Rational Design and Synthesis of SnO<sub><i>x</i></sub> Electrocatalysts with Coralline Structure for Highly Improved Aqueous CO<sub>2</sub> Reduction to Formate
Bibliographic record
Abstract
Abstract Several catalyst materials composed of tin oxide composites (SnOx) with a novel coralline structure are synthesized by using a facile hydrothermal self‐assembly process. The catalysts are then used to prepare a SnOx/GDL (gas diffusion layer) electrode for CO2 electroreduction to formate in 0.5 m KHCO3 aqueous solution. Influential factors, such as hydrothermal synthesis temperature (T)/time (Δt) and the valence state of Sn in the SnOx nanocatalysts, on both catalysts’ morphologies, and Faradaic efficiency for formate production are investigated systematically. By using a SnOx(100–8)/GDL electrode (i.e. T and Δt are 100 °C and 8 h, respectively) as the cathode, the high maximum faradaic efficiency of 87.1 % is achieved at a controlled potential of −1.6 V, which is superior to all the reported SnOx and Sn/SnOx catalysts in the literature. By combining X‐ray photoelectron spectroscopy and X‐ray diffraction analysis, the coralline‐structured SnOx is observed to be composed of SnO and SnO2, where the SnO is covered by a SnO2 film about 1–2 nm thick, which makes a contribution to the catalytically active sites for CO2 electroreduction. This coralline‐structured SnOx exhibits high durability, as evaluated by a stable catalytic current density of approximately 10 mA cm−2 over 20 h of continuous operation. This work highlights the controlling role of the correct morphology and the valence state of tin oxide on formate formation during CO2 reduction in aqueous solution.
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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".