The construction of conduction pathways is considered to be a crucial issue for the thermal and electric transfer ability improvement of silicon rubber. Fabricating three-dimensional (3D) conductive scaffolds is attractive for rapid heat and electricity conduction in silicon composites. In the present work, carbon nanotubes (CNTs) and conductive sponges (CSs) were chosen to construct a 1D-3D collaborative network in a silicone matrix. Novel silicone rubber composites with preferable thermal and electrical conductivity properties were fabricated.The morphology, thermostability, electroconductibility, and mechanical properties were studied. This kind of silicone-based composite with high thermal and electrical conductivity has broad applications for thermal management in emerging electronic devices.
Barkhausen noise (BN) is electromagnetic pulse sequence that could be used to nondestructively predict the properties of materials such as hardness, residual stress and carbon content. Current BN signal analysis methods fail to describe the highly variated BN signal and achieve high regression accuracy due to the low interpretability of neural network and limited capacity of mathematical regression tools. In this paper, two multi-variable regression tools, named partial Chebyshev polynomial regression (PCPR) and Mutual Information-based Feature Selection with Class-dependent Redundancy and multi-variable Chebyshev polynomials regression (MIFS-CR+MCPR), are employed for the first time to predict the hardness of Cr12MoV steel (i.e. X12m). Combined with Chebyshev polynomials, our regression tools are designed on the basis of cascaded regression and mutual-information-based feature selection. As represented by the experimental results for predicting the hardness of X12m, the proposed method outperforms other comparative methods including neural network and partial linear square regression method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.