Deep-learning approach for developing bilayered electromagnetic interference shielding composite aerogels based on multimodal data fusion neural networks.

• Tác giả: Peng Ai, Chenglei He, Yun Jiang, Xiaoping Mai, Lan Xie, Bai Xue, Liya Yu
• Ngôn ngữ: eng
• Thông tin xuất bản: United States : Journal of colloid and interface science , 2025
• Bộ sưu tập: NCBI
• ID: 710485

Tóm tắt

A non-experimental approach to developing high-performance EMI shielding materials is urgently needed to reduce costs and manpower. In this investigation, a multimodal data fusion neural network model is proposed to predict the EMI shielding performances of silver-modified four-pronged zinc oxide/waterborne polyurethane/barium ferrite (Ag@F-ZnO/WPU/BF) aerogels. First, 16 Ag@F-ZnO/WPU/BF samples with varying Ag@F-ZnO and BF contents were successfully prepared using the pre-casting and directional freezing techniques. The experimental results demonstrate that these aerogels perform well in terms of averaged EMI shielding effectiveness (SE