Recent Advances in the Development of Metal–Organic Frameworks-based Photocatalysts for Water Splitting and CO2 Reduction
Bibliographic record
Abstract
The rapid increase in the worldwide population and agro-industrial activities have led to substantial environmental pollution and energy crises in recent decades. The scientific community has given dramatic attention to developing green technologies for production processing and environmental remediation by using natural resources to solve such issues and to provide a better future for our planet. Photocatalysis technologies have been proven to be green alternatives for many applications, including environmental remediation and energy production. Indeed, the engineering of photocatalytic materials with enhanced ability has received the most attention from the scientific community. In recent years, many efforts have been made to develop and modify novel materials based on metal–organic frameworks (MOFs), having excellent stability, high porosity and light absorption, as ideal materials for adsorption, catalysis, and photocatalytic processes. This chapter summarizes and critically discusses approaches towards modifying MOFs-based photocatalysts for enhanced photocatalytic activities such as coupling with inorganic semiconductors, carbon materials, and dye-photosensitizers. Recent advances in using MOFs-based photocatalysts for CO2 reduction into different valuable products and H2 generation through photocatalytic and electrophotocatalytic methods are reviewed.
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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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.006 |
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".