Plasmonic Photocatalysts for Sunlight‐Driven Reduction of CO<sub>2</sub>: Details, Developments, and Perspectives
Bibliographic record
Abstract
Abstract Plasmonic photocatalysis is among the most efficient processes for the photoreduction of CO 2 into valuable fuels. The formation of localized surface plasmon resonance (LSPR), energy transfer, and surface reaction are the significant steps in this process. LSPR plays an essential role in the performance of plasmonic photocatalysts as it promotes an excellent, light absorption over a broad wavelength range while simultaneously facilitating an efficient energy transfer to semiconductors. The LSPR transfers energy to a semiconductor through various mechanisms, which have both advantages and disadvantages. This work points out four critical features for plasmonic photocatalyst design, that is, plasmonic materials, size, shape of plasmonic nanoparticles (PNPs), and the contact between PNPs and semiconductor. Various developed plasmonic photocatalysts, as well as their photocatalytic performance in CO 2 photoreduction, are reviewed and discussed. Finally, perspectives of advanced architectures and structural engineering for plasmonic photocatalyst design are put forward with high expectations to achieve an efficient CO 2 photoreduction shortly.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".