Paper Number
RS24 

Session
Techniques and Methods: Rheometry & Spectroscopy/Microscopy

Title
Automated, high-throughput microrheology for material formulation

Presentation Date and Time
October 12, 2022 (Wednesday) 1:30

Track / Room
Track 6 / Mayfair

Authors (Click on name to view author profile)

  1. Luo, Yimin (University of California Santa Barbara)
  2. Bayles, Alexandra V. (University of Delaware, Chemical and Biomolecular Engineering)
  3. Gu, Mengyang (University of California Santa Barbara)
  4. He, Yue (University of California Santa Barbara)
  5. Martineau, Rhett (Air Force Research Laboratory)
  6. Gupta, Maneesh (Air Force Research Laboratory)
  7. Squires, Todd (University of California Santa Barbara)
  8. Valentine, Megan T. (University of California Santa Barbara)
  9. Helgeson, Matthew E. (University of California, Santa Barbara, Chemical Engineering)

Author and Affiliation Lines (in printed abstract book)
Yimin Luo1, Alexandra V. Bayles1, Mengyang Gu1, Yue He1, Rhett Martineau2, Maneesh Gupta2, Todd Squires1, Megan T. Valentine1 and Matthew E. Helgeson1
1University of California Santa Barbara, Santa Barbara, CA, CA 93105; 2Air Force Research Laboratory, Dayton, OH

Speaker / Presenter 
Helgeson, Matthew E.

Keywords
experimental methods; bio-fluids; biomaterials; emulsions; gels; microscopy; ML based; polymer solutions; polymer sustainability

Text of Abstract
Brownian 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 in parallel with sample measurements. 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 several examples: (i) in situ characterization of viscosity during polyelectrolyte coacervation, (ii) kinetic profiling of gelation in protein solutions, and (iii) in situ monitoring of polymerization and depolymerization reactions. The results highlight the considerable promise of automated microrheology to aid the design of chemistries and formulations for complex fluids and soft solids.