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Machine learning-assisted development of asymmetric melamine foam composites featuring a dendritic copper sulfide conductive skeleton and a planar conductive coating
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DOI：10.1016/j.compositesa.2026.110141.png)
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Abstract
En 中文
The growing demand for electromagnetic shielding composites in military and industrial applications presents new design challenges, particularly in achieving rapid production under moderate conditions. Herein, the melamine foam @ copper sulfide/polydimethylsiloxane/poly(3,4-ethylenedioxythiophene): poly(styrene sulfonate) (MF@CuS/PDMS/PEDOT: PSS) (MCuPP) composites were obtained by hydrothermal synthesis, encapsulation, and coating. An MF@CuS/PDMS composite (a porous conductive layer) is fabricated to elongate the propagation pathway of electromagnetic waves (EMWs). PEDOT: PSS conductive polymer solution is dropped on the upper surface of the MF@CuS/PDMS, which dries to a flat surface and is considered the reflective layer. When EMWs incident on the porous MF@CuS/PDMS with higher electrical conductivity (0.36 S/cm), the 8.2-mm-thick dual-conductive-layer asymmetric MCuPP2.4 exhibits an average total electromagnetic shielding performance (SET) of 57.72 dB and an A value of 0.496. The conductive loss generated by the porous conductive skeleton MF@CuS, combined with the back-and-forth reflection of EMWs between the PEDOT: PSS and conductive skeleton, endows the composite with a high SET performance. Using machine learning, the SET of the MCuPP was predicted. The results indicate that this method is suitable for this study and can reduce experimental costs. These findings provide a reference for the design of electromagnetic shielding materials incorporating asymmetric structures.
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