Decoding the Role of Tin Telluride as Electrochemical CO2 Reduction Catalyst
Bibliographic record
Abstract
Tin-based compounds, particularly SnO2-derived catalysts, are extensively studied for selective electrochemical reduction of CO2 (eCO2RR) to formate. As compared to Sn oxides, chalcogenides such as SnTe are relatively unexplored in the domain of eCO2RR, in spite of having desired electronic properties, often combined with native surface oxide layers. In this work, we report dual catalytic behavior of finely powdered polycrystalline SnTe showing high activity towards CO2 reduction as well as the hydrogen evolution reaction (HER). We show that SnTe exhibits selective eCO2RR to formate with partial faradic current densities of −35 mAcm-2 at −1.1 V vs. RHE in 0.5 M CsHCO3 solution, similar to SnO2. Concurrently, SnTe exhibits high activity towards HER, in contrast to SnO2. Comprehensive potential dependent structural characterizations and SEIRAS measurements suggest that the chemical transformation of SnTe and SnO2 to reduced Sn under high reductive potentials may be the reason for their similar eCO2RR activity. On the other hand, control experiments on elemental Te and SnO2 as well as XPS data point towards the important role of residual tellurium on the surface of the SnTe pre-catalyst to drive the HER. This work underscores the significance of understanding the in-situ transformation of the pre-catalyst to the active species during the eCO2RR to rationalize its activity and product selectivity.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".