Abstract
When examining fractal growth issues in electrodeposited metals, analytical and control models for growth based on diffusion-limited aggregation (DLA) are frequently used. In this work, an improved DLA model was used to design a simulation program for the growth of copper dendrites deposited on point and plate electrodes. The effect of copper ion concentration on the fractal growth of copper powder dendrites was investigated. It was found that the morphology of the simulated particles in point electrodes changed from a disordered dense structure to an open dendritic structure, and the fractal dimension decreased as the binding probability increased. While in the plate electrode, the morphology changed from dense to loose, the void between the dendrite arms expanded, and the thickness of the deposited layer increased. The morphology of copper powder dendrites matched the real electrodeposition. The shape of the deposited copper was strongly influenced by the concentration of copper ions.
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Funding
This work was supported by the National Natural Science Foundation of China (Grant No. 52174331), Natural Science Foundation of Chongqing (Grant No. CSTB2023NSCQ-MSX0352), Scientific and Technological Research Program of Chongqing Municipal Education Commission (Grant No. KJQN202201529), Chongqing Doctoral “Through Train” Research Project (Grant No. CSTB2022BSXM − JCX0137), Natural Science Foundation of Chongqing (Grant No. cstc2021jcyj-msxmX0150), Natural Science Foundation of Chongqing (Grant No. cstc2021jcyj-msxmX0743), and the Master’s Degree Innovation Program Project of Chongqing University of Science and Technology (Grant No. YKJCX2220211).
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M.Y devised experimental research methods and wrote the main manuscript texts. N.W prepared Figures 4-7. J.A revised the manuscript. W.X guarantees the completeness and accuracy of the research. W.Y and H.W were responsible for directing the experiment
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Yang, M., Xia, W., An, J. et al. Fractal growth of copper powder on point and plate electrodes based on diffusion-limited aggregation model. Ionics (2024). https://doi.org/10.1007/s11581-024-05554-w
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DOI: https://doi.org/10.1007/s11581-024-05554-w