Plasmonic Nanomaterials as Catalysts for CO<sub>2</sub> Electroreduction
Bibliographic record
Abstract
Rising CO 2 levels and their negative impact on the environment and humans have propelled the development of carbon conversion processes such as the electrochemical reduction of CO 2 (CO 2 ER). Some of the key challenges with this process include high overpotentials, product selectivity, and catalyst stability. Recently, plasmon-enhanced electrocatalysis has gained more attention because it can accelerate reaction rates and product selectivity through the use of light and plasmonic nanomaterials. This Review highlights recent advances in plasmon-enhanced CO 2 ER on Au-, Ag-, and Cu-based single-metal catalysts, as well as plasmonic multi-metal catalysts. It covers experimental techniques used to elucidate plasmon-enhanced mechanisms and performance, along with in situ and computational techniques that unravel reaction mechanisms and provide a better understanding of the process. The Review ends with an outlook of this process and ways to improve it and make it more practical and relevant to the industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".