Assessing Differential Particle Deformability under Microfluidic Flow Conditions

Assessing the mechanical behavior of nano- and micron-scale particles with complex shapes is fundamental in drug delivery. Although different techniques are available to quantify the bulk stiffness in static conditions, there is still uncertainty in assessing particle deformability in dynamic conditions. Here, a microfluidic chip is designed, engineered, and validated as a platform to assess the mechanical behavior of fluid-borne particles. Specifically, potassium hydroxide (KOH) wet etching was used to realize a channel incorporating a series of micropillars (filtering modules) with different geometries and openings, acting as microfilters in the direction of the flow. These filtering modules were designed with progressively decreasing openings, ranging in size from about 5 down to 1 μm. Discoidal polymeric nanoconstructs (DPNs), with a diameter of 5.5 μm and a height of 400 nm, were realized with different poly(lactic-co-glycolic acid) (PLGA) and poly(ethylene glycol) (PEG) ratios (PLGA/PEG), namely, 5:1 and 1:0, resulting in soft and rigid particles, respectively. Given the peculiar geometry of DPNs, the channel height was kept to 5 μm to limit particle tumbling or flipping along the flow. After thorough physicochemical and morphological characterization, DPNs were tested within the microfluidic chip to investigate their behavior under flow. As expected, most rigid DPNs were trapped in the first series of pillars, whereas soft DPNs were observed to cross multiple filtering modules and reach the micropillars with the smallest opening (1 μm). This experimental evidence was also supported by computational tools, where DPNs were modeled as a network of springs and beads immersed in a Newtonian fluid using the smoothed particle hydrodynamics (SPH) method. This preliminary study presents a combined experimental-computational framework to quantify, compare, and analyze the characteristics of particles having complex geometrical and mechanical attributes under flow conditions.