Enhanced photocatalysis of metal/covalent organic frameworks by plasmonic nanoparticles and homo/hetero-junctions
Bibliographic record
Abstract
Metal-organic frameworks (MOFs) and covalent organic frameworks (COFs) have garnered attention in photocatalysis due to their unique features including extensive surface area, adjustable pores, and the ability to incorporate various functional groups. However, challenges such as limited visible light absorption and rapid electron-hole recombination often hinder their photocatalytic efficiency. Recent developments have introduced plasmonic nanoparticles (NPs) and junctions to enhance the photocatalytic performance of MOFs/COFs. This paper provides a comprehensive review of recent advancements in MOF/COF-based photocatalysts improved by integration of plasmonic NPs and junctions. We begin by examining the utilization of plasmonic NPs, known for absorbing longer-wavelength light compared to typical MOFs/COFs. These NPs exhibit localized surface plasmon resonance (LSPR) when excited, effectively enhancing the photocatalytic performance of MOFs/COFs. Moreover, we discuss the role of homo/hetero-junctions in facilitating charge separation, further boosting the photocatalytic performance of MOFs/COFs. The mechanisms behind the improved photocatalytic performance of these composites are discussed, along with an assessment of challenges and opportunities in the field, guiding future research directions.
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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".