PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
March 3, 2026Coatings1 citationsOpen Access

Effect of Physical Control Parameters and Hydrodynamic Behavior on Copper Electrodeposition Efficiency: A Numerical Simulation Study

View Full Paper
MBMarco BonechiSSSalvatore Di SivoGZGiacomo Zambelli

Key Points

  • Electrodeposition efficiency improves as fluid direction optimizes coating thickness distribution, improving uniformity.
  • Results indicate that orientation of cathodes can significantly influence deposit quality during copper electrodeposition.
  • Assessment using multiphysics simulation captures the behaviors of ionic transport and fluid flow relevant to electrodeposition.
  • Guidance on agitation direction may enhance the design of electroplating processes for industrial applications.

Abstract

This study concerns the simulation of copper electrodeposition and related phenomenological and technological aspects as influenced by electrode geometry and electrolyte flow velocity. A multiphysics simulation approach was employed, integrating mathematical models accounting for electrochemical deposition and hydrodynamic behavior. Ionic transport is described by the Nernst-Planck equations, electrode kinetics by Butler–Volmer expressions, and fluid flow by the Navier–Stokes equations. Simplified 2D and 3D models were developed to investigate industrial frame plating electrodeposition processes. The results indicate that the fluid direction of the solution in relation to the position of the substrate within the electrodeposition cell enables the distribution of the thickness of the coating to be optimized. Numerical simulation can be used to guide the choice of the orientation of cathodes to be electroplated inside the electroplating tank, to take into consideration agitation direction, and to achieve the best deposit uniformity.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Bonechi et al. (2026) studied this question.

synapsesocial.com/papers/69a75d0fc6e9836116a267f3https://doi.org/10.3390/coatings16020162
Ask AI
Helpful
Bookmark
Share
View Full Paper