结合 XGBoost 机器学习、碳纳米管和蒙脱石,加强碳泡沫复合材料的结构分析和电磁屏蔽,并将其应用于混凝土中

IF 4 3区 材料科学 Q2 MATERIALS SCIENCE, MULTIDISCIPLINARY
Yi Cao , Mohamed Amine Khadimallah , Mohd Ahmed , Hamid Assilzadeh
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引用次数: 0

摘要

碳泡沫复合材料的电磁屏蔽包括利用碳泡沫的天然导电性来阻挡或吸收电磁场。这些复合材料可保护敏感的电子设备免受电磁干扰(EMI)的影响,因为电磁干扰会干扰或损坏这些设备的运行。XGBoost 机器学习技术可分析和优化材料成分,以屏蔽电磁干扰。通过将碳纳米管(CNT)和蒙脱石(MMT)集成到碳泡沫样品中,本研究旨在确定碳泡沫复合材料在不同频率下的电磁屏蔽效能(SE)、导电率和介电常数。这项分析将有助于开发增强型复合材料,以有效屏蔽混凝土环境中的电磁干扰,尤其是敏感电子设备所在结构中的电磁干扰。这项研究的新颖之处在于将碳纳米管和蒙脱石双重融入碳泡沫复合材料,独特地探索了它们对机械和电气性能的协同效应。该研究采用了 XGBoost 机器学习技术来优化材料成分,以增强电磁干扰屏蔽能力。本研究探讨了碳纳米管和蒙脱石在碳泡沫复合材料中的双重集成,评估了它们对机械和电磁特性的协同影响。在碳泡沫中加入 1%、3% 和 5% 的这些添加剂后,压缩强度、拉伸强度和弯曲强度都有了显著提高,其中 5% 的蒙脱石添加剂使压缩强度从 3.96 兆帕提高到 9.44 兆帕,几乎翻了一番。同时,这些复合材料显示出更强的电磁干扰 SE,并在不同频率下具有详细的电气特性。利用 XGBoost 机器学习技术,得出了 EMI 屏蔽的最佳材料成分,为要求坚固结构和电气性能的工业应用带来了进步。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Enhancing structural analysis and electromagnetic shielding in carbon foam composites with applications in concrete integrating XGBoost machine learning, carbon nanotubes, and montmorillonite

Electromagnetic shielding in carbon foam composites involves using the natural conductivity of carbon foam to block or absorb electromagnetic fields. These composites protect sensitive electronic devices from electromagnetic interference (EMI), which can disrupt or damage their operation. The inclusion of XGBoost machine learning analyzes and optimizes the material compositions for electromagnetic interference shielding. By integrating Carbon Nanotubes (CNTs) and Montmorillonite (MMT) into samples of carbon foam, this research aims to identify the electromagnetic shielding effectiveness (SE), electrical conductivity, and dielectric permittivity at different frequencies of carbon foam composites. This analysis will facilitate the development of enhanced composite materials tailored for effective EMI shielding in concrete environments, particularly in structures housing sensitive electronic equipment. The novelty of this study lies in the dual integration of carbon nanotubes and montmorillonite into carbon foam composites, uniquely exploring their synergistic effects on both mechanical and electrical properties. The study employs XGBoost machine learning to optimize the material compositions for enhanced electromagnetic interference shielding. This study probes the dual integration of CNTs and montmorillonite into carbon foam composites, evaluating their synergistic impact on mechanical and electromagnetic properties. Incorporating 1 %, 3 %, and 5 % of these additives into carbon foams, substantial improvements were recorded in compressive, tensile, and flexural strengths, peaking with a 5 % MMT enhancement that nearly doubled the compressive strength from 3.96 MPa to 9.44 MPa. Concurrently, these composites displayed enhanced EMI SE, with detailed electrical characterizations at varying frequencies. Employing XGBoost machine learning, optimal material compositions were derived for EMI shielding, presenting advancements for industrial applications requiring robust structural and electrical performance.

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来源期刊
Synthetic Metals
Synthetic Metals 工程技术-材料科学:综合
CiteScore
8.30
自引率
4.50%
发文量
189
审稿时长
33 days
期刊介绍: This journal is an international medium for the rapid publication of original research papers, short communications and subject reviews dealing with research on and applications of electronic polymers and electronic molecular materials including novel carbon architectures. These functional materials have the properties of metals, semiconductors or magnets and are distinguishable from elemental and alloy/binary metals, semiconductors and magnets.
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