Opentrons for automated and high-throughput viscometry

We present an improved high-throughput proxy viscometer based on the Opentrons (OT-2) automated liquid handler. The working principle of the viscometer lies in the differing rates at which air-displacement pipettes dispense liquids of different viscosities. The operating protocol involves measuring the amount of liquid dispensed over a set time for given dispense conditions. Data collected at different set dispense flow rates was used to train an ensemble machine learning regressor to predict Newtonian liquid viscosity in the range of 20-20 000 cP, with similar to 450 cP error (similar to 8% relative to sample mean). A phenomenological model predicting the observed trends is presented and used to extend the applicability of the proxy viscometer to simple non-Newtonian liquids. As proof-of-concept, we demonstrate the ability of the proxy viscometer to characterize the rheological behavior of two types of power-law fluids.