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Electroplating of metals into a porous preform with conductive walls is relevant in the fabrication of structural composites, fuel cells and batteries, and microelectronics. Electrodeposition process parameters, such as direct current or pulsed current, electric potential, and electrolyte concentration, as well as preform geometry, have important implications in the process outcomes including the filling process and the percentage of the infiltrated volume. Although electroplating into a vertical interconnect access (with nonconductive walls) for microelectronic applications has been extensively studied, the "flow-through" electroplating into a channel geometry with conducive walls has not been previously investigated. Here, copper infiltration into a such channel has been investigated using computational analysis for the first time. The effects of the inlet flow velocity, potential, electrolyte concentration, and microchannel geometry are systematically studied to quantify their influence on the electrodeposition rate, uniformity of the deposition front, and the infiltrated area within the channel. Computational results revealed that the unfilled area can be reduced to lower than 1% with a low applied potential, a high electrolyte concentration, and no inflow velocity. The results can be used to guide experiments involving electroplating metals into porous preforms toward reliable and reproducible manufacturing processes. 

The brick-and-mortar structure inspired by nature, such as in nacre, is considered one of the most optimal designs for structural composites. Given the large number of design possibilities, extensive computational work is required to guide their manufacturing. Here, we propose a computational framework that combines statistical analysis and machine learning with finite element analysis to establish structure–property design strategies for brick-and-mortar composites. Approximately 20,000 models with different geometrical designs were categorized into good and bad based on their failure modes, with statistical analysis of the results used to find the importance of each feature. Aspect ratio of the bricks and horizontal mortar thickness were identified as the main influencing features. A decision tree machine learning model was then established to draw the boundaries of good design space. This approach might be used for the design of brick-and-mortar composites with improved mechanical properties.