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

• Published in: Metals (Basel ) Vol. 11; no. 8; p. 1164
• Main Authors: Ayub, Saba, Guan, Beh Hoe, Ahmad, Faiz, Javed, Muhammad Faisal, Mosavi, Amir, Felde, Imre
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-08-2021
• Subjects: Artificial intelligence; Artificial neural networks; Carbon; Chemical vapor deposition; Composite materials; Data science; electromagnetic inferences; Electromagnetic shielding; Graphene; Jargon; Keywords; Machine learning; metal; Metals; Methods; Permeability; polymer; Polymer matrix composites; Polymers; R&D; Research & development; shielding; Software; State-of-the-art reviews; traditional methods
• ISSN: 2075-4701; 2075-4701
• DOI: 10.3390/met11081164

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.