Loading across the Periodic Table: Introducing 14 Different Metal Ions To Enhance Metal–Organic Framework Performance
Bibliographic record
Abstract
Loading metal guests within metal–organic frameworks (MOFs) via secondary functional groups is a promising route for introducing or enhancing MOF performance in various applications. In this work, 14 metal ions (Li +, Na +, K +, Mg 2+, Ca 2+, Ba 2+, Zn 2+, Co 2+, Mn 2+, Ag +, Cd 2+, La 3+, In 3+, and Pb 2+ ) have been successfully introduced within the MIL-121 MOF using a cost-efficient route involving free carboxylic groups on the linker. The local and long-range structure of the metal-loaded MOFs is characterized using multinuclear solid-state NMR and X-ray diffraction methods. Li/Mg/Ca-loaded MIL-121 and Ag nanoparticle-loaded MIL-121 exhibit enhanced H 2 and CO 2 adsorption; Ag nanoparticle-loaded MIL-121 also demonstrates remarkable catalytic activity in the reduction of 4-nitrophenol.
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) | 0.000 | 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) | 0.002 | 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".