Pulse Electrodeposition of Cu on Porous Ag Framework for Electrochemical CO<sub>2</sub> Conversion to Ethanol
Bibliographic record
Abstract
Electrochemical conversion of carbon dioxide (CO2) gas is a simple and cost-benefit method to produce economically valuable materials such as carbon monoxide (CO), multi-carbon (C2+) oxygenate, and hydrocarbons. In particular, ethanol (EtOH) production is attractive to be applied for conventional transport fuels. However, there has been a lack of techniques to yield predominant EtOH from CO2. Copper (Cu), as the well-known and exclusive C2+ catalyst, thermodynamically prefers the production of (C2H4) compared to EtOH. Previous studies suggested that increased concentration of carbon monoxide (CO) as the intermediate of CO2 ameliorated the EtOH selectivity.1,2 Because the solubility of CO is very little in an aqueous electrolyte solution, surface diffusion of CO has been considered to enhance the local CO concentration. It suggests that catalyst interface is crucial to increasing EtOH yield, while little knowledge was developed. We prepared porous Ag inverse opal (AgIO) frameworks with a uniform pore size of ~ 600 nm. The CO2 gas was electrochemically reduced to CO with > 90% selectivity at -1.05 V vs. RHE. In addition, the CO settled in the pore, providing an opportunity for sequential electrochemical/chemical reactions. We deposited the ultra-thin Cu layer (2~10 nm) upon the Ag surface through the pulse electrodeposition technique (PED). This catalyst, indicated as PEDCu/AgIOs, performed ~33 % Faradaic efficiency (FE) of EtOH at -1.05 V vs. RHE. Moreover, PEDCu/AgIOs showed a high oxygenates ratio relative to hydrocarbons (FEoxygenate/FEC2H4) of 2.28, while H2 evolution was significantly suppressed. These results suggested that as-prepared thin Cu film was partially segregated, and the Ag surface was exposed, forming the mixed Ag and Cu interfaces in the given electrochemical condition. This hypothesis was confirmed by negligible EtOH from the Ag-free CuIOs structure. More importantly, the resulting EtOH selectivity outperformed Cu nanoparticles (~7 nm diameter) dispersed on AgIOs (7.45 % FE) and aggregated Cu nanostructures on AgIOs (17.65 % FE) prepared by the constant current mode of electrodeposition. We, therefore, anticipated that CO spillover was promoted by the remaining and ultra-thin Cu layer. Further experiments were conducted by controlling of the thickness, pore size, and interpore size of AgIO frameworks to understand their roles. PEDCu/AgIOs with larger AgIOs thickness and smaller pore size showed higher EtOH selectivity, while the interpore size did not significantly affect the product selectivity. In the presentation, I will discuss key parameters to determine EtOH selectivity and the presumable pathway of EtOH production in details. References Ting, L. R. L.; Piqué, O.; Lim, S. Y.; Tanhaei, M.; Calle-Vallejo, F.; Yeo, B. S., Enhancing CO2 Electroreduction to Ethanol on Copper–Silver Composites by Opening an Alternative Catalytic Pathway. ACS Catal. 2020, 10 (7), 4059-4069. Gurudayal; Perone, D.; Malani, S.; Lum, Y.; Haussener, S.; Ager, J. W., Sequential Cascade Electrocatalytic Conversion of Carbon Dioxide to C-C Coupled Products. ACS Appl. Energy Mater. 2019, 2 (6), 4551-4559.
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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".