COF‐Based S‐Scheme Heterojunction Photocatalyst
Bibliographic record
Abstract
Semiconductor photocatalysis presents a promising route to convert solar energy into storable fuels and tackle global energy and environmental challenges. However, its efficiency is often hindered by rapid electron-hole recombination. Covalent organic frameworks (COFs)-a class of crystalline, porous organic polymers-offer exceptional potential for photocatalysis owing to their precisely tunable structures and distinctive physicochemical properties, yet their performance remains limited by intrinsic charge recombination. To overcome this limitation, the construction of S-scheme heterojunctions has been proposed as a promising strategy to enhance charge separation while maintaining strong redox capabilities. This review begins by presenting a comprehensive perspective on the development and scientific significance of S-scheme heterojunctions. It then systematically summarizes the design principles and synthetic strategies of COFs, followed by an in-depth discussion of the fabrication methods and principles of COF-based S-scheme heterojunctions. Furthermore, advanced characterization techniques that enable precise elucidation of charge migration pathways within these heterostructures are highlighted. The review also provides a comprehensive overview of recent applications of COF-based S-scheme photocatalysts, including hydrogen evolution, carbon dioxide reduction, environmental remediation, hydrogen peroxide production, and others. Finally, current challenges and future perspectives are discussed to inspire continued innovation in the development of high-performance S-scheme photocatalytic systems.
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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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".