(467d) Modeling the Dynamic Adhesion of Colloidal Particles In a Biomimetic System Based on Nanotextured Surfaces

Davis, J. M., University of Massachusetts, Amherst
Duffadar, R. D., University of Massachusetts

An interpretive and predictive computational model has been developed to understand interfacial motion signatures of colloidal particles in flow 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 cationic polymer that form 10 nm disks when adsorbed to the collecting surface) create nanometer scale chemical heterogeneity, or surface ?texture.? Although arranged randomly on the surface for consistency with the actual deposition process, these patches contains hidden length scales capable of selectivity, pattern recognition, motion control, and adhesion of much larger objects. Simulations of the dynamic adhesion of colloidal particles on these surfaces is used to probe the fundamental variables that govern this behavior, including the density of surface functional groups, their arrangement in the plane of a surface, their binding strength, and the role of the repulsive background field, particle size, and shear rate.

The theoretical model couples hydrodynamic forces and torques exerted by the fluid to the spatially varying colloidal landscape presented by the nano-textured surfaces to simulate the particle-wall interactions in this system. Imperfections on the surface are taken into account through the inclusion of surface roughness, which gives rise to contact and frictional forces that enable the investigation of particle motion not only in solution but also upon contact with the surface.

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. The simulations track the particle trajectories and velocities in the system, showing excellent quantitative agreement with the experimental observation of threshold adhesion behavior and particle deposition rate vs. patch density on the surface. The model is further used to study the effect of variations in shear rate, particle size, and ionic strength of the system.

Multi-variable phase-space maps, or ?adhesion state diagrams,? are constructed from simulation results to outline the domains of different motion signatures, including no contact, rolling, skipping, and arrest, and exhibit quantitative agreement with experimental results. These phase-space maps illustrate how materials created from the random deposition of nanoconstructs can be used to control particle-wall contact, which may find applications in micro-fluidic sorting devices and sensors. Furthermore, the computed adhesion state diagrams show qualitative agreement with the state diagrams found for leukocyte rolling and adhesion despite the absence of physical bonds between the particles and surface, which further illustrates the relevance of this work to biomimetic pattern recognition and adhesion.

Spatial fluctuations in the local nano-feature density due to the random deposition process are crucial to the observed dynamic adhesion behavior. The critical number of patches present within the zone of influence (ZOI) of colloidal forces that are required to capture a particle is computed and provides insight into the effect of patch strength in controlling particle dynamics. Radial distribution functions are computed and used to assess the spatial arrangement of patches within the ZOI. The history of the electrostatic energy landscape experienced by a captured particle, and not merely the patch distribution beneath the particle at capture, is shown to govern the adhesion behavior. A comparison of the average binding force and energy per adhesive nanoconstruct is made with analogous parameters in biological systems.