Structuring Metal–Organic Framework Materials into Hierarchically Porous Composites through One‐Pot Fabrication Strategy
Bibliographic record
Abstract
Abstract Controlled synthesis of metal–organic framework (MOF)‐based materials with multiple levels of porous structures across different length scales is of great interest in various applications but it still remains challenging. Most of the current strategies are time consuming and labor intensive, and not readily scaled‐up. In this work, we introduce a straightforward one‐pot fabrication strategy to prepare a robust and flexible hierarchically macro‐meso‐micro porous HKUST‐1/polyvinylidene fluoride (PVDF) composite through solvent evaporation, in which MOF crystallization and polymer precipitation are combined together. The effect of the MOF precursor and the polymer initial amount on the morphology of the final composite was thoroughly studied. The interaction between the MOF and the polymer during the evaporation process is the key factor, which would limit the mobility of the polymer chains and cause instability in the MOF growth, thus endowing the composite with a hierarchically macro‐meso‐micro porous structure. This “all‐in‐one” porous structure could enhance the mass transport property of molecules within the composite. The obtained HKUST‐1/PVDF composite showed an enhanced CO2 adsorption rate constant of 0.821 min−1 (298 K, 1 bar), which was 3.5 times higher than that of the pristine MOF. In addition, the composite showed an equivalent gas adsorption capacity under all tested pressures and greatly improved water stability.
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) | 0.001 | 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".