PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
April 29, 2024IEEE Transactions on Dielectrics and Electrical Insulation7 citations

The Influence of ATH Particle Size on HTV Silicone Rubber Used for Outdoor Insulation

View Full Paper
RSRuiqi ShangYRYuheng RenLWLu Wen

Key Points

Key points are not available for this paper at this time.

Abstract

Aluminum trihydrate (ATH) is often used in high-temperature vulcanized (HTV) silicone rubber insulators to improve flame retardancy. In addition, to guarantee excellent mechanical properties, the ATH particles chosen are usually very fine with a diameter of around 1 μm. Nevertheless, the high specific surface area increases the filler-matrix interactions and hydroxyl groups, negatively affecting the long-term performance of HTV silicone rubber. To enhance the anti-aging properties, this study investigates the method of blending two ATH fillers with different particle sizes and examines its impact on the comprehensive properties of composites, particularly the dielectric behavior. The results show that ATH mixtures with appropriate proportions of fine and coarse particles can mitigate interfacial effects and dielectric losses without compromising mechanical properties. Besides, this method is more effective in hygrothermal aging tests because of the improvement of the ATH/matrix interface. Notably, coarse particles can also enhance flame retardancy due to improved heat absorption and retention. The results of this work prove that the blending of two ATH particle sizes can enhance the dielectric properties, thermal stability, and hygrothermal aging resistance. The optimal content of fine and coarse particles is 86% and 14% for the HTV silicone rubber investigated in this paper. This work also provides an effective approach for enhancing HTV silicone rubber performance.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Shang et al. (2024) studied this question.

synapsesocial.com/papers/68e6d064b6db64358764e5b1https://doi.org/10.1109/tdei.2024.3394829
Ask AI
Helpful
Bookmark
Share
View Full Paper