaries (GBs), oxidation states, and alloying strategies are focused on, constructing four different types of catalysts structures: original Cu GBs, oxygen‐enriched grain boundary oxidation (GBO), Ag‐enriched GBO, and Cu/Ag GBs. Multiple operando characterizations reveal that GBs and GBO strengthen the resistance of the oxidative Cu species to the electrochemical reduction. The in situ generated strongly oxidative hydroxyl radicals alter the local reaction environment on the catalyst surface, inducing and stabilizing oxidative Cuδ+ species. Catalytic activity comparisons indicate that the oxidation state of Cu plays a decisive role in the CO2‐to‐C2+ conversion, and the nanoalloy effect tends to favor the CH4 production in intricate GBs assemblies. Theoretical calculations suggest that weak CO adsorption on GBO structures facilitates hydrogenation, promoting C–C coupling toward C2+ products.
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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) | 0.000 | 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".