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.

Recent advancements in the electromagnetic interference shielding performance of nanostructured materials and their nanocomposites: a review

“…Reduced graphene oxide has attracted considerable attention in the development of lightweight, multifunctional shielding materials. [46][47][48][49][50][51][52][53][54][55][56][57][58][59][60][61][62][63][64][65] According to Yuan and coworkers, 46 a highly aligned laminated structure consisting of RGO foam showed EMI SE of $36-43 dB in the X-band. 46 Wu et al 47 studied the effect of annealing temperature on the permittivity and electromagnetic attenuation performance of reduced graphene oxide.…”

“…In view of this, an updated literature survey has been carried out below on the EMI shielding performance of carbon nanotubes, graphene and their inorganic material-based nanocomposites. 21–65…”

Experimental Correlation of the Role of Synthesized Biochar on Thermal, Morphological, and Crystalline Properties of Coagulation Processed poly(1,4-Phenylene Sulfide) Nanocomposites

“…The carbon products obtained from industrial sources has been used as filler in various polymers [1][2][3]. It has been extensively practiced in industrial sectors to advance the routine properties of polymeric products [4][5][6]. There has been a dynamic study in industrial and academic fields to study composites and nanocomposites of polymers.…”

Prediction and Control of Thermal Transport at Defective State Gr/h-BN Heterojunction Interfaces

“…Moreover, molecular dynamics simulations cannot achieve large-scale optimization to calculate the physical properties through traditional theoretical calculations [16,17]. In recent years, the use of deep learning to design the properties of heterogeneous composites has received much attention [18][19][20], for example, Li et al [21] used deep learning algorithms to establish the mapping between the topological properties of artificial phonon crystals and acoustic wave propagation properties. Deep learning-based neural networks can establish the intrinsic connection between macroscopic mechanical properties and microstructure, in addition, to accurately and efficiently predicting the mechanical properties of multicomponent composites.…”