Plasmonic Materials: Opportunities and Challenges on Reticular Chemistry for Photocatalytic Applications
Bibliographic record
Abstract
Abstract Solar‐light harvesting materials currently represent a hot topic in catalysis due to the several applications where they can be used. Among the recent strategies to enhance the photocatalytic performance of semiconductor materials, plasmonic metals are trending. Coupling plasmonic metal nanoparticles with a semiconductor material can give unique synergistic effects and properties. Especially when reticular materials, like metal organic frameworks, are used to generate these plasmonic nanocomposites. Herein, a brief introduction to the localized surface plasmon resonance and reticular materials design and fabrication is given. Also, the advantages of plasmonic with reticular nanostructures are discussed. The following highlights summarize recent advances in sunlight‐driven plasmonic reactions (CO2 photoreduction, water depollution, gas sensing, and optical reactions). Theoretical and experimental approaches are discussed, regarding mechanistic phenomena of nanocomposites with reticular materials and surface plasmon metals. A proper discussion and perspective of the remaining challenges and future opportunities for plasmonic metals with reticular materials in the field of photocatalysis is given in the overview and conclusion.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".