A Rational Design of Cu<sub>2</sub>O−SnO<sub>2</sub> Core‐Shell Catalyst for Highly Selective CO<sub>2</sub>‐to‐CO Conversion
Bibliographic record
Abstract
Abstract The electrochemical reduction of CO2 (CO2RR) is a versatile method that is capable of simultaneously reduce CO2 emission and produce valuable fuels and chemicals. However, its application is hindered by the lack of cost‐effective catalysts and significant overpotential requirement. In this work, we report a low‐cost and surfactant/capping agent free method to synthesize cubic Cu2O−SnO2 core‐shell electrocatalyst, whose thickness can be easily controlled by the content of tin precursor. The optimized Cu2O−SnO2 catalyst with a 5 nm‐thick shell achieved over 90 % faradaic efficiency towards CO at a low overpotential of 390 mV, which is comparable to some of the noble metal catalysts. The catalyst also exhibited good stability over 18 hours of test at −0.6 V vs. RHE in 0.5 M KHCO3 electrolyte. This work provides a widely applicable strategy for developing a low‐cost electrocatalyst for CO2 conversion.
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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) | 0.000 | 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 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".