Modification of Montmorillonite with Alkyl Silanes and Fluorosurfactant for Clay/fluoroelastomer (FKM) Nanocomposites
Bibliographic record
Abstract
Abstract The main objective of the present work was to functionalize nanoclays with organosilanes and surfactant in order to facilitate the dispersion of the nanofillers in the host fluoroelastomer (FKM) polymer matrix. Better dispersion was achieved by improving interaction between the clay polymer nanocomposite (CPN) constituents. The first part of this study investigated modification of montmorillonite (Mnt) using different saturated and unsaturated alkyl silanes and an alkyl hydrocarbon ammonium quaternary surfactant. Silicon magic angle spinning nuclear magnetic resonance spectroscopy, thermal gravimetric analysis (TGA), elemental analysis, X-ray diffraction (XRD), and Fourier transform infrared spectroscopy were used to characterize the silane-grafted clays. Results indicated that the amount of silane grafted depended on the specific structure of the silane. Silane-grafted Mnt was also modified with ionic surfactants intercalated between the clay layers. A 169% increase in the clay basal spacing (from initial spacing of 10.0 Å to 26.9 Å) was achieved. The second part of the study successfully synthesized FKM nanocomposites containing custom-functionalized Mnt, with the aim of producing reinforced high-performance materials. The effects of clay modification on the morphology and thermal properties of the CPN were studied using XRD, TGA, scanning electron microscopy, and transmission electron microscopy. The CPN made with the modified clay exhibited greater thermal stability than the CPN of the commercially available modified Mnt, with a degradation onset point ~ 40°C higher.
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) | 0.000 | 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".