Modulating Electrochemical CO <sub>2</sub> Reduction Pathways via Interfacial Electric Field
Bibliographic record
Abstract
Abstract Copper (Cu) is a versatile electrocatalyst for the carbon dioxide reduction reaction (CO 2 RR), capable of generating various hydrocarbons. While this versatility is advantageous, controlling product selectivity remains a major challenge. Conventional Cu‐based electrodes often favor ethylene production, limiting the selectivity for other products such as methane. Here, the interfacial electric field is systematically engineered to direct the CO 2 RR pathway from ethylene toward methane production. Through extensive analysis, it is demonstrated that combining Cu and indium tin oxide (ITO) creates an interfacial electric field within the Cu/ITO electrode, ideal for altering the CO 2 RR pathway. This facilitates electron transfer from Cu to ITO, inducing a positive charge on the Cu species, which shifts the selectivity from ethylene to methane. While p ‐block elements such as tin (Sn) and indium (In) predominantly yield formate, and Cu is selective toward ethylene, the Cu/ITO catalyst demonstrates a methane production rate that exceeds that of Cu by over 50‐fold. This result highlights the substantial potential of engineering the interfacial electric field to control electrocatalytic reaction pathways. Computational analyses using DFT calculations revealed the significant electronic charge transfer between the Cu surfaces and the In and Sn sites which agrees well with the spectroscopic measurements of interfacial electric field.
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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".