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Synergistic effects of fly ash and graphene oxide composites at high temperatures and prediction using ANN and RSM approach

粉煤灰 石墨烯 材料科学 水泥 氧化物 耐久性 微观结构 复合材料 纳米技术 冶金
作者
I. Ramana,N. Parthasarathi
出处
期刊:Scientific Reports [Nature Portfolio]
卷期号:15 (1): 1604-1604 被引量:5
标识
DOI:10.1038/s41598-024-83778-6
摘要

Fly ash (FA) is a consequence of burning coal and is widely used in construction because of its pozzolanic qualities, which increase the strength and longevity of materials. Graphene oxide (GO) is a functionalized version of graphene with low electrical conductivity, high mechanical strength, and a large surface area. By examining the behavior of fly ash and GO composites at high temperatures, new materials with improved mechanical and functional qualities that are appropriate for a range of industrial uses can be created. By improving the material quality of cement and reducing material use while boosting durability, adding graphene oxide to cement offers an opportunity to drastically lower carbon emissions. However, the entire effect is dependent on the GO emissions, manufacturing procedures, and viability from an economic standpoint; to fully reap the benefits of this novel strategy for the environment, more research and development are necessary. this paper primarily examined the effects of high temperatures on the mechanical, microstructural, and thermal properties of concrete when fly ash was replaced with 20% by weight of the cement, graphene oxide were added by 0.08% by the weight of the cement, and their combinations i.e. (20% FA + 0.08% GO) by the weight of the cement were tested at various temperatures 200, 400, 600, and 800 °C for 4 h. The ideal temperature decided by the mechanical characteristics is 200 °C, and to understand the microstructure of graphene oxide (GO) material is essential for understanding its performance, stability, and the principles underlying its behavior, particularly at elevated temperatures. Machine learning tools such as Response Surface Methodology (RSM) and Artificial Neuron Network (ANN) have been used to forecast the mechanical properties of concrete.
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