Researcher(s)
- Francesco Barone, Chemical Engineering, University of Delaware
Faculty Mentor(s)
- Eric Furst, Chemical Engineering, University of Delaware
Abstract
Microrheology measures soft material flow and deform at the microscopic level. This field has become important in studying gels, emulsions, living cells, and biological fluids, where mechanical properties play a central role in function, stability, and transport. Traditional methods for recording microscopy rely on expensive cameras like CCDs and CMOS. I used a cost-effective platform and developed code for passive microrheology using a smartphone camera. This allows us to measure viscosity through the Brownian motion of 2 µm polystyrene (PS) tracer particles suspended in complex fluids.
The smartphone is mounted using an Amazon XYZ phone mount, clamped to the microscope eyepiece. Imaging is performed through a 40× objective, and spatial calibration is achieved by imaging a micrometer slide and applying Python code to determine a micron-to-pixel conversion.
Sample channels are made by creating a chamber between a glass slide and coverslip using double-sided tape as a spacer. The edges are sealed with epoxy to prevent evaporation and flow. Videos are captured using consistent zoom and camera settings. Particle tracking is performed in Python using trackpy, followed by subpixel localization, physical filtering, and drift correction. A bootstrap strategy is used for ensemble mean squared displacement (MSD) computation, providing statistical confidence and filtering for diffusive motion.
The platform was validated in water with 2 µm PS tracer particles, where MSD slopes approached α = 0.95 and viscosity estimates agreed with standard values (~1.3 mPa·s). We are currently adapting the code to measure a 40 wt% glycerol-water mixture containing SDS to stabilize suspension. Higher viscosity samples present unique challenges due to increased drag of the particles and other environmental factors under investigation. We are currently improving our platform to handle these challenges.
Preliminary results suggest smartphones can perform passive microrheology with potential applications in labs and education.