Cleaving Carboxyls: Understanding Thermally Triggered Hierarchical Pores in the Metal–Organic Framework MIL-121
Bibliographic record
Abstract
Carboxylic acid linker ligands are known to form strong metal–carboxylate bonds to afford many different variations of permanently microporous metal–organic frameworks (MOFs). A controlled approach to decarboxylation of the ligands in carboxylate-based MOFs could result in structural modifications, offering scope to improve existing properties or to unlock entirely new properties. In this work, we demonstrate that the microporous MOF MIL-121 is transformed to a hierarchically porous MOF via thermally triggered decarboxylation of its linker. Decarboxylation and the introduction of hierarchical porosity increases the surface area of this material from 13 to 908 m 2 /g and enhances gas adsorption uptake for industrially relevant gases (i.e., CO 2, C 2 H 2, C 2 H 4, and CH 4 ). For example, CO 2 uptake in hierarchically porous MIL-121 is improved 8.5 times over MIL-121, reaching 215.7 cm 3 /g at 195 K and 1 bar; CH 4 uptake is 132.3 cm 3 /g at 298 K and 80 bar in hierarchically porous MIL-121 versus zero in unmodified MIL-121. The approach taken was validated using a related aluminum-based MOF, ISOMIL-53. However, many specifics of the decarboxylation procedure in MOFs have yet to be unraveled and demand prompt examination. Decarboxylation, the formation of heterogeneous hierarchical pores, gas uptakes, and host–guest interactions are comprehensively investigated using variable-temperature multinuclear solid-state NMR spectroscopy, X-ray diffraction, electron microscopy, and gas adsorption; we propose a mechanism for how decarboxylation proceeds and which local structural features are involved. Understanding the complex relationship among the molecular-level MOF structure, thermal stability, and the decarboxylation process is essential to fine-tune MOF porosity, thus offering a systematic approach to the design of hierarchically porous, custom-built MOFs suited for targeted applications.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".