Crystal Growth of Metal–Organic Framework-5 around Cellulose-Based Fibers Having a Necklace Morphology
Bibliographic record
Abstract
Herein, metal-organic framework (MOF)-5 crystals were grown on cellulose-based substrates including paper and cotton. Dopamine was used as a surface modification agent to improve the compatibility between MOF-5 crystals and the used substrates. The formed polydopamine film promoted the growth of MOF-5 crystals, which were bonded to the substrates. Besides dopamine, the structure of the substrate also played a major role in the crystal growth. In the case of paper, which had a structure with fibers closely packed to each other, MOF-5 crystals grew only on the surface of fibers (one side) and could easily fall off. Unlike paper, the cotton bulb had a looser structure and MOF-5 crystals grew around the fibers, forming a stable "necklace" morphology. The effects of dopamine modification on the crystal growth and the formation of "necklace" morphology were investigated using scanning electron microscopy analysis. The crystalline structure of MOF-5 was confirmed using X-ray diffraction. To determine how firmly crystals were attached to the cotton fibers, the substrates were exposed to a constant and strong air flow. It was found that the dopamine-modified cotton increased the strength of MOF-5 crystals attached to fibers. This work demonstrated the firm attachment of MOF-5 crystals onto the substrate, facilitating various potential applications.
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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".