Hierachical Aerogel-Supported Cu–Sn–O<sub><i>x</i></sub> Solid Solutions for Highly Selective CO<sub>2</sub> Electroreduction and Zn–CO<sub>2</sub> Batteries
Bibliographic record
Abstract
The electrochemical CO 2 reduction reaction (CO 2 RR) into high-value carbon compounds such as CO and HCOOH is a promising strategy for the utilization and conversion of emitted CO 2 . However, the selectivity of the CO 2 RR for HCOOH is typically less than 90% and operates within a narrow voltage range, which limits its practical application. Herein, we propose a novel heterostructural aerogel as a highly efficient electrocatalyst for CO 2 RR to HCOOH. This catalyst consists of Cu–Sn–O x solid solutions embedded in a reduced graphene oxide matrix (Cu–Sn–O x /rGO). The incorporation of Cu 2+ into the SnO 2 matrix enhances HCOOH production by improving the adsorption of the *OCHO intermediate and inhibiting H 2 evolution, as confirmed by in situ measurements and computational studies. As a result, Cu–Sn–O x /rGO achieves a remarkable Faradaic efficiency (FE) of up to 91.4% for HCOOH and maintains high selectivity over a broad operating voltage range (−0.8 to −1.1 V). Additionally, the assembled Zn–CO 2 batteries demonstrated an excellent power density of 1.14 mW/cm 2 and exceptional stability for over 25 h.
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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".