Plasmon-enhanced CO2 electroreduction on copper, silver, and copper-silver nano-catalysts
Bibliographic record
Abstract
CO2 electrochemical reduction (CO2ER) allows the conversion of CO2 into fuels and chemicals. Copper is the only known catalyst that converts CO2 into hydrocarbon products but is hindered poor selectivity and stability. Cu-based bimetallic particles have shown to improve the selectivity and stability of the catalysts. This work reports a novel study in the use of a broad-range light source to induce the plasmonic effect in Cu, Ag and Cu-Ag bimetallic nanoparticle catalysts for CO2ER. Active Cu100-xAgx (x= 0, 50, 60, 75, 85, 100 at. %) catalysts were synthesized using a facile chemical reduction and compared to commercial counterparts. The catalytic activity of the particles was correlated with detailed physicochemical characterizations. The synthesized particles were found to be active catalysts for CO2ER, with improved electro-catalytic activities exhibited by Cu85Ag15, Cu60Ag40 and Cu syntheses in respective order. All nanoparticles demonstrated increases in the catalytic activity ranging between 15-26% under white light illumination, attributed to plasmonic promotion using a broad visible wavelength range cold halogen lamp for the first time on CO2ER.The best plasmonic promotion of 26% was observed in the CuAg commercial alloy. Meanwhile, the best promotion of the synthesized bimetallic particles was of 18% found in the Cu60Ag40 catalyst. Additionally, improved electrochemical and plasmonic stability was observed with the use of the Cu-Ag bimetallic synthesized structures compared to monometallic Cu.
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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) | 0.000 | 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".