“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 CO2 reduction (CO2RR) 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 CO2RR 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 CO2RR 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 CO2RR electrocatalyst, this work presents a promising catalyst-design strategy for efficient energy conversion.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".