4.6 Article

Improved thermal conductivity of ceramic filler-filled polyamide composites by using PA6/PA66 1:1 blend as matrix

Journal

JOURNAL OF APPLIED POLYMER SCIENCE
Volume 134, Issue 40, Pages -

Publisher

WILEY
DOI: 10.1002/app.45371

Keywords

blends; composites; crystallization; polyamides; thermal properties

Ask authors/readers for more resources

Polyamide-type composites with improved thermal conductivity are prepared by using polyamide 6(PA6)/polyamide 6,6 (PA66) 1:1 blend as the matrix and aluminum nitride (AlN) as the filler through melt compounding. Field emission scanning electron microscopy coupled with energy dispersive spectrometry (EDS) mapping of Al is used to investigate distribution of AlN. Differential scanning calorimeter is used to investigate the crystallization behavior of the composites. The thermal conductivity of PA6/PA66/AlN composite with 50 wt % AlN is 1.5 Wm(-1)K(-1), 88% enhancement compared to those of single polymer based PA6/AlN or PA66/AlN composites. The reason for the improved thermal conductivity is the increased effective volume concentration of AlN in one (probably PA66) phase. The experimental data are fitted into Bruggeman and Agari-Uno model. Composites with similar thermal conductivity are also prepared using silicon carbide as the filler instead of AlN, showing that using PA6/PA66 1:1 blend as the matrix is a universal method to prepare thermally conductive composites with less filler loading. (c) 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017, 134, 45371.

Authors

I am an author on this paper
Click your name to claim this paper and add it to your profile.

Reviews

Primary Rating

4.6
Not enough ratings

Secondary Ratings

Novelty
-
Significance
-
Scientific rigor
-
Rate this paper

Recommended

Article Statistics & Probability

Double-Matched Matrix Decomposition for Multi-View Data

Dongbang Yuan, Irina Gaynanova

Summary: This study investigates the problem of extracting joint and individual signals from multi-view data, and proposes a double-matched matrix decomposition method. The proposed method allows simultaneous extraction of joint and individual signals across subjects and miRNAs, and demonstrates superior signal estimation performance compared to single-matching methods.

JOURNAL OF COMPUTATIONAL AND GRAPHICAL STATISTICS (2022)

No Data Available