Acidic CO2-to-HCOOH electrolysis with industrial-level current on phase engineered tin sulfide
Bibliographic record
Abstract
Abstract Acidic CO2-to-HCOOH electrolysis represents a sustainable route for value-added CO2 transformations. However, competing hydrogen evolution reaction (HER) in acid remains a great challenge for selective CO2-to-HCOOH production, especially in industrial-level current densities. Main group metal sulfides derived S-doped metals have demonstrated enhanced CO2-to-HCOOH selectivity in alkaline and neutral media by suppressing HER and tuning CO2 reduction intermediates. Yet stabilizing these derived sulfur dopants on metal surfaces at large reductive potentials for industrial-level HCOOH production is still challenging in acidic medium. Herein, we report a phase-engineered tin sulfide pre-catalyst (π-SnS) with uniform rhombic dodecahedron structure that can derive metallic Sn catalyst with stabilized sulfur dopants for selective acidic CO2-to-HCOOH electrolysis at industrial-level current densities. In situ characterizations and theoretical calculations reveal the π-SnS has stronger intrinsic Sn-S binding strength than the conventional phase, facilitating the stabilization of residual sulfur species in the Sn subsurface. These dopants effectively modulate the CO2RR intermediates coverage in acidic medium by enhancing *OCHO intermediate adsorption and weakening *H binding. As a result, the derived catalyst (Sn(S)-H) demonstrates significantly high Faradaic efficiency (92.15 %) and carbon efficiency (36.43 %) to HCOOH at industrial current densities (up to −1 A cm−2) in acidic medium.
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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".