Computational models have been developed to understand the interfacial motion signatures (skipping, rolling, and arrest) of micron- and submicron-scale objects in low Reynolds number flows over recently designed nano-patterned surfaces, which form the basis for biomimetic sensors. The new sensors can, for example, mimic the rolling and capture of white blood cells near the site of an injury. Artificial molecules (patches of pDMAEMA) are used to create surface “texture” from chemical heterogeneity at nanometer length scales. These flat, 10 nm diameter patches bearing positive charge are attached with an average spacing of 1-100 nm to a silica surface that is otherwise electrostatically repulsive to negatively charged silica spheres in solution, thereby creating spatially varying colloidal forces between the particles and the wall. Although the arrangement of these patches is mostly random, the interfaces contain hidden length scales capable of selectivity, pattern recognition, motion control, and adhesion of much larger objects, which can be tuned by varying the net surface coverage by the patches and the ionic strength of the solution.

One key finding is the existence of a threshold nano-feature density below which particle adhesion does not occur, which forms the basis for selectivity. At different nano-texture densities on the sensor surface, however, particle rolling, slipping, and arrest are observed when micron-scale particles are subject to hydrodynamic forces in shear flow. The theoretical model couples the hydrodynamic forces and torques to the heterogeneous colloidal field presented by the nano-textured surfaces and is used to simulate the particle-wall interactions in this system. The simulations track the particle trajectories and velocities during flow over the nano-textured surfaces, quantitatively uphold the experimental observation of threshold adhesion behavior, and reproduce experimental results for the particle deposition rate vs. patch density on the surface.

Imperfections on the surface are taken into account in the model through the inclusion of surface roughness and contact and frictional forces between the particles and the nano-textured surface, which enables the investigation of particle motion not only in solution but also upon contact with the surface, thereby identifying the different regimes of dynamic adhesion. After experimental validation, the model is used to explore the effect of patch density, ionic strength, shear rate, and surface roughness on the particle dynamics. The regions in parameter space that give rise to each of these characteristic dynamic adhesion signatures have been quantified, which can enable colloidal-scale objects to be detected and distinguished in real time.

The modeling results provide a quantitative understanding of the particle-wall interactions that will guide the next generation of material surface designs and nano-textures. Spatial fluctuations in the local nano-feature density due to the random deposition process are crucial to the observed dynamic adhesion behavior, which was found not to occur for ordered heterogeneity of the same length scale. These results demonstrate a novel way to control particle-wall contact, which may find applications in microfluidic sorting devices and sensors. The computed “adhesion state diagram” is similar to that found for leukocyte rolling and adhesion, which illustrates the potential relevance of this work to pattern recognition and adhesion in biological systems.