Modulating Activity and Selectivity of CO<sub>2</sub> Electroreductions at Au-Water Interfaces via Engineering Local Cation Condition
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The mechanistic understanding of the CO 2 reduction reaction (CO 2 RR) under electrochemical conditions is crucial for optimizing the overall catalytic performance. While electrolyte ions have received considerable attention, it remains unclear how the condition of interfacial cations modulates the CO 2 RR and the competitive hydrogen evolution reaction (HER) at the electrode–electrolyte interfaces. Herein, we explore the CO 2 activation and Volmer step representing the critical first electron transfer during the CO 2 RR and HER, respectively. This investigation involves manipulating the cation identity (K +, Li +, and H + ) and concentration at Au–water interfaces, which is carried out via the slow-growth sampling approach integrated with ab initio molecular dynamics simulations. Our results demonstrate that the high local alkali metal cation (AM + ) concentration facilitates the CO 2 RR following the order of 2K + > 1K + > 2Li + > 1Li + > 0AM +, and the highly promoted CO 2 activation kinetics originate from the short-range coordination between alkali metal cations and reaction intermediates. However, the interfacial HER behaves very differently with the kinetic order of 1Li + > 0AM + > 1K + > 2Li + > 2K +, closely related to the interfacial water structures, which are affected by both cation identity and local concentrations. Overall, the activity and selectivity of the CO 2 RR at the Au–water interface can be enhanced by increasing the local cation concentration (K + > Li + ). These findings highlight the critical roles of alkali metal cations and reaction microenvironments in modulating interfacial reaction kinetics.
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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) | 0.002 | 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".