Bibliographic record
Abstract
Electrochemical carbon dioxide reduction (CO2R) is a promising technology to use renewable electricity to convert CO2 into valuable carbon-based products (1-4). Of metal-based electrocatalysts, silver is one of the best known materials to produce CO in aqueous media, via two proton-coupled electron-transfer processes, with a high selectivity of near 100% Faradaic efficiency (FE) (5). Here, in this study, using density functional theory (DFT), we showed that hydronium (H3O+) is a key intermediate in the first oxygen hydrogenation step to form adsorbed carboxyl (*COOH), and lowers the activation energy barrier for CO formation. Removing the hydronium influence, we found that the activation energy barrier for oxygen hydrogenation and therefore adsorbed carboxyl formation increases significantly, while the activation energy barrier for carbon hydrogenation and consequently adsorbed formate (*HCOO) formation reduces. This mechanism suggests that formate formation pathway at lower concentration of hydronium is more favorable than CO formation pathway. Inspired by these DFT results, we designed experiments at highly concentrated KOH solution, to limit the hydronium availability in the aqueous electrolyte. Using a gas diffusion electrode in flow cell configuration enabled us to utilize a very basic electrolyte by separating it from the CO2 gas stream, and also to run experiments at a high current density of 300 mA/cm2. We found that, the CO2R pathways switches from entirely CO formation under neutral condition, to almost 60% FE for formate formation in 11 M KOH. Different in situ and ex situ materials characterization such as XAS, XPS, SEM and XRD, demonstrated that the electrocatalysts before and after the reactions were identical. In addition, control experiments excluded the applied potential effect, confirming that formate formation was a direct effect of alkaline media and lack of hydronium. Observing high selectivity for formate formation on silver in aqueous media and at high current density has never been reported before. We believe that selectivity shift provides new insights into the role of hydronium on CO2 electroreduction processes and the ability for electrolyte manipulation to directly influence transition state kinetics, altering favored CO2 reaction pathways. C.-T. Dinh et al., CO2 electroreduction to ethylene via hydroxide-mediated copper catalysis at an abrupt interface. Science 360, 783 (2018). T.-T. Zhuang et al., Steering post-C–C coupling selectivity enables high efficiency electroreduction of carbon dioxide to multi-carbon alcohols. Nature Catalysis 1, 421-428 (2018). Z.-Q. Liang et al., Copper-on-nitride enhances the stable electrosynthesis of multi-carbon products from CO2. Nat Commun 9, 3828 (2018). M. G. Kibria et al., A Surface Reconstruction Route to High Productivity and Selectivity in CO2 Electroreduction toward C2+ Hydrocarbons. Advanced Materials 30, 1804867 (2018). C. M. Gabardo et al., Combined high alkalinity and pressurization enable efficient CO2 electroreduction to CO. Energy & Environmental Science, (2018). 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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".