Experimental investigation on the effect of carbon nanotube additive on the field-induced viscoelastic properties of magnetorheological elastomer
Source abstract
Abstract
The additives improve the properties of magnetorheological elastomer by modifying the surface of ferromagnetic filler particles or by varying the properties of a host polymer matrix. In this study, effect of carbon nanotube additive on the viscoelastic properties of magnetorheological elastomer reinforced with optimum quantity of ferromagnetic filler is studied. Room temperature vulcanizing silicone elastomer-based test samples are prepared by mixing the elastomer with the carbon nanotube and carbonyl iron powder blend obtained from ultrasonication. Viscoelastic properties are measured by adopting the dynamic blocked transfer stiffness method. The results revealed that the properties of magnetorheological elastomer vary significantly with the inclusion of carbon nanotube. With the addition of 0.5 wt% carbon nanotube, the zero field dynamic stiffness of magnetorheological elastomer is enhanced by 36.7% and the loss factor is increased by 17.2%. The enhancement in zero field properties led to the least field-induced enhancement for magnetorheological elastomer doped with 0.5 wt% carbon nanotube. A relatively larger flexibility of pure magnetorheological elastomer samples had resulted in the maximum field-induced enhancement of 48.04%. Among the prepared test samples with carbon nanotube addition, the sample loaded with 0.25 wt% carbon nanotube exhibited a pronounced stiffness enhancement and lower loss factor. This substantiated the existence of an optimum limit for carbon nanotube additive. The present study also confirmed the feasibility of developing MRE tailor-made to suit the particular application by selecting a proper composition of matrix, filler and the additives.
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Research context
OpenAlex topics: Vibration Control and Rheological Fluids · Structural Engineering and Vibration Analysis · Seismic Performance and Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
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