Electrochemical, Scanning Electrochemical Microscopic, and <i>In</i> <i>Situ</i> Electrochemical Fourier Transform Infrared Studies of CO<sub>2</sub> Reduction at Porous Copper Surfaces
Bibliographic record
Abstract
There is significant interest in the design of high-performance electrocatalysts for efficient electrochemical reduction of CO 2 to address the pressing environmental issue and climate change. Herein, a novel copper–aluminum nanostructured catalyst is fabricated via an alloying/dealloying technique. The effect of the initial alloy’s elemental composition and subsequent dealloying, via HCl acid treatments, on the stability and activity of the catalyst for electrochemical CO 2 reduction is studied. The optimized porous catalyst shows high catalytic activity for the electrochemical CO 2 reduction reaction (CO 2 RR) with current efficiencies achieving greater than 81%. Gas and liquid product analysis confirms the formation of CO, H 2, and HCOO – . Scanning electrochemical microscopy was employed to monitor the activity of the catalyst and the CO 2 RR products. In situ electrochemical FTIR spectroscopic studies revealed the first CO 2 RR intermediate was carbon-bound to the acid-treated 50:50 Cu/Al (at. %) alloy surface in a monodentate orientation. The synthetic approach reported in the present study leads to a new promising electrocatalyst with superior catalytic activity and high efficiencies for the effective electrochemical reduction of CO 2 to valuable products.
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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".