Influence of carbon nanofiber functionalization and compatibilizer on the physical properties of carbon nanofiber reinforced polypropylene nanocomposites
Bibliographic record
Abstract
The effect of acrylic acid (AA) and amine alcohol (DMAE) functionalization via plasma of carbon nanofibers (CNF) on physical and mechanical properties of polypropylene‐CNF nanocomposites prepared by melt mixing was studied. The behavior of these functionalized CNF via plasma was compared with a CNF functionalized by oxidation with a mixture of sulphuric acid (H2SO4) and nitric acid (HNO3). Two different types of compatibilizers were used: an amine alcohol modified PP (PPgDMAE) and a maleic anhydride grafted PP (PPgMA). The CNF functionalization was evidenced by Raman spectroscopy, comparing the ratio of peaks at 1371 and at 1590 cm−1. Dispersion of the CNF was assessed using scanning microscopy, and the effect of the type of CNF functionalization on the dispersion was evidenced. A noticeable increase in thermal stability, mechanical and electrical properties and crystallization rate were observed. For example, the storage modulus when using PPgDMAE and AA functionalized CNF increased up to 48% over the control. The mechanical properties of flexural and impact strength showed an effective load transfer when using this CNF and compatibilizer system, which was attributed to the better functionalized CNF dispersion and the strong interactions between this CNF and the polymer matrix. POLYM. COMPOS., 39:3575–3585, 2018. © 2017 Society of Plastics Engineers
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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".