Ligand-modified nanoparticle surfaces influence CO electroreduction selectivity
Bibliographic record
Abstract
Abstract Improving the kinetics and selectivity of CO 2 /CO electroreduction to valuable multi-carbon products is a challenge for science and is a requirement for practical relevance. Here we develop a thiol-modified surface ligand strategy that promotes electrochemical CO-to-acetate. We explore a picture wherein nucleophilic interaction between the lone pairs of sulfur and the empty orbitals of reaction intermediates contributes to making the acetate pathway more energetically accessible. Density functional theory calculations and Raman spectroscopy suggest a mechanism where the nucleophilic interaction increases the sp 2 hybridization of CO (ad) , facilitating the rate-determining step, CO* to (CHO)*. We find that the ligands stabilize the (HOOC–CH 2 )* intermediate, a key intermediate in the acetate pathway. In-situ Raman spectroscopy shows shifts in C–O, Cu–C, and C–S vibrational frequencies that agree with a picture of surface ligand-intermediate interactions. A Faradaic efficiency of 70% is obtained on optimized thiol-capped Cu catalysts, with onset potentials 100 mV lower than in the case of reference Cu catalysts.
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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".