(Keynote) Detecting the Elusive Adsorbed CO<sub>2</sub> <sup>·-</sup> Intermediate on a Copper Electrode Using Operando Raman Spectroscopy and Pyridine As a Promoter: Implication for CO<sub>2</sub> Electroreduction
Bibliographic record
Abstract
Electrochemical conversion of the abundant CO2 to fuels, polymers, drugs, and other materials using renewable energy is one of the most promising approaches that can help deploy this energy and reduce our dependence on fossil fuels. However, the rational development of a viable CO2 electroreduction system is hampered by a daunting lacunae in the mechanistic knowledge of this reaction. This is especially true with respect to the knowledge derived from experimental (electrochemical and operando spectroscopic) data. The problem stems from the complexity of both CO2 electroreduction per se and the great technical challenges to detect and identify the key reaction intermediates in operandi exactly on the same catalyst that is employed in the preparative electrolysis studies. We address this technical challenge by employing advantages of the electrochemical surface-enhanced Raman scattering (SERS) method, which have been neglected in the earlier attempts to gain mechanistic insights into CO2 electroreduction. These advantages include the outstanding sensitivity of SERS to the species adsorbed on the ‘hot spots’ and the ability of this method to detect these species on real electrocatalysts in real time. Specifically, combining this method with Density Functional Theory (DFT) simulations and employing pyridine as the reaction promoter, we resolve the long-standing controversies about the mechanism of the formate and CO synthesis on a polycrystalline copper electrode in aqueous electrolytes, as well as on the promoting effects of pyridine. Correlating the microscopic results with the electrolysis and electrochemical data, we conclude that the formation of the CO2 • d- intermediate is the rate-determining step of the formate synthesis on Cu, while formate and CO share the same route.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.027 | 0.005 |
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".