Kaolinite–poly(methacrylamide) intercalated nanocomposite via in situ polymerization
Bibliographic record
Abstract
The preparation and characterization of a kaolinite–methacrylamide intercalation compound was achieved by a guest-displacement method, the direct treatment of a dimethylsulfoxide (DMSO) pre-intercalate with an aqueous solution of methacrylamide under mild conditions, giving intercalation ratios up to 92% and an expansion of 0.53 nm of the interlayer space. The DMSO molecules were fully displaced from the interlayer space by methacrylamide. Only partial intercalation could be obtained with N-methylformamide (NMF) pre-intercalate. After intercalation, the monomer was thermally polymerized at 100 or 150 °C with various heating times. XRD, FTIR, 13C CP/MAS NMR, and thermogravimetric analysis confirmed that polymerization was partially achieved in the interlayer spaces, while the layered structure of kaolinite was maintained, constraining the polymer in an interlayer space of 0.57 nm in the c-direction. In good agreement with the TG data, elemental analysis gave a stoichiometry of Al2Si2O5(OH)4·(C4H7NO)0.52 for the thermally treated methacrylamide intercalate, corresponding to a ratio of one methacrylamide unit per two kaolinite structural units. The thermal stability of the methacrylamide intercalate was increased after thermal treatment and polymerization.Key words: kaolinite, intercalation, nanocomposites, aluminosilicates, layered materials, interlayer polymerization, poly(methacrylamide).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".