In this work, investigations were made on the mechanical properties, stress-strain behavior during compression, swelling and compression set properties of polysulfide sealants at different carbon black and silicon dioxide loadings, and dynamic mechanical thermal analysis was also presented. The results reveal that carbon black filler indeed has significant effects on reinforcing mechanical properties of polysulfide sealants. Increasing carbon black loading improves the tensile strength of sealants promptly, but compression performance increases slowly. The simultaneous use of carbon black and silicon dioxide filler in polysulfide sealants hardly changes the tensile strength of sealants, whereas the ultimate elongation and compression performance of sealants are enhanced remarkably.

No abstract

In this article, mechanical and compression set properties, swelling property, and stress-strain behavior during compression of polysulfide sealants based on different polysulfide resin were investigated. The results showed that molecular weight and cross-linking agent of liquid polysulfide resin had significant influence on mechanical and compression set properties of the sealants. The sealants based on higher molecular weight polysulfide resin had higher mechanical properties. At the same time, lower cross-linking agent in polysulfide resin produced lower cross-link density and higher swelling property, which resulted in higher compression set value of the sealant. However, when different molecular weight polysulfide resins were used in the sealant simultaneously, the testing results indicated that the compression performance of the sealants was significantly enhanced, while mechanical properties of the sealants kept nearly unchanged.

No abstract

This paper proposes a novel approach for analyzing and predicting electromagnetic compatibility (EMC). The proposed method mainly consists of two parts. First, we separate input mixed signals from an electromagnetic environment by using fastICA, based on which, subsequently, we train a immune evolutionary network classifier (IENC). The classifier then finally could be used to analysis and predict electromagnetic compatibility. Experimental results have demonstrated the validity of the proposed algorithm.