Tuning the Composition of Electrodeposited Bimetallic Tin–Lead Catalysts for Enhanced Activity and Durability in Carbon Dioxide Electroreduction to Formate
Bibliographic record
Abstract
Abstract Bimetallic Sn–Pb catalysts with five different Sn/Pb atomic ratios were electrodeposited on Teflonated carbon paper and non‐Teflonated carbon cloth using both fluoroborate‐ and oxide‐containing deposition media to produce catalysts for the electrochemical reduction of CO2 (ERC) to formate (HCOO−). The interaction between catalyst composition, morphology, substrate, and deposition media was investigated by using cyclic voltammetry and constant potential electrolysis at −2.0 V versus Ag/AgCl for 2 h in 0.5 m KHCO3. The catalysts were analyzed before and after electrolysis by using SEM and XRD to determine the mechanisms of Faradaic efficiency loss and degradation. Catalysts that are mainly Sn with 15–35 at % Pb generated Faradaic efficiencies up to 95 % with a stable performance. However, pure Sn catalysts showed high initial stage formate production rates but experienced an extensive (up to 30 %) decrease of the Faradaic efficiency. The XRD results demonstrated the presence of polycrystalline SnO2 after electrolysis using Sn–Pb catalysts with 35 at % Pb and its absence in the case of pure Sn. It is proposed that the presence of Pb (15–35 at %) in mainly Sn catalysts stabilized SnO2, which is responsible for the enhanced Faradaic efficiency and catalytic durability in the ERC.
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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 | 0.001 |
| 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 | 0.000 |
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".