Visualization of Deep Convolutional Neural Networks to Investigate Porous Nanocomposites for Electromagnetic Interference Shielding

Abstract

Constructing porous structures in electromagnetic interference (EMI) shielding materials is a common strategy to decrease the secondary pollution caused by the reflection of electromagnetic waves (EMWs). However, the lack of direct analysis methods makes it difficult to fully understand the effect of porous structures on EMI, hindering EMI composites’ development. Furthermore, while deep learning techniques, such as deep convolutional neural networks (DCNNs), have significantly impacted material science, their lack of interpretability limits their applications to property predictions and defect detection tasks. Until recently, advanced visualization techniques provided an approach to reveal the relevant information behind DCNNs’ decisions. Inspired by it, a visual approach for porous EMI nanocomposite mechanism studies is proposed. This work combines DCNN visualization with experiments to investigate EMI porous nanocomposites. First, a rapid and straightforward salt-leaked cold-pressing powder sintering method is employed to prepare high-EMI CNTs/PVDF composites with various porosities and filler loadings. Notably, the solid sample with 30 wt % loading maintains an ultrahigh shielding effectiveness of 105 dB. The influence of porosity on the shielding mechanism is discussed macroscopically based on the prepared samples. To determine the shielding mechanism, a modified deep residual network (ResNet) is trained on a dataset of scanning electron microscopy (SEM) images of the samples. The Eigen-CAM visualization of the modified ResNet intuitively shows that the amount and depth of the pores impact the shielding mechanisms and that shallow pore structures contribute less to EMW absorption. This work is instructive for material mechanism studies. Besides, the visualization has the potential as a porous-like structure marking tool.

Recent Trends in Polymeric Foams and Porous Structures for Electromagnetic Interference Shielding Applications

“…Even more scarce is the application of AI and ML-based tools to the analysis of polymeric foams and porous structures; indeed, they are rarely used to enhance the understanding of the effect of the cellular/porous structure development on EMI shielding mechanisms and efficiencies. As such, Shi and co-workers [ 61 ] recently combined deep convolutional neural network (DCNN) visualization with experimental data to analyze the EMI shielding of porous PVDF-CNT nanocomposites; this involved a modified deep residual network (ResNet) which was trained using SEM micrographs of the prepared foams, demonstrating its effectiveness in assessing the impact of the cellular structure (cell size and cell density) on the shielding mechanisms, with bigger cell sizes contributing less to EM wave absorption. This study, alongside others that have focused on the preparation of porous structures created by 3D printing [ 62 ], proves how AI and ML approaches can significantly help study complex multiphase materials, particularly the analysis of properties that are greatly dependent on material (micro)structures, and which are affected by different mechanisms, as in the case of EMI shielding.…”