Electromagnetic interference shielding property of polybenzoxazine/graphene/nickel composites

A review on recent progress in polymer composites for effective electromagnetic interference shielding properties - structures, process, and sustainability approaches

The rapid proliferation and extensive use of electronic devices have resulted in a meteoric increase in electromagnetic interference (EMI), which causes electronic devices to malfunction. The quest for the best shielding material to overcome EMI is boundless. This pursuit has taken different directions, right from materials to structures to process, up to the concept of sustainable materials. The emergence of polymer composites has substituted metal and metal alloy-based EMI shielding materials due to their unique features such as light weight, excellent corrosion resistance, and superior electrical, dielectric, thermal, mechanical, and magnetic properties that are beneficial for suppressing the EMI. Therefore, polymer nanocomposites are an extensively explored EMI shielding materials strategy. This review focuses on recent research developments with a major emphasis on structural aspects and processing for enhancing the EMI shielding effectiveness of polymer nanocomposites with their underlying mechanisms and some glimpses of the sustainability approaches taken in this field.

Structural design and preparation of Ti3C2Tx MXene/polymer composites for absorption-dominated electromagnetic interference shielding

Electromagnetic interference (EMI) is a pervasive and harmful phenomenon in modern society that affects the functionality and reliability of electronic devices and poses a threat to human health. To address this issue, EMI-shielding materials with high absorption performance have attracted considerable attention. Among various candidates, two-dimensional MXenes are promising materials for EMI shielding due to their high conductivity and tunable surface chemistry. Moreover, by incorporating magnetic and conductive fillers into MXene/polymer composites, the EMI shielding performance can be further improved through structural design and impedance matching. Herein, we provide a comprehensive review of the recent progress in MXene/polymer composites for absorption-dominated EMI shielding applications. We summarize the fabrication methods and EMI shielding mechanisms of different composite structures, such as homogeneous, multilayer, segregated, porous, and hybrid structures. We also analyze the advantages and disadvantages of these structures in terms of EMI shielding effectiveness and the absorption ratio. Furthermore, we discuss the roles of magnetic and conductive fillers in modulating the electrical properties and EMI shielding performance of the composites. We also introduce the methods for evaluating the EMI shielding performance of the materials and emphasize the electromagnetic parameters and challenges. Finally, we provide insights and suggestions for the future development of MXene/polymer composites for EMI shielding applications.

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

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.