Acoustomicrofluidic Defect Engineering and Ligand Exchange in ZIF‐8 Metal–Organic Frameworks
Bibliographic record
Abstract
A way through which the properties of metal-organic frameworks (MOFs) can be tuned is by engineering defects into the crystal structure. Given its intrinsic stability and rigidity, however, it is difficult to introduce defects into zeolitic imidazolate frameworks (ZIFs)-and ZIF-8, in particular-without compromising crystal integrity. In this work, it is shown that the acoustic radiation pressure as well as the hydrodynamic stresses arising from the oscillatory flow generated by coupling high frequency (MHz-order) hybrid surface and bulk acoustic waves into a suspension of ZIF-8 crystals in a liquid pressure transmitting medium is capable of driving permanent structural changes in their crystal lattice structure. Over time, the enhancement in the diffusive transport of guest molecules into the material's pores as a consequence is shown to lead to expansion of the pore framework, and subsequently, the creation of dangling-linker and missing-linker defects, therefore offering the possibility of tuning the type and extent of defects engineered into the MOF through the acoustic exposure time. Additionally, the practical utility of the technology is demonstrated for one-pot, simultaneous solvent-assisted ligand exchange under ambient conditions, for sub-micron-dimension ZIF-8 crystals and relatively large ligands-more specifically 2-aminobenzimidazole-without compromising the framework porosity or overall crystal structure.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".