Ultrasmall Au nanocatalysts supported on nitrided carbon for electrocatalytic CO<sub>2</sub> reduction: the role of the carbon support in high selectivity
Bibliographic record
Abstract
Au is one of the most promising electrocatalysts to convert CO2 into CO in an aqueous-phase electrochemical reduction. However, ultrasmall Au nanocatalysts (AuNCs, <2 nm) have proven to be favorable for water reduction over CO2, although they possess a large surface-to-volume ratio and potentially are ideal for CO2 reduction. We herein report that ultrasmall AuNCs (1.9 ± 0.3 nm) supported on nitrided carbon are remarkably active and selective for CO2 reduction. The mass activity for CO of AuNCs reaches 967 A g-1 with a faradaic efficiency for CO of ∼83% at -0.73 V (vs. reversible hydrogen electrode) that is an order of magnitude more active than the state-of-the-art results. The high activity is endowed by the large surface area per unit weight and the high selectivity of ultrasmall AuNCs for CO2 reduction originates from the cooperative effect of Au and the nitrided carbon support where the surface N sites act as Lewis bases to increase the surface charge density of AuNCs and enhance the localized concentration of CO2 nearby catalytically active Au sites. We show that our results can be applied to other pre-synthesized Au catalysts to largely improve their selectivity for CO2 reduction by 50%. Our method is expected to illustrate a general guideline to effectively lower the cost of Au catalysts per unit weight of the product while maintaining its high selectivity for CO2 reduction.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".