2022/11/17 by Matthew G. Smith, Smith, Matthew G., Jack Radford +15
Medicine · #Blood properties and coagulation #FOS: Physical sciences #Fluid Dynamics (physics.flu-dyn)
paper · pdf · doi:10.48550/arxiv.2211.09689
openalex publication_date 2022/11/17 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
It has been argued [Tassieri, Soft Matter, 2015, 11, 5792] that linear microrheology with optical tweezers (MOT) of living systems ``is not an option'', because of the wide gap between the observation time required to collect statistically valid data and the mutational times of the organisms under study. Here, we have taken a first step towards a possible solution of this problem by exploiting modern machine learning (ML) methods to reduce the duration of MOT measurements from several tens of minutes down to one second. This has been achieved by focusing on the analysis of computer simulated trajectories of an optically trapped particle suspended in a set of Newtonian fluids having viscosity values spanning three orders of magnitude, i.e. from 10-3 to 1 Pa⋅s. When the particle trajectory is analysed by means of conventional statistical mechanics principles, we explicate for the first time in literature the relationship between the required duration of MOT experiments (Tm) and the fluids relative viscosity (ηr) to achieve an uncertainty as low as 1%; i.e., Tm≅ 17ηr3 minutes. This has led to further evidences explaining why conventional MOT measurements commonly underestimate the materials' viscoelastic properties, especially in the case of high viscous fluids or soft-solids such as gels and cells. Finally, we have developed a ML algorithm to determine the viscosity of Newtonian fluids that uses feature extraction on raw trajectories acquired at a kHz and for a duration of only one second, yet capable of returning viscosity values carrying an error as low as ∼0.3% at best; hence the opening of a doorway for MOT in living systems.