CO <sub>2</sub> Electroreduction to CO Over Silver Nanoclusters: The Impact of Nuclearity on Synergistic Activity Modulation
Bibliographic record
Abstract
Abstract Electrochemical reduction of CO 2 (eCO 2 R) powered by renewable energy holds the potential to produce sustainable platform chemicals and decarbonize the hard‐to‐abate sectors. Herein, the structure‐activity correlation of atomically precise silver nanoclusters (NCs) in eCO 2 R to carbon monoxide (CO) is studied, elucidating the effect of the nuclearity of metal core and the electronic nature of the ligands. Electrocatalytic studies on Ag NCs, [Ag 21 (MCT) 12 (TPP) 2 ] + , [Ag 31 (TRZ) 10 ] 2− , [Ag 42 (CBDT) 15 (TPP) 4 ] 2− (shortly, Ag 21 , Ag 31 , and Ag 42 , respectively), reveal that the CO Faradaic efficiency (FE CO ) increases while the FE CO(max) (the maximum FE CO ) moves to higher positive potentials upon decreasing the nuclearity of these Ag NCs, almost in a quantitative correlation. Notably, every ≈ten Ag atoms variation in the cluster shifts the potentials for FE CO(max) and maximum partial current density, j CO ( max ) by ≈70 and ≈80 mV, respectively. The smallest nanocluster, Ag 21 , achieved a near‐unity FE CO(max) of 99.6% at −0.59 V vs RHE, and a competitive eCO 2 R‐to‐CO rate, producing a j CO ( max ) of 148 mA cm −2 at −0.7 V vs RHE. First principle calculations reveal that decreasing the atomicity in Ag NCs reduces the activation energy barriers for the 2e − reduction pathway due to the modulation of surface charge distribution and the electronic density of states of the active Ag sites.
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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".