Yi Cao , Mohamed Amine Khadimallah , Mohd Ahmed , Hamid Assilzadeh
{"title":"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","authors":"Yi Cao , Mohamed Amine Khadimallah , Mohd Ahmed , Hamid Assilzadeh","doi":"10.1016/j.synthmet.2024.117656","DOIUrl":null,"url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":22245,"journal":{"name":"Synthetic Metals","volume":"307 ","pages":"Article 117656"},"PeriodicalIF":4.0000,"publicationDate":"2024-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Enhancing structural analysis and electromagnetic shielding in carbon foam composites with applications in concrete integrating XGBoost machine learning, carbon nanotubes, and montmorillonite\",\"authors\":\"Yi Cao , Mohamed Amine Khadimallah , Mohd Ahmed , Hamid Assilzadeh\",\"doi\":\"10.1016/j.synthmet.2024.117656\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>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.</p></div>\",\"PeriodicalId\":22245,\"journal\":{\"name\":\"Synthetic Metals\",\"volume\":\"307 \",\"pages\":\"Article 117656\"},\"PeriodicalIF\":4.0000,\"publicationDate\":\"2024-05-31\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Synthetic Metals\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0379677924001188\",\"RegionNum\":3,\"RegionCategory\":\"材料科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Synthetic Metals","FirstCategoryId":"88","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0379677924001188","RegionNum":3,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
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.
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.