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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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) | 0.000 | 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".