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Present conductive silicone rubber with carbon blacks has low compression recovery, and is difficult to make long-term electromagnetic shielding products. In order to solve the problem, special hollow-shell carbon blacks were used and then treated by coupling agent to prepare conductive silicone rubber. The compression set of conductive rubber at 1008C for 48 h was only 8.9%, displaying a extremely good recovery. Importantly, the special rubber can be extruded and then continuously vulcanized. Moreover, its electromagnetic shielding effectiveness reached beyond 20 dB at the frequency of 0.01 MHz-10 GHz. After the thermal oxidative aging at 90 and 1108C for 30 days, carbon blacks occurred to aggregate, which gives rise to a drop of tensile property and slight effects on the modulus and hardness. The compression set also increased, but it still maintained a low level after 30 days. There is only little change for the conductivity of silicone rubber and thus its electromagnetic shielding effectiveness still retained beyond 20 dB, showing a good aging resistance. It is believed that our findings may provide an effective solution to fabricate rubber products to sever the electromagnetic protection. POLYM. COMPOS., 00:000-000, 2018.

Predicting the corrosion loss over time in different environments is challenging because the corrosion process develops over time and is influenced by multiple environmental factors simultaneously. Conventional regression analysis is not applicable because it has several limitations in dealing with multilevel structured data. In this paper, the hierarchical linear modelling method is employed instead. A two-level linear growth model is built to analyse the individual corrosion growth and the corrosion effects of the environment on carbon steel, zinc, and copper. Environmental factors that have important impact on the corrosion process are distinguished. By including the corrosion time and environmental factors as predictor variables, the model predictions are in good agreement with the experiments. The uncertainty of the corrosion data is quantitatively described and the confidence intervals are obtained. Then the long-term corrosion loss is predicted with power-linear corrosion kinetic model and corrosion-induced damage risks are analysed.

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