The dynamic adhesion behavior of micrometer-scale silica particles is investigated numerically for a low Reynolds number shear flow over a planar collecting wall with randomly distributed electrostatic heterogeneity at the 10-nanometer scale. The hydrodynamic forces and torques on a particle are coupled to spatially varying colloidal interactions between the particle and wall. Contact and frictional forces are included in the force and torque balances to Capture Particle skipping, rolling, and arrest. These dynamic adhesion Signatures are consistent with experimental results and are reminiscent of motion signatures observed in cell adhesion Under flowing conditions, although for the synthetic system the particle-wall interactions are controlled by colloidal forces rather than physical bonds between cells and a functionalized surface. As the fraction of the surface (Theta) covered by the cationic patches is increased from zero, particle behavior sequentially transitions from no contact with the Surface to skipping, rolling, and arrest, with the threshold patch density for adhesion (Theta(crit)) always greater than zero and in quantitative agreement with experimental results. The ionic strength of the flowing solution determines the extent of the electrostatic interactions and can be used to tune selectively the dynamic adhesion behavior by modulating two competing effects. The extent of electrostatic interactions in the plane of the wall, or electrostatic zone of influence, governs the importance of spatial fluctuations in the cationic patch density and thus determines if flowing particles contact the wall. The distance these interactions extend into solution normal to the wall determines the strength of the particle-wall attraction, which governs the transition from skipping and rolling to arrest. The influence of Theta, particle size, Debye length, and shear rate is quantified through the Construction of adhesion regime diagrams, which delineate the regions in parameter space that give rise to different dynamic adhesion signatures and illustrate selective adhesion based on particle size or Curvature. The results Of this study are suggestive of novel ways to control particle-wall interactions using randomly distributed surface heterogeneity. (C) 2008 Elsevier Inc. All rights reserved.