Selective and Scalable CO<sub>2</sub> Electrolysis Enabled by Conductive Zinc Ion-Implanted Zeolite-Supported Cadmium Oxide Nanoclusters
Bibliographic record
Abstract
Catalyst supports play an essential role in catalytic reactions, hinting at pronounced metal–support effects. Zeolites are a propitious support in heterogeneous catalysts, while their use in the electrocatalytic CO 2 reduction reaction has been limited as yet because of their electrically insulating nature and serious competing hydrogen evolution reaction (HER). Enlightened by theoretical prediction, herein, we implant zinc ions into the structural skeleton of a zeolite Y to strategically tailor a favorable electrocatalytic platform with remarkably enhanced electronic conduction and strong HER inhibition capability, which incorporates ultrafine cadmium oxide nanoclusters as guest species into the supercages of the tailored 12-ring window framework. The metal d-bandwidth tuning of cadmium by skeletal zinc steers the extent of substrate–molecule orbital mixing, enhancing the stabilization of the key intermediate *COOH while weakening the CO poisoning effect. Furthermore, the strong cadmium–zinc interplay causes a considerable thermodynamic barrier for water dissociation in the conversion of H + to *H, potently suppressing the competing HER. Therefore, we achieve an industrial-level partial current density of 335 mA cm –2 and remarkable Faradaic efficiency of 97.1% for CO production and stably maintain Faradaic efficiency above 90% at the industrially relevant current density for over 120 h. This work provides a proof of concept of tailored conductive zeolite as a favorable electrocatalytic support for industrial-level CO 2 electrolysis and will significantly enhance the adaptability of conductive zeolite-based electrocatalysts in a variety of electrocatalysis and energy conversion applications.
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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".