Local Atomic Modulation of Metal Sites Drives Efficient Electrochemical Reduction of CO<sub>2</sub>
Bibliographic record
Abstract
Electrochemical reduction of carbon dioxide (CO2RR) has attracted intense research attention since it provides an avenue to the renewable-electricity-powered synthesis of value-added carbon-based fuels and feedstocks. Unfortunately, high selectivity in formate electrosynthesis has thus far only been achieved at the expense of low current density and high over-potential. Herein, we report a strategy to create high active sites in tin catalysts by incorporating copper and non-metal sulfur in an intimately integrated fashion. We hypothesized that the presence of copper and sulfur atoms in the catalyst surface could promote under-coordinated metal sites, and thereby improve the electrochemical reduction of CO2 to formate. We explored, using density functional theory, how the incorporation of copper and sulfur atoms into tin favours formate generation. The resultant under-coordinated tin sites accelerate CO2RR at record geometric current densities with a Faradaic efficiency of 93% and exhibit prolonged stability. Furthermore, the partial current density for formate on under-coordinated tin sites – normalized by electrochemically active surface area – is also three times higher than that of unmodified tin catalysts. X-ray absorption studies and density functional theory reveal the synergistic role of copper, sulfur and tin in producing local coordination and a consequent electronic structure that enhances electron capturing ability.
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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".