The Golden Atomic Ratio in Binary Nanoalloys for Enhanced CO <sub>2</sub> Electroreduction: Dual-Metal Synergy of AgPd
Bibliographic record
Abstract
An intriguing phenomenon has been observed in various binary nanoalloys for the electrochemical CO 2 reduction reaction (CO 2 RR), where an atomic ratio close to 1:3 mostly yields the optimal activity, but its origin remains poorly understood. Here, we synthesized a series of size-uniform Ag x Pd 1– x nanoclusters (NCs) with precisely controlled atomic ratios as a model system to verify its universality and intrinsic derivation since Ag offers a high intrinsic CO selectivity but requires a large overpotential (η) due to weak intermediate binding, while Pd forms CO at a small η but suffers from CO poisoning due to an overly strong CO adsorption. Indeed, Ag 0.25 Pd 0.75 NCs with an atomic ratio of 1:3 possessed optimal CO 2 RR activity, delivering a nearly 100% CO Faraday efficiency and a maximum energy efficiency of 71.8%. Computational calculations demonstrated that the Ag/Pd atomic ratio of 1:3 induced an optimal electronic structure characterized by a d -band center positioned favorably relative to the Fermi level. This configuration synergistically lowered the energy barrier for *COOH formation and promoted *CO desorption kinetics, as corroborated by in situ spectroscopic analysis, where Ag 0.25 Pd 0.75 NCs exhibited attenuated *CO adsorption signals compared with other stoichiometries, indicating enhanced CO desorption capability. This study provides in-depth mechanistic insights into the “golden ratio” in nanoalloys for the CO 2 RR and reveals a universal paradigm of designing advanced nanoalloys.
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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 | 0.000 | 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 | 0.000 |
| 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 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".