“Two Ships in a Bottle” Design for Zn–Ag–O Catalyst Enabling Selective and Long-Lasting CO<sub>2</sub> Electroreduction
Bibliographic record
Abstract
Electrochemical CO 2 reduction (CO 2 RR) using renewable energy sources represents a sustainable means of producing carbon-neutral fuels. Unfortunately, low energy efficiency, poor product selectivity, and rapid deactivation are among the most intractable challenges of CO 2 RR electrocatalysts. Here, we strategically propose a “two ships in a bottle” design for ternary Zn–Ag–O catalysts, where ZnO and Ag phases are twinned to constitute an individual ultrafine nanoparticle impregnated inside nanopores of an ultrahigh-surface-area carbon matrix. Bimetallic electron configurations are modulated by constructing a Zn–Ag–O interface, where the electron density reconfiguration arising from electron delocalization enhances the stabilization of the *COOH intermediate favorable for CO production, while promoting CO selectivity and suppressing HCOOH generation by altering the rate-limiting step toward a high thermodynamic barrier for forming HCOO*. Moreover, the pore-constriction mechanism restricts the bimetallic particles to nanosized dimensions with abundant Zn–Ag–O heterointerfaces and exposed active sites, meanwhile prohibiting detachment and agglomeration of nanoparticles during CO 2 RR for enhanced stability. The designed catalysts realize 60.9% energy efficiency and 94.1 ± 4.0% Faradaic efficiency toward CO, together with a remarkable stability over 6 days. Beyond providing a high-performance CO 2 RR electrocatalyst, this work presents a promising catalyst-design strategy for efficient energy conversion.
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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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".