Postsynthetic Modification of Core–Shell Magnetic Covalent Organic Frameworks for the Selective Removal of Mercury
Bibliographic record
Abstract
Core–shell magnetic covalent organic framework (COF) materials were prepared, followed by shell material functionalization with different organic ligands, including thiosemicarbazide, through a postsynthetic modification approach. The structures of the prepared samples were characterized with various techniques, including powder X-ray diffraction (PXRD), Brunauer–Emmett–Teller (BET) method, thermogravimetric analysis (TGA), photoinduced force microscopy (PiFM), transmission electron microscopy (TEM), scanning electron microscopy (SEM), X-ray photoelectron spectroscopy (XPS), and solid 13 C NMR. PXRD and BET studies revealed that the crystalline and porous nature of the functionalized COFs was well maintained after three steps of postsynthetic modification. On the other hand, solid 13 C NMR, TGA, and PiFM analyses confirmed the successful functionalization of COF materials with good covalent linkage connectivity. The use of the resulting functionalized magnetic COF for selective and ultrafast adsorption of Hg(II) has been investigated. The observations displayed rapid kinetics with adsorption dynamics conforming to the quasi-second-order kinetic model and the Langmuir adsorption model. Furthermore, this prepared crystalline magnetic material demonstrated a high Langmuir Hg(II) uptake capacity, reaching equilibrium in only 5 min. Thermodynamic calculations proved that the adsorption process is endothermic and spontaneous.
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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".