Advancing from MOFs and COFs to Functional Macroscopic Porous Constructs
Bibliographic record
Abstract
Abstract Metal–organic frameworks (MOFs) and covalent‐organic frameworks (COFs) are the highly porous rising stars of reticular chemistry. However, most face challenges such as poor macroscopic structuring capability, inadequate mechanical robustness, and inaccessible porosities for target reactants, which hinder their practical applications. This review explores various strategies to assemble MOFs and COFs into macroscopic 3D‐structured multi‐scale porous structures, such as aerogels, foams, and sponges. The methods discussed include direct mixing, self‐shaping, in situ growth, template‐assisted approaches, and 3D printing. These strategies enable macroscopic MOF or COF porous structures to achieve excellent mechanical strength and tunable porosity from the molecular level and micro‐scale up to the macroscopic level. This structural tunability allows the MOF or COF porous structures to outperform their neat powders by making their micro‐ and meso‐porosities more accessible to target reactants. Such improvements pave the way for the functionality of MOF or COF species at larger scales, addressing urgent societal needs, including environmental remediation, CO 2 capturing, value‐added catalytic reactions, water harvesting, electromagnetic (EM) shielding, and beyond.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".