Novel Route to Size-Controlled Fe–MIL-88B–NH<sub>2</sub> Metal–Organic Framework Nanocrystals
Bibliographic record
Abstract
A new approach for the synthesis of uniform metal-organic framework (MOF) nanocrystals with controlled sizes and aspect ratios has been developed using simultaneously the non-ionic triblock co-polymer F127 and acetic acid as stabilizing and deprotonating agents, respectively. The alkylene oxide segments of the triblock co-polymer can coordinate with metal ions and stabilize MOF nuclei in the early stage of the formation of MOF nanocrystals. Acetic acid can control the deprotonation of carboxylic linkers during the synthesis and, thus, enables the control of the rate of nucleation, leading to the tailoring of the size and aspect ratio (length/width) of nanocrystals. Fe-MIL-88B-NH(2), as an iron-based MOF crystal, was selected as a typical example to illustrate our approach. The results reveal that this approach is used for not only the synthesis of uniform nanocrystals but also the control of the size and aspect ratio of the materials. The size and aspect ratio of nanocrystals increase with an increase in the concentration of acetic acid in the synthetic mixture. The non-ionic triblock co-polymer F127 and acetic acid can be easily removed from the Fe-MIL-88B-NH(2) nanocrystal products by washing with ethanol, and thus, their amine groups are available for practical applications. The approach is expected to synthesize various nanosized carboxylate-based MOF members, such as MIL-53, MIL-89, MIL-100, and MIL-101.
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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) | 0.001 | 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".