Efficient Electrochemical CO<sub>2</sub> Reduction Using AgN<sub>3</sub> Single‐Atom Sites Embedded in Free‐Standing Electrodes for Flow Cell Applications
Bibliographic record
Abstract
The electrochemical reduction of CO 2 into valuable chemicals presents a promising strategy for carbon utilization; however, it remains challenging due to low activity, poor selectivity and stability of existing catalysts. In this study, we report the fabrication of free‐standing silver single‐atom catalysts (Ag SACs) designed for the efficient conversion of CO 2 to carbon monoxide (CO) at high current densities in a bicarbonate electrolyzer. The Ag single atoms dispersed within a carbon matrix, forming AgN 3 active sites for the electrocatalytic CO 2 reduction reaction (CO 2 RR). The catalytic activity and stability of the free‐standing Ag SACs are evaluated at a current density of 100 mA cm −2 , demonstrating prolonged electrolysis with consistent Faradaic efficiency for CO production. Density functional theory calculations revealed that the AgN 3 active site significantly lowers the energy barriers for the CO 2 absorption step compared to AgAg and AgNi sites, facilitating CO 2 activation and contributing to enhanced catalytic activity and stability during CO 2 reduction. Detailed analysis of the electronic structure and coordination environment further validates the superior performance of the AgN 3 site in the free‐standing Ag SACs, underscoring their effectiveness in CO 2 electroreduction. These findings highlight the potential of the free‐standing Ag SACs to advance CO 2 reduction technologies, offering enhanced efficiency and selectivity for CO 2 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 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".