Bibliographic record
Abstract
Photocatalysis is a versatile phenomenon that finds expressions in many applications in the field of energy and environmental remediation, such as hydrogen production, conversion of carbon dioxide into hydrocarbon fuels, pollutant degradation, pathogen disinfection, biomass conversion, organic synthesis, transformation reactions, etc. The effective utilization of such multifaceted phenomenon demands suitable materials in order to conduct the reactions with enhanced quantum efficiencies. The meticulous understanding of the photocatalytic phenomenon reveals that an ideal photocatalyst, for various reasons, should hold appropriate band edge positions, narrow band gap energy, abundant catalytic sites, enhanced charge transfer characteristics, improved surface adsorption, and excellent stability. Of the various materials available, with their own pros and cons, the metal–organic framework compounds (MOFs) play an important role and provide room to suitably engineer the materials for various photocatalytic applications. The unique structure of MOFs involves inorganic cluster junctions, organic linkers, and guests in the pores. Together, these structures provide more features to MOFs and enable them to integrate both heterogeneous and homogeneous catalytic functions. MOFs can be a promising candidate for versatile and simultaneous photocatalytic applications. In this context, this chapter has been constructed to provide insights into the structural features, synthesis, characterizations, mechanisms, and photocatalytic applications of MOFs in the field of environmental remediation. Finally, it concludes by providing the concepts derived from MOFs for the effective utilization of photocatalytic phenomenon and future prospects of MOFs in the field of photocatalytic science and technology.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".