The Effect of Chemical Composition on the Morphology of Pb/Zn-Containing Dust

Abstract

Dust containing lead and zinc is a harmful contaminant, which causes serious harm to the natural environment and human health. At present, it is believed that the microscopic morphology of lead-zinc dust is intimately related to its biological toxicity. Chemical composition serves as a pivotal factor influencing the structural characteristics of dust. However, research on the impact of chemical composition variations on the microscopic morphology of dust containing lead and zinc remains inadequate. The particle size analysis reveals that as PbO content increases and ZnO content decreases, the particle size of the dust diminishes, but some samples exhibit a larger agglomeration structure. Combined with the results of the box number method, it is evident that at lower magnifications, an increase in PbO content leads to a decrease in image complexity and a loosening of aggregated structures. The similarity in pile shapes amplifies this trend, resulting in a decline in the box-counting dimension (D value) within the PbO/ZnO ratio range of 26.45 to 138, accompanied by an inverse change in the corresponding goodness of fit R-sq value. At the observation multiple of 30,000 times (30 K), smaller particles within the sample become visible, and the presence of relatively larger particles and complex sizes enhances the fractal characteristics of the sample, leading to a higher D value. Within the PbO/ZnO ratio range of 90/10 to 99/1, a coupling relationship exists between the chemical composition of the sample and the morphology of the dust. Specifically, the PbO/ZnO ratio exhibits a positive correlation with the D value. Conversely, the diversity of corresponding fractal features is negatively correlated with the D value. When the PbO content surpasses 99%, this correlation weakens, and the diversity of graphical representations displays an alternating pattern of growth and decrease. Notably, the D value and the goodness of fit (R-sq) of the D value are negatively correlated, indicating that as the complexity of the graph increases, the goodness of fit decreases.