Insights into pore surface modification of mesoporous polymer–silica composites: introduction of reactive amines
Bibliographic record
Abstract
Pore surface engineering of mesoporous materials is fundamental for the development of highly selective sorbents, solid catalysts or drug delivery systems. In the present study, tailored mesoporous amine-functionalized polymer–silica composites are synthesized using a two-step mesopore surface-confined polymerization technique. For this, a functional polymer, polychloromethylstyrene (PCMS), is first introduced as a uniform coating on the mesopore surface of mesoscopically ordered silica, e.g. SBA-15 or KIT-6 materials. In the second step, selected amines, as model functions, are attached to the polymer surface by nucleophilic substitution, generating a variety of nanoporous amino-polymer–silica composites. In particular, it is shown that this approach allows for a tuning of surface concentration of the organic groups either by varying polymer loading or by copolymerization of the CMS monomers with non-reactive monomers (styrene). Moreover, this method is suitable for facile introduction of diverse types of amine groups, e.g. secondary amines, diamines, linear or branched polyamines. The pristine mesoporous silica hosts and the different functional mesoporous polymer–silica composites are characterized in detail by nitrogen physisorption, powder X-ray diffraction, elemental analysis, thermogravimetry–differential thermal analysis coupled with mass spectrometry (TG-DTA/MS), attenuated total reflection-IR spectroscopy (ATR-IR) and scanning electron microscopy. In addition, the obtained functionalized mesoporous composites are proven active as base catalysts in the Knoevenagel condensation. From these investigations, it appeared that the preparation method should be highly flexible and appropriate to enable modulation of location and distribution of various functional groups within mesopores.
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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".