Insights into Electrochemical CO<sub>2</sub> Reduction on SnS<sub>2</sub>: Main Product Switch from Hydrogen to Formate by Pulsed Potential Electrolysis
Bibliographic record
Abstract
Abstract Tin disulfide (SnS 2 ) is a promising candidate for electrosynthesis of CO 2 ‐to‐formate while the low activity and selectivity remain a great challenge. Herein, we report the potentiostatic and pulsed potential CO 2 RR performance of SnS 2 nanosheets (NSs) with tunable S‐vacancy and exposure of Sn‐atoms or S‐atoms prepared controllably by calcination of SnS 2 at different temperatures under the H 2 /Ar atmosphere. The catalytic activity of S‐vacancy SnS 2 (V s ‐SnS 2 ) is improved 1.8 times, but it exhibits an exclusive hydrogen evolution with about 100 % FE under all potentials investigated in the static conditions. The theoretical calculations reveal that the adsorption of *H on the V s ‐SnS 2 surface is energetically more favorable than the carbonaceous intermediates, resulting in active site coverage that hinders the carbon intermediates from being adsorbed. Fortunately, the main product can be switched from hydrogen to formate by applying pulsed potential electrolysis benefiting from in situ formed partially oxidized SnS 2− x with the oxide phase selective to formate and the S‐vacancy to hydrogen. This work highlights not only the V s ‐SnS 2 NSs lead to exclusively H 2 formation, but also provides insights into the systematic design of highly selective CO 2 reduction catalysts reconstructed by pulsed potential electrolysis.
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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".