Strategies for the Construction of Functional Materials Utilizing Presynthesized Metal‐Organic Cages (MOCs)
Why this work is in the frame
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Bibliographic record
Abstract
Metal-organic cages (MOCs) that assemble from metal ions or metal clusters and organic ligands have attracted the interest of the scientific community because of their various functional coordination cavities. Unlike metal-organic frameworks (MOFs) with infinite frameworks, MOCs have discrete structures, making them soluble and stable in certain solvents and facilitating their application as starting reagents in the further construction of single components or composite materials. In recent years, increasing progress has been made in this field. In this review, we introduce these works from the perspective of design strategies, and focus on how presynthesized MOCs can be used to construct functional materials. Finally, we discuss the challenges and development prospects in this field.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.039 | 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 it