Control of Marangoni-driven patterning by an optimized distribution of surface energy

Steven K. Stanley, Christopher J. Ellison, Roger T. Bonnecaze

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


We computationally demonstrate a method to control Marangoni-driven flows and create patterns with sharp features on polymer films by optimizing the spatial variation of surface energy or tension. This Marangoni-driven patterning (MDP) uses the variations in surface tension to drive fluid flow. By selectively exposing a thin polymer film to UV light, a photochemical reaction takes place, which subsequently alters the surface tension of the polymer film in the exposed regions. On heating above its glass transition temperature, the polymer flows from regions of lower to higher surface tension to form hill-and-valley features. A barrier to advancing the application of MDP is that the flow will often dull sharp features and degrade the fidelity of the desired pattern. To compensate a pixel-based optimization of the surface energy or equivalently, the photoexposure pattern is developed. A genetic algorithm is used to search for the optimum photoexposure pattern based on simulations of the flow, which includes Marangoni and capillary forces and diffusion of the surface tension promoter. The optimization of the photoexposure pattern significantly improves the fidelity of the desired final pattern for a wide range of annealing temperatures and times. Guidelines for successful MDP are identified based on ratios of characteristic times for the Marangoni and capillary flows and lateral diffusion.

Original languageEnglish (US)
Article number065304
JournalJournal of Applied Physics
Issue number6
StatePublished - Feb 14 2020

Bibliographical note

Funding Information:
The authors would like to thank Dr. Michael Baldea for helpful conversations regarding optimization, Dr. Chris Mack for helpful conversations regarding photolithography, and Dr. Kamy Sepehrnoori for helpful conversations regarding numerical methods. This work is supported by a grant from the National Science Foundation (NSF) under Cooperative Agreement No. EEC-1160494. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation. There are no conflicts to declare.

Publisher Copyright:
© 2020 Author(s).

Copyright 2020 Elsevier B.V., All rights reserved.

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