Rare earth element‐modified MOF materials: synthesis and photocatalytic applications in environmental remediation
Bibliographic record
Abstract
Abstract Metal–organic framework‐like materials (MOFs) have been developed in the fields of photocatalysis for their excellent optical properties and physicochemical properties, including environmental remediation, CO 2 photoreduction, water splitting, and so on. With their important roles in various fields, rare earth elements have received growing interests from scientists. Modifying MOFs with rare earth elements for modification allows broadening the absorption spectrum, while the active electrons on their empty 4f orbitals can act as traps to capture photoexcited carriers to inhibit the recombination of electron–hole pairs, thus promoting photocatalytic activity. Therefore, rare earth elements modified MOFs provide an attractive way to achieve their high value utilization. In this mini‐review, the synthesis of rare earth element‐modified MOFs photocatalysts and corresponding applications in the removal of antibiotics, CO 2 reduction, and hydrogen production are constructively summarized and discussed. Finally, the latest advancements and current difficulties of these materials as well as the application prospects are also provided.
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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) | 0.001 | 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".