Preparation and Characterization of Guar-Montmorillonite Nanocomposites
Bibliographic record
Abstract
Polymer-clay nanocomposites are highly sought-after materials, mainly due to their applicability in a variety of avenues. From the standpoint of the preparation of these nanocomposites, however, organic compatibility with clay and adherence to “green chemistry” concepts and principles can be limiting factors. As such, the objective was to prepare a biopolymer-modified clay nanocomposite using a simple and environmentally friendly method of preparation, whereby pre-treatment of the clay for organic compatibility was bypassed. Novel montmorillonite nanocomposites were prepared using neutral guar gum and cationic guar gum. X-ray diffraction (XRD) and transmission electron microscopy (TEM) confirmed the formation of intercalated structures. A monolayer configuration of cationic guar within the interlayer space was indicated by XRD results, while treatment with neutral guar gum resulted in the observance of both monolayer and bilayer configurations. Additionally, TEM results indicated partial exfoliation. Results attributed from 13C cross polarization/magic angle spinning nuclear magnetic resonance spectroscopy (CP/MAS NMR) of the nanocomposites indicated peaks corresponding to the guar constituent, confirming the adsorption of the biopolymer. Inductively coupled plasma emission spectrometry (ICP-ES) results indicated the exchange of cations present in neutral guar gum with the sodium cations of montmorillonite, in the case of the neutral guar nanocomposites.
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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 | 0.001 | 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".