Tuning the Photophysical and Photochemical Properties of Rare-Earth Cluster-Based Metal–Organic Frameworks
Bibliographic record
Abstract
The design and synthesis of luminescent and photoactive metal–organic frameworks (MOFs) are of interest from both a fundamental and application standpoint. Luminescent and photoactive MOFs can be designed to have photophysical properties similar to those of other materials, with the added benefit of possessing a large surface area and high porosity. The incorporation of lanthanoids within cluster-based MOF metal nodes coupled with the strategic utilization of conjugated organic linkers allows for the design of materials with unique and highly tunable photophysical and photochemical properties. This Spotlight on Applications highlights our efforts in the development of various luminescent and photochemically active rare-earth (RE) cluster-based MOFs as well as the potential applications of these materials. The interplay between lanthanoid elements and organic linkers in MOFs is crucial toward the design and synthesis of RE-MOFs with tailored photophysical and photochemical properties. The paper focuses on methods for tuning the luminescent properties of RE-MOFs via the antenna effect, resulting in either metal-based, linker-based, or dual metal- and linker-based luminescence. Furthermore, strategies for producing singlet oxygen by the incorporation of photosensitizers in RE-MOFs are discussed. Through this work, we aim to shine light on the diversity of the structure, function, and potential applications of RE-MOFs.
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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".