Unveiling the Nucleation and Growth Mechanisms in Ni-based Alloys Electrodeposition

Electrodeposition plays a crucial role in the fabrication of advanced coatings and materials with tailored properties. Research on the electrochemical deposition of nickel-based alloys highlights its catalytic performance. Investigating the nucleation and growth mechanisms in Ni-based alloys deposition bridges the gap between fundamental electrochemistry and practical applications.

 

Defining New Electrochemical Reactions

 

One of the key challenges in Ni alloy electrodeposition is understanding the reduction mechanism of alloying element. Unlike nickel, which reduces directly, certain alloying elements undergo a more complex reduction process. Through electrochemical modeling, new reaction pathways were defined by summing multiple electrochemical reactions and optimizing the Butler-Volmer equations. This approach enables a more accurate description of the kinetic parameters governing the process.

 

AI-Powered Analysis of SEM Images

 

To quantify the nucleation behavior, the VUB AI tool was utilized for analyzing scanning electron microscopy (SEM) images. This AI-driven approach enables precise detection and measurement of nuclei, providing valuable insights into the effect of deposition parameters on nucleation density and growth dynamics. By optimizing segmentation parameters, nuclei were successfully identified and quantified across various experimental conditions.

 

Quantifying Nucleation Behavior

 

Extracting meaningful data from SEM images is essential for understanding nucleation kinetics. A methodology was developed to process nucleation images, generating frequency distributions of nuclei based on size. These results were further analyzed to correlate nucleation trends with electrodeposition conditions, enabling a deeper understanding of how process parameters influence nucleation density.

 

Bridging Experimentation and Simulation

 

By integrating experimental data with electrochemical simulations, a strong agreement between theoretical predictions and real observations was demonstrated. The kinetic parameters derived from this study provide a robust foundation for optimizing Ni-alloy deposition processes in industrial and research applications.
This work contributes to the advancement of electrodeposition science by combining electrochemical modeling, AI-driven image analysis, and experimental validation. These insights pave the way for improving the design and performance of Ni-based materials coatings in various technological fields.