Tuning the Composition of Bimetallic Electrodeposited Sn-Pb Catalysts for Enhanced Activity and Durability in CO<sub>2</sub> Electroreduction to Formate
Bibliographic record
Abstract
Efficient and durable catalysts are needed to convert CO2 to value added products. In recent years, extensive research has been done on tin (Sn) because of its high faradaic efficiency for producing formate (HCOO-) from CO2 [1–5] and lead (Pb) because of its high hydrogen evolution (HER) overpotential [4, 6]. In our recent study [7], bimetallic Sn-Pb catalysts with five different Sn/Pb atomic ratios were electrodeposited on Teflonated carbon paper and non-Teflonated carbon cloth using both metal fluoroborate- and metal oxide-containing deposition media to produce catalysts for electrochemical reduction of CO2 (ERC) to HCOO-. The interaction between catalyst composition, morphology, substrate and deposition media was investigated by cyclic voltammetry followed by chronoamperometry at -2.0 V vs. Ag/AgCl for 2 h in 0.5 M KHCO3. Sn majority catalysts with 15 to 35 atomic % Pb generated faradaic efficiencies up to 95% with stable performance. Pure Sn catalysts on the other hand, in spite of high initial stage formate production rates, experienced extensive (up to 30%) decrease of the faradaic efficiency. The decrease in faradaic efficiency is most likely due to the reduction of the SnO2 layer to metallic Sn0 during formate production. This newly formed Sn0 surface is no longer active for CO2 reduction to formate but instead preferentially produces parasitic H2. XRD results demonstrated the presence of polycrystalline SnO2 after electrolysis using Sn-Pb catalysts with 35 atomic % Pb and its absence in case of pure Sn. It is proposed that the presence of Pb (15 to 35 at %) in Sn majority catalysts stabilized SnO2, which is responsible for the enhanced faradaic efficiency and catalytic durability in ERC. Our results point to a promising strategy for increasing the durability of Sn based catalyst materials for ERC. Oloman C, Li H (2008) Electrochemical Processing of Carbon Dioxide. ChemSusChem 1:385–391. doi: 10.1002/cssc.200800015 Li H, Oloman C (2005) The Electro-Reduction of Carbon Dioxide in a Continuous Reactor. J Appl Electrochem 35:955–965. doi: 10.1007/s10800-005-7173-4 Bumroongsakulsawat P, Kelsall GH (2015) Tinned graphite felt cathodes for scale-up of electrochemical reduction of aqueous CO2. Electrochimica Acta 159:242–251. doi: 10.1016/j.electacta.2015.01.209 Alvarez-Guerra M, Del Castillo A, Irabien A (2014) Continuous electrochemical reduction of carbon dioxide into formate using a tin cathode: Comparison with lead cathode. Chem Eng Res Des 92:692–701. doi: 10.1016/j.cherd.2013.11.002 Chen Y, Kanan MW (2012) Tin Oxide Dependence of the CO2 Reduction Efficiency on Tin Electrodes and Enhanced Activity for Tin/Tin Oxide Thin-Film Catalysts. J Am Chem Soc 134:1986–1989. doi: 10.1021/ja2108799 Innocent B, Liaigre D, Pasquier D, Ropital F, Léger J-M, Kokoh KB (2009) Electro-reduction of carbon dioxide to formate on lead electrode in aqueous medium. J Appl Electrochem 39:227–232. doi: 10.1007/s10800-008-9658-4 Gyenge EL, Moore CE (2017) Tuning the Composition of Bimetallic Electrodeposited Sn-Pb Catalysts for Enhanced Activity and Durability in CO2 Electroreduction to Formate. ChemSusChem 17:3512-3519 . doi: 10.1002/cssc.201700761 Figure 1. Cumulative formate production faradaic efficiency (FE) (a, c) and total moles of formate synthesized (b, d) for catalysts produced by electrodeposition using the fluoroborate bath. Chronoamperometry at -2.0 V vs Ag/AgCl in 0.5 M KHCO3 at 293 K for 2 hours. Substrates: (a, b) teflonated carbon paper and (c, d) carbon cloth. Figure 1
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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 | 0.001 |
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".