被撤回的出版物: Rational design of CoNi@C-BTC/rGO nanocomposite coated with PEDOT polymer towards enhancing the microwave absorption in X-band frequency (Retracted article. See vol. 958, 2023)

The present study focusses on the design of microwave absorbers based on CoNi-BTC (CN) mixed with reduced graphene oxide and covered by PDOT to form P@CN/G nanocomposites. They were successfully synthesized via solvothermal method followed by pyrolysis and then characterized with XRD, XPS, VSM and FESEM. CN nanoparticles have an approximately uniform spheroidal monodispersed morphology with 300-400 nm diameter, and are uniformly covered by PDOT. XRD performed on P@CN/G sample clearly suggests the coexistence of both CoNi@C-BTC and graphene phases in the nanocomposite while XPS characterization confirms that Co, Ni, C, and O are detected in the P@CN/G sample. VSM measurements reveal S-like hysteresis loops for both CN, CN/G and P@CN/G, confirming soft magnetic materials and ferromagnetic behavior which linked to the presence of ferromagnetic Co and Ni nanoparticles in the nanocomposite powder. Electromagnetic parameters, namely dielectric permittivity, magnetic permeability and associated losses, are measured for P@CN/G composite sample dispersed into epoxy resin with 15%, 20%, 25% and 30% concentration using a vector network analyzer. Measured parameters are used to predict by simulations the return losses (RL) for various combinations of thickness and concentration. It is concluded from the exhaustive comparison using a FOM gauge that sample having 30% concentration and thickness 2.4 mm achieves the best performance, i.e. a value FOM equal to 1150 and minimal return losses RL equal to -50 dB. (C) 2021 Elsevier B.V. All rights reserved.

Automated summary

This study focuses on the design and synthesis of microwave absorbers based on CoNi-BTC and reduced graphene oxide, resulting in P@CN/G nanocomposites. Through various characterizations, the morphology of nanoparticles and the presence of magnetic Co and Ni nanoparticles were confirmed, leading to the determination of optimal sample parameters for performance.