Electromagnetic (EM) wave absorbing materials are strongly desired for the reason that EM pollution has become a serious problem with the rapid development of modern technology, which brings harm to the environment and human health. Herein, nanocomposites of Sn/reduced graphene oxide (Sn/rGO) as a novel type of EM absorbing materials were synthesized via a facile and efficient strategy. Nanoscale SnO 2 microspheres were synthesized by sol-gel method, and the Sn/rGO composites were obtained after hydrothermal process with GO. Samples with different Sn content were fabricated (rGO:Sn = 30:1, 20:1, 10:1, mass ratio), and their EM wave absorption performance were analyzed. By changing the mass ratio of Sn and rGO, dielectric constant of the composites can be adjusted, and better impedance matching can be achieved. The unique capacitive structure and conductive network enable the material to have strong dielectric loss and excellent absorption performance. This research offers a new route for designing and fabricating high-frequency EM wave absorbing materials.
A three-layer BP neural network model was established by relating subjective evaluation of fabric prickle level and 16 objective parameters from KES-FB system. The elastic gradient decrease method was adopted for network training to achieve the preset precision of the model which was later applied to fabric prickle level evaluation. Results from this method gave a considerably accuracy compared with actual subjective results which implied a compatibility between BP neural network and traditional subjective evaluation.
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