Bacterial adhesion reduction on the surface with a simulated pattern: An insight into extrand model.
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| Title: | Bacterial adhesion reduction on the surface with a simulated pattern: An insight into extrand model. |
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| Authors: | Ayazi, Masoumeh1, Golshan Ebrahimi, Nadereh1 ebrahimn@modares.ac.ir, Jafari Nodoushan, Emad1 |
| Source: | International Journal of Adhesion & Adhesives. Jan2019, Vol. 88, p66-73. 8p. |
| Subjects: | Bacterial adhesion, Patterning therapy, Antibacterial agents, Lithography, Surface tension |
| Abstract: | Abstract In recent years, patterning has become a highly interesting approach for creating anti-bacterial surfaces. Since bacteria have become adept at protecting themselves against their killing agents, creating surfaces that able to repel bacteria solutions are simple and eco-friendly emerging approaches to overcome the new generations of bacteria. Extrand's model presents a methodology for calculation of dynamic contact angles by considering the contact line fraction of solid asperities. In this study, a simulation of this model is presented with the aim of understanding the effect of surface patterns parameters on superhydrophobicity. The results demonstrate that a pattern with hexagonal array of circle pillars leads to the most achievable contact angle. To verify the theoretical results, similar pattern was created on a PDMS surface with a two-step lithography method. The resultant surface had shown an advancing contact angle of 161°. Anti-bacterial characterizations of the PDMS surface revealed a remarkable reduction in the adhesion of gram-negative bacteria, E. coli. The performed surface tension measurements of the bacterial solutions demonstrate that presence of bacteria in water alter the surface tension of the aqueous solution. In addition, the effectiveness of applied patterns to the reduction of bacterial adhesion to the surface is differed for gram negative and positive bacterial solutions. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Adhesion & Adhesives is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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