journal article·2023-05

The Roles of Filler Amount and Particle Geometry on the Mechanical, Thermal, and Tribological Performance of Polyamide 6 Containing Silicon-Based Nano-Additives

Alperen Kaplan, Aysegul Erdem, Cagrialp Arslan, Soner Savas, Umit Tayfun, Mehmet Dogan

Silicon· Volume 15· Issue 7
Publisher page

Abstract

The development of polymeric nanocomposites provides a practical strategy to achieve weight-reduction purposes and enhanced performance in structural applications. Polyamide 6 (PA6) has high potential in various applications where high mechanical properties are desired. In this study, four different silicon-containing nano-additives with various geometries, namely nanoclay (NC), halloysite nanotube (H), dodecaphenyl polyhedral oligomeric silsesquioxane (DP-POSS), and silica nanoparticles (NS), are compared based mainly on their reinforcing activities. The amount and geometry of reinforcers are examined through mechanical, thermal, thermo-mechanical, tribological, and morphological analysis. According to test results, different nanofillers show the best result in different behaviors of composites. The highest tensile and flexural strength are observed with the use of NC. Tribological test results implied that the wear resistance of composites depends on the loading level of nano-additives and the highest abrasion resistance is observed for halloysite-filled composites. The involvement of Si-containing nano-additives in PA6 composites confers thermal and thermo-mechanical performance as their filling amounts are kept at low levels. Overall performances of nanocomposites were improved with the inclusion of nano-additives in the order of sheet-like (2D) > tubular (1D) > irregular > spherical (0-D) structural geometries according to investigated results.
Source: openaire · Quality: candidate unverified · Source record ↗

Research context

OpenAlex topics: Tribology and Wear Analysis · Polymer Nanocomposites and Properties · biodegradable polymer synthesis and properties

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

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