Pattern formation during Electropolishing

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We are developing a new field of research, 'Pattern Formation during Electropolishing', which has the potential of being a powerful tool chest for many applications requiring porous or nanopatterned surfaces, or sharp tips. Electropolishing is largely (albeit not entirely) understood in a one-dimensional manner, as a function of distance from the electrode surface but assuming homogeneous behavior in plane parallel to the electrode surface. Instabilities or inhomogeneities that can lead to symmetry breaking and pattern formation are not usually accounted for. We strive to develop 3-dimensional models that take into account a number of near-surface processes that can explain the formation of a variety of different patterns. While there are isolated examples in the literature of in-depth studies of a single system, we are the first group in the world to take a comprehensive approach across a range of systems and phenomena. Specifically, there are several examples in the literature of competing and contradictory models, in particular with regards to the role of anodic oxides. The mechanistic differences between the processes leading to porous anodic oxides, anodic oxide nanotubes or shallow metallic patterns are entirely unclear and not explained in any current model. Due to the practical importance of these structures (especially transition metal oxide nanotubes for catalytic, photovoltaic and biomedical applications), a better understanding of how they are formed would be highly desirable and economically relevant. Phenomena that we have observed in our group include shallow metallic pattern formation (dimples, pimples, stripes and labyrinthine patterns), porous metal layers, detaching and adherent porous oxides, ordered transition metal nanotubes, necking and tip formation, as well as current oscillations resulting in stepped metallic mesas. No coherent and comprehensive 3-dimensional model of electropolishing exists as of yet that could account for all of our observations but we are working to gain full predictive control over this powerful set of tools.