Controlled Growth of Highly Defected Zirconium–Metal–Organic Frameworks via a Reaction–Diffusion System for Water Remediation
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The relentless growth of metal–organic framework (MOF) chemistry is paralleled by the persistent urge to control the MOFs physical and chemical properties. While this control is mostly achieved by solvothermal syntheses, room temperature procedures stand out as more convenient and sustainable pathways for the production of MOF materials. Herein, a novel approach to control the crystal size and defect numbers of a dihydroxy-functionalized zirconium-based metal–organic framework (UiO-66(OH) 2 ) at room temperature is reported. Through a reaction–diffusion method in a 1D system, zirconium salt was diffused into an agar gel matrix containing the organic linker to form nanocrystals of UiO-66(OH) 2 with tailored structural features that include crystal size distribution, surface area, and defect number. By variation of the synthesis parameters of the system, hierarchical MOF nanocrystals with an average size ranging from 30 nm up to 270 nm and surface areas between 201 and 500 m 2 g –1 were obtained in a one-pot synthetic route. To stress the importance of crystal size, morphology, and structural defects on the adsorption properties of UiO-66(OH) 2, the adsorption capacity of the MOF toward methylene blue dye was tested with the largest and most defected crystals achieving the best performance of 202 mg/g. The distinctive structural characteristics including the hierarchical micromesoporous frameworks, the nanosized particles, and the highly defective crystals obtained by our synthesis procedure are deemed challenging through the conventional synthesis methods. This work paves the way for engineering MOF crystals with tunable physical and chemical properties, using a green synthesis procedure, for their advantageous use in many desirable applications.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.002 | 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 teacher head, 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".