Effect of surface modification of bentonite nanoclay with polymers on its stability in an electrolyte solution
Bibliographic record
Abstract
Recently surface modification of clay minerals has become increasingly important for improving the practical applications of clays and clay minerals. In this research work surface modifications of bentonite nanoclay particles were carried out by chemical grafting of different copolymers including poly(1-vinyl pyrrolidone- co -styrene) (PVP), poly(methyl methacrylate- co -methacrylic acid) (PMMA) and poly (acrylamide- co -diallyl dimethyl ammonium chloride) (PDADMAC). The efficiency of the grafting reactions was investigated using FTIR, TGA and XRD methods. It was shown that PVP as well as PDADMAC copolymers had been grafted successfully on the exterior of the nanoclay tactoids surfaces, while most of the PMMA molecules had entered the galleries and grafted on the inner surfaces of the nanoclay. Turbidimetry, zeta potentiometry and dynamic light scattering (DLS), were employed to analyze the dispersion stability of the unmodified and modified nanoclays in an electrolyte solution with high salinity. The turbidimetry results showed that stabilities of unmodified and modified nanoclay particles in the electrolyte solutions decreased as the time passed from their preparation, and after 24 h they settled completely in the solutions. However, rates of the settlement of unmodified and modified particles differed from each other, that of the modified ones being lower. This difference was attributed to the grafted copolymers on the modified particles surfaces. Also, the results of the zeta potentiometry and DLS were in harmony with the turbidimetry observations.
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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".