Preparation Methods for Graphene Metal and Polymer Based Composites for EMI Shielding Materials: State of the Art Review of the Conventional and Machine Learning Methods

Abstract

Advancement of novel electromagnetic inference (EMI) materials is essential in various industries. The purpose of this study is to present a state-of-the-art review on the methods used in the formation of graphene-, metal- and polymer-based composite EMI materials. The study indicates that in graphene- and metal-based composites, the utilization of alternating deposition method provides the highest shielding effectiveness. However, in polymer-based composite, the utilization of chemical vapor deposition method showed the highest shielding effectiveness. Furthermore, this review reveals that there is a gap in the literature in terms of the application of artificial intelligence and machine learning methods. The results further reveal that within the past half-decade machine learning methods, including artificial neural networks, have brought significant improvement for modelling EMI materials. We identified a research trend in the direction of using advanced forms of machine learning for comparative analysis, research and development employing hybrid and ensemble machine learning methods to deliver higher performance.

Keywords

• electromagnetic inferences
• shielding
• graphene
• metal
• polymer
• traditional methods