Surface Chemistry Modulates CO2 Reduction Reaction Intermediates on Silver Nanoparticle Electrocatalysts
Bibliographic record
Abstract
Electrocatalytic reduction of carbon dioxide (CO2R) to fuels and chemicals is a pressing scientific and engineering challenge that is, in part, hampered by a lack of understanding of the surface reaction mechanism, even for relatively simple systems. While many efforts have been dedicated to promoting CO2R on catalytic surfaces by tuning composition, morphology, and defects, the role of the reaction environment around the active site, and how this can be leveraged to modulate CO2R, is less clear. To this end, we focused on a model CO2R catalyst, Ag nanoparticles, and carried out a combined electrocatalytic and operando Raman spectroscopic investigation of CO2R on their surfaces. Bare Ag and chemically modified Ag nanoparticles were investigated to understand how the surface reaction environment dictates intermediate binding and catalytic efficiency en route to CO generation. The results revealed that the primary product on Ag is CO, which is formed through a doubly-bound CObridge configuration. In contrast, electrografted imidazole and polyvinylpyrrolidone (PVP)-coated Ag feature CO in a singly-bound COatop configuration on their surfaces, whereas porous zeolitic-imidazolate framework-coated Ag was observed to bind both CObridge and COatop. Further, another function of the Ag surface modifications is to dictate the type of Ag surface sites which form Ag-C bonds with CO2R intermediates. Through analysis of the of electrochemical and spectroscopic data, we deduce which key aspects of CO2R on Ag surface render a CO2R system efficient and show how surface chemistry dictates diverging CO2R surface reaction mechanisms. The insights gained here are important as they provide the community with a greater understanding of heterogeneous CO2R and can be further translated to a number of catalytic systems.
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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".