High-Nuclearity Copper Molecular Catalysts for Electrocatalytic CO-to-Acetate Conversion
Bibliographic record
Abstract
Ligand-modified metal nanoclusters (NCs) have emerged as candidate materials for catalysis owing to their well-defined yet tunable structure and their metal centers’ high nuclearity. We posited that NC-based catalytic behavior will depend on ligand properties, the accessibility of active sites, and their atomic configuration. We synthesized a series of Cu NC-based catalysts, tuned local hydrophobicity through ligand adjustment, balanced the ligand coverage and active site exposure, and found that we were, in this way, able to engender efficient electrosynthesis of acetate via CO electroreduction. Computation and operando spectroscopy show that asymmetric Cu–Cu sites, which determine the CO binding strength, impact the bifurcation step after C–C coupling. The best of these catalysts, Cu 13 Nap, achieved an acetate Faradaic efficiency (FE) of 86% and an energy efficiency of 29% in a 5 bar system, exceeding the single C 2+ FE of <50% previously achieved by NC-based catalysts.
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 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".