Abstract
Zincite (ZnO) nanoparticles are prepared by adopting reflux assisted co-precipitation method. Prepared product is subjected to different instrumental techniques to investigate its morphology and lattice structure. Rietveld refinements are performed on XRD results and values of various lattice parameters are calculated and structural model of ZnO is predicted. The morphology of the product is analyzed with the help of scanning and transmission electron microscopies (SEM and TEM). The size of prepared nanoparticles is in the range of 80–100 nm where as some larger particles having irregular morphology with size up to 1 μm are also observed in the product. Prepared product is also used as fuel additive and its effect on different fuel parameters is studied. The combustion characteristics (flash and fire point) and physical characteristics (cloud and pour point, kinematic viscosity and specific gravity) of fuel are studied in the presence of 10, 15 and 20 ppm dosage of additive. ZnO is used as nanoadditive for enhancing concrete durability. Concrete pellets are formed with different dosage (0, 0.1, 0.5, 1.5 w/w%) of nanoadditive. Ratio of cement, sand and nanoadditive is kept as 33:66:1 respectively in all concrete samples with constant amount of water. Effect of nanoadditve is studied on different properties of cement such as aging, porosity, compressive strength, specific heat, thermal conductivity and thermal diffusivity. ZnO nanoadditive is found to be effective in modulating both mechanical and thermal properties of concrete samples.
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The authors are also highly grateful to the Department of Chemistry, University of Agriculture, Faisalabad 38000, Pakistan for financial assistance. The authors have also declared no conflict of interest. However, the final write up was accomplished at King Fahd University of petroleum and Minerals (KFUPM), Dhahran, Kingdom Saudi Arabia.
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Jamil, S., Tariq, T., Khan, S.R. et al. Structural Characterization, Synthesis and Application of Zincite Nanoparticles as Fuel Additive. J Clust Sci 33, 1165–1176 (2022). https://doi.org/10.1007/s10876-021-02047-y
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DOI: https://doi.org/10.1007/s10876-021-02047-y