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Electrical resistivity and compressive strength of cement mortar based on green magnetite nanoparticles and wastes from steel industry

材料科学 抗压强度 水泥 导电体 复合材料 电阻率和电导率 灰浆 磁铁矿 填料(材料) 轧管秤 冶金 电气工程 工程类
作者
Seerwan M. Ahmed,Ibtisam Kamal
出处
期刊:Case Studies in Construction Materials [Elsevier BV]
卷期号:17: e01712-e01712 被引量:22
标识
DOI:10.1016/j.cscm.2022.e01712
摘要

Smart emerging materials are intelligent composites that have properties different from normal materials. Conductive building materials, including conductive cement mortar and concrete, may be defined as a mixture containing a certain amount of electrically conductive components (filler) designed to enable the conduction of electricity. They have amazing infrastructure applications, including building surface heating, pavement de-icing, electromagnetic defence, cathodic protection, and enhancing foundations and buried ground grid systems. In this study, green magnetite nanoparticles (Fe3O4) are synthesized using plant-mediated rout, characterized, and used as electro-conductive fillers in cement-mortar that are formulated using two types of industrial wastes from the steel industry; the steel slag (SS) and mill scale (MS) as fine aggregates instead of sand. The effect of the water/cement ratio, the content of the green conductive filler, and the industrial wastes on the electrical resistivity and compressive strength of the prepared mortars are investigated. Conventional cement mortar samples are prepared and tested similarly for comparison purposes. The results revealed that the electrical resistivity decreased and compressive strength increased with increasing the content of the conductive fillers, and response surface analysis results confirmed that smart cement-steel slag mortars with an optimum electrical resistivity of 8.83 Ω.m. and compressive strength of 36.08 MPa could be formulated with 0.55 w/c and incorporation of 8.15 wt% mill scale, 45.6 ml green magnetite nanoparticles (NPs) colloid. The developed mathematical models were estimated with high regression coefficients and a % error of less than 5.5, confirming their accuracy. Mathematical correlations between electrical resistivity and compressive strength are developed.
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