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High-throughput microrheology of polymer solutions and gels

ORAL · Invited

Abstract

Passive probe microrheology has become a popular method for characterizing viscoelasticity on small fluid samples, and holds significant potential for informing rheological design over a wide formulation space with limited material. Realizing this potential will require automated, high throughput data acquisition and analysis. Here, we report a new method for extracting microrheology information using differential dynamic microscopy (DDM). Using Fourier-domain analysis of video images, DDM can extract the mean-squared displacement in systems that would otherwise be difficult to measure using conventional particle tracking. Combining DDM with downsampling by Gaussian process regression, we demonstrate that DDM microrheology can be performed in real time. This rapid acceleration is leveraged to integrate fully automated sample preparation, data acquisition and analysis to demonstrate autonomous, high-throughput microrheology characterization. We illustrate the utility of high-throughput microrheology through two examples – in situ characterization of viscosity during polyelectrolyte coacervation, and kinetic profiling of gelation in protein solutions. The results highlight the considerable promise of automated microrheology to aid in the design of polymeric fluids and soft solids.

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Presenters

  • Matthew E Helgeson

    University of California, Santa Barbara, 1 Department of Chemical Engineering, University of California Santa Barbara, Department of Chemical Engineering and Materials Research Laboratory, University of California, Santa Barbara, 93106, United States

Authors

  • Matthew E Helgeson

    University of California, Santa Barbara, 1 Department of Chemical Engineering, University of California Santa Barbara, Department of Chemical Engineering and Materials Research Laboratory, University of California, Santa Barbara, 93106, United States

  • Yimin Luo

    University of California, Santa Barbara

  • Alexandra V Bayles

    University of California, Santa Barbara

  • Yuekun Heng

    3 Department of Statistics and Probability, University of California Santa Barbara

  • Maneesh K Gupta

    4 Air Force Research Laboratory, Wright-Patterson AFB, Air Force Research Laboratory, WPAFB

  • Todd M Squires

    University of California, Santa Barbara

  • Megan T Valentine

    University of California, Santa Barbara

  • Matthew E Helgeson

    University of California, Santa Barbara, 1 Department of Chemical Engineering, University of California Santa Barbara, Department of Chemical Engineering and Materials Research Laboratory, University of California, Santa Barbara, 93106, United States