i-Rheo: determining the linear viscoelastic moduli of colloidal dispersions from step-stress measurements

Abstract

The Fourier transform-based method ‘i-Rheo’ allows the accurate determination of material viscoelastic moduli from step-stress measurements.

Yield stress “in a flash”: investigation of nonlinearity and yielding in soft materials with an optofluidic microrheometer

“…In macroscopic and microscopic creep measurements (empty squares and color-coded diamonds, respectively) we find two similar trends with a linear regime (G independent from γ and identical to linear elasticity estimates), followed by a plastic regime. For low stresses, we could also extract the frequencydependent linear viscoelastic moduli from our microscopic creep data through i − Rheo, a method based on Fourier analysis 33,34 . We found weak frequency dependence, in agreement with bulk oscillatory measurements (see Supplementary Fig.…”

i-Rheo-optical assay: Measuring the viscoelastic properties of multicellular spheroids

“…These parameters contribute to a comprehensive understanding of the rheological behaviour of the investigated cellular spheroids. Moreover, we employed the open-access, model-free executable tool named “i-Rheo” [ [44] , [45] , [46] ] to derive the viscoelastic properties of the samples directly from their time-dependent stress and strain curves and conducted comparisons with those obtained from the Burgers model. To corroborate our rheo-optical compression assay, we conducted a similar load test through nanoindentation measurements performed on PANC-1 spheroids and on two biomimetic hydrogels, designed for mimicking biological samples in terms of their mechanical properties [ 11 ].…”

Memory effects in colloidal motion under confinement and driving

“…It is a formidable challenge to parameterise the GLE from observations of a given complex system, that is, estimating ζ(t) from data for the MSD or related correlation functions. Today, several techniques are available for this task, which may roughly be divided into methods based on few-parameter families of ansatz functions to represent the memory function [23][24][25], and into ansatz-free methods in the time domain [26][27][28][29] or, via Fourier transforms, in the frequency domain [29][30][31][32]. Notable recent advances follow a different route and rely on memory estimation using bare trajectory data as input instead of correlation functions [33,34].…”