Modeling and experiments of the adhesion force distribution between particles and a surface

You, S. and Wan, M. P. (2014) Modeling and experiments of the adhesion force distribution between particles and a surface. Langmuir, 30(23), pp. 6808-6818. (doi: 10.1021/la500360f)

Abstract

Due to the existence of surface roughness in real surfaces, the adhesion force between particles and the surface where the particles are deposited exhibits certain statistical distributions. Despite the importance of adhesion force distribution in a variety of applications, the current understanding of modeling adhesion force distribution is still limited. In this work, an adhesion force distribution model based on integrating the root-mean-square (RMS) roughness distribution (i.e., the variation of RMS roughness on the surface in terms of location) into recently proposed mean adhesion force models was proposed. The integration was accomplished by statistical analysis and Monte Carlo simulation. A series of centrifuge experiments were conducted to measure the adhesion force distributions between polystyrene particles (146.1 ± 1.99 μm) and various substrates (stainless steel, aluminum and plastic, respectively). The proposed model was validated against the measured adhesion force distributions from this work and another previous study. Based on the proposed model, the effect of RMS roughness distribution on the adhesion force distribution of particles on a rough surface was explored, showing that both the median and standard deviation of adhesion force distribution could be affected by the RMS roughness distribution. The proposed model could predict both van der Waals force and capillary force distributions and consider the multiscale roughness feature, greatly extending the current capability of adhesion force distribution prediction.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: You, Dr Siming
• Authors: You, S., and Wan, M. P.
• College/School: College of Science and Engineering > School of Engineering > Systems Power and Energy
• Journal Name: Langmuir
• Publisher: American Chemical Society
• ISSN: 0743-7463
• ISSN (Online): 1520-5827
• Published Online: 21 May 2014