Metal–organic frameworks for the generation of reactive oxygen species
Bibliographic record
Abstract
Reactive oxygen species (ROS) are highly reactive molecules derived from oxygen, which are naturally generated and play essential roles in biological processes. At the same time, ROS are the basis of advanced oxidation processes (AOPs), which can be used for multiple applications of industrial interest, including water treatment and organic synthesis. Additionally, anti-cancer therapies that involve the targeted production of ROS in cancerous cells have shown promising results in vitro and in vivo by promoting oxidative stress and, hence, cell death. However, up to this day, the development of catalysts and systems that are, at the same time, easily synthesized, low-cost, nontoxic, and highly effective remains a challenge. With that in mind, metal–organic frameworks (MOFs), a relatively new class of coordination polymers, may display all these characteristics and many others, including tunable structure, extensive porosity, and high surface areas. Because of that, the design and synthesis of MOFs and MOF-based materials for the generation of ROS has garnered attention in recent years. In this review, we summarize recent advances in the development and use of MOFs or MOF-based materials as catalysts in AOPs and biological systems through the generation of ROS, shining light on promising results and 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) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".