Leveraging Inherent Structure of Tin Oxide for Efficient Carbonaceous Products Electrosynthesis
Bibliographic record
Abstract
Abstract Electrochemical CO 2 reduction reaction (CO 2 RR) holds a great potential for converting CO 2 into valuable carbon‐based chemicals and fuels. A promising strategy for enhancing CO 2 RR performance is the deliberate structural design of electrocatalysts, which can maximize the utilization of inherent structural advantages. In this work, SnO 2 nanocubes (NCs) and nanorods (NRs) are synthesized using a surface energy‐driven growth orientation method, where the stable (110) facet and the highly energetic (001) facet constitute the SnO 2 nanostructures. Leveraging the inherent structural merits of different facets on SnO 2 , theoretical calculations reveal that the (001) facet plays a primary role in inhibiting hydrogen evolution reaction (HER), while both (110) and (001) facets are highly favorable for CO 2 ‐to‐formate conversion under the external bias. As a result, SnO 2 NCs with a higher facet ratio of (001)/(110) achieve nearly 100% selectivity for the formation of carbonaceous products during CO 2 RR. More importantly, a maximum partial current density of about 1 A cm −2 with a formate Faradaic efficiency (FE) of over 90% is achieved in a flow cell, distinguishing it from most of the reported Sn‐based electrocatalysts. These results highlight the strategic advantages of leveraging the inherent structure of nanomaterials for efficient CO 2 RR.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".