Dispersion and exfoliation of nanoclays in itaconic acid funcionalized LDPE by ultrasound treatment
Bibliographic record
Abstract
ABSTRACT Polyethylene–montmorillonite nanocomposites were prepared in the melt applying various ultrasonic powers (231, 347, and 462 W) and temperatures (185, 200, and 215 °C) in the presence of itaconic acid. Dispersion–exfoliation of montmorillonite and in situ low density polyethylene functionalization with itaconic acid were carried out by ultrasound assisted extrusion process. Clay dispersion and exfoliation was evaluated by X‐ray diffraction (XRD) and electronic microscopy (STEM). The degree of grafting itaconic acid into low density polyethylene (LDPE) was determined by infrared analysis (FTIR) and titration technics. Ultrasound‐treated nanocomposites showed improved clay dispersion compared with non‐treated ones. Mechanical properties, XRD, and STEM microscopy indicated that the grafting level, clay dispersion, and exfoliation was directly influenced by IA concentration, temperature, and ultrasonic energy. For instance, the itaconic acid grafted into LDPE was improved from 0.13% to 0.71% when ultrasound power was increased and when temperature was reduced. The modulus was enhanced up to 225 MPa when the ultrasound power was increased up to 462 W. The nanocomposites decomposition temperature, determined by thermogravimetric analysis, was increased nearly 20 °C when 1% w/w itaconic acid was used. © 2018 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2018, 135, 46260.
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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".