We present a thin and flexible multilayer solution for effective absorption of electromagnetic waves from 8 to 40 GHz and probably higher. Submillimetric conductive and dielectric layers based on polymer and carbon nanotubes are successively stacked. A smart gradient-periodic arrangement results in a very high absorption index despite overall millimetric thickness of the system.
The dipolar interaction field in arrays of nickel nanotubes has been investigated on the basis of expressions derived from the effective demagnetizing field of the assembly as well as magnetometry measurements. The model incorporates explicitly the wall thickness and aspect ratio, as well as the spatial order of the nanotubes. The model and experiment show that the interaction field in nanotubes is smaller than that in solid nanowires due to the packing fraction reduction in tubes related to their inner cavity. Finally, good agreement between the model and experiment is found for the variation of the interaction field as a function of the tube wall thickness.
Carbon nanotubes (CNTs) have emerged worldwide because of their remarkable properties enlarging their field of applications. Functionalization of CNTs is a convenient strategy to tackle low dispersion and solubilization of CNTs in many solvents or polymers. It can be done by covalent or noncovalent surface functionalization that is briefly discussed regarding the current literature. Endohedral and exohedral are conventional methods based on covalent and van der Waals bonding forces that are created through CNT functionalization by various materials. In this paper, a review of new approaches and mechanisms of functionalization of CNTs is proposed, including amidation, fluorination, bromination, chlorination, hydrogenation, and electrophilic addition. Our analysis is supported by several characterization methods highlighting recent improvements hence extending the range of applicability of CNTs.
Electronic devices that transmit, distribute, or utilize electrical energy create electromagnetic interference (EMI) that can lead to malfunctioning and degradation of electronic devices. EMI shielding materials block the unwanted electromagnetic waves from reaching the target material. EMI issues can be solved by using a new family of building blocks constituted of polymer and nanofillers. The electromagnetic absorption index of this material is calculated by measuring the “S-parameters”. In this article, we investigated the use of artificial intelligence (AI) in the EMI shielding field by developing a new system based on a multilayer perceptron neural network designed to predict the electromagnetic absorption of polycarbonate-carbon nanotubes composites films. The proposed system included 15 different multilayer perception (MLP) networks; each network was specialized to predict the absorption value of a specific category sample. The selection of appropriate networks was done automatically, using an independent block. Optimization of the hyper-parameters using hold-out validation was required to ensure the best results. To evaluate the performance of our system, we calculated the similarity error, precision accuracy, and calculation time. The results obtained over our database showed clearly that the system provided a very good result with an average accuracy of 99.7997%, with an overall average calculation time of 0.01295 s. The composite based on polycarbonate−5 wt.% carbon nanotube was found to be the ultimate absorber over microwave range according to Rozanov formalism.
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