Influence of functionalized polypropylene on polypropylene/graphene oxide nanocomposite properties
Why this work is in the frame
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Bibliographic record
Abstract
Graphene oxide (GO) derived from oxidation of natural graphite contains many active groups that can interact with a great variety of polar moieties. In this work, polypropylene (PP)/graphene oxide nanocomposites using polypropylene (PP) grafted with amine‐alcohol (PPgDMAE) as compatibilizer were prepared by two different methods. Maleic anhydride grafted PP (PPgMA) was reacted with 2‐[2‐(dimethylamine)‐ethoxy] ethanol (DMAE) in the melt for forming amine‐alcohol functionalized polypropylene (PPgDMAE). Nanocomposites were prepared by two methods. In one method, PP/GO nanocomposite was prepared by direct melt mixing in an internal batch mixer using PPgDMAE as compatibilizer. In another method, a previous mixing of PPgDMAE with GO in hot Xilene was done and then, once the solvent was evaporated, it was incorporated into PP by melt‐mixing. The microstructure and interface enhancement of the prepared composites were analyzed by Fourier transform infrared spectroscopy (FTIR), Raman, X‐ray difraction (DRX) contact angle, scanning and transmission electron microscopy (STEM), mechanical, thermal, and electrical properties measurements. Fourier transform infrared spectroscopy (FTIR) revealed the interaction between GO and PPgDMAE. The loading of GO conducted to enhance the composite mechanical properties attributed to the strong interfacial interactions between GO and PPgDMAE. A significant improvement in mechanical thermal stability and electrical properties was observed when nanocomposites were prepared by the solution blending method compared with melt mixing method. This work suggests a potential application of GO in preparation of high performance PP composites. POLYM. COMPOS., 39:1361–1369, 2018. © 2016 Society of Plastics Engineers
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it