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 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) | 0.001 | 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".