Time-rate-transformation framework for targeted assembly of short-range attractive colloidal suspensions

Abstract

The aggregation of attractive colloids has been extensively studied from both theoretical and experimental perspectives as the fraction of solid particles is changed, and the range, type and strength of attractive or repulsive forces between particles varies. The resulting gels consisting of disordered assemblies of attractive colloidal particles, have also been investigated with regards to percolation, phase separation, and the mechanical characteristics of the resulting fractal networks.Despite tremendous progress in our understanding of the gelation process, and the exploration of different routes for arresting the dynamics of attractive colloids, the complex interplay between convective transport processes and many-body effects in such systems has limited our ability to drive the system towards a specific configuration. Here we study a model attractive colloidal system over a wide range of particle characteristics and flow conditions undergoing aggregation far from equilibrium. The complex multiscale dynamics of the system can be understood using a Time-Rate-Transformation diagram adapted from understanding of materials processing in block copolymers, supercooled liquids and much stiffer glassy metals to direct targeted assembly of attractive colloidal particles.Main-The physical and mechanical properties of soft condensed matter generally, and colloidal systems specifically, are directly governed by their underlying microstructure. The diversity of

Nonlinear Mechanics of Colloidal Gels: Creep, Fatigue, and Shear-Induced Yielding

“…By increasing the attraction strength, one can continuously go from a fluid state to a gel state. Another common way to control the gel state consists in playing with the shear history experienced by the system during or after gelation [Ovarlez et al, 2013, Koumakis et al, 2015, Helal et al, 2016, Narayanan et al, 2017, Hipp et al, 2019, Jamali et al, 2020. By tuning the way a colloidal gel is sheared or mixed, one may vary its terminal structure and mechanical strength without changing the Figure 1: Schematic state diagram for colloidal particles with short-range attraction.…”

Life and death of colloidal bonds control the rate-dependent rheology of gels

“…This coupling has been studied within the context of soft glassy rheology 19 – 21 . Recently, detailed simulations of gelation under flowing conditions have shown that structures that are formed under flow significantly differ from ones that are formed in quiescent conditions, indicating a strong role to be played by the fluid flow in defining the resulting particulate structures 22 . These spatially distributed particle structures exhibit a wide range of exotic rheological responses from an emergence of a yield stress under which the material behaves similar to a viscoelastic solid, to time- and rate-dependent flows that are referred to as thixotropic elasto-visco-plastic (TEVP) 19 , 23 – 28 .…”

“…Understanding the micromechanistic view of the physical underpinnings in these complex rheological behavior is essential in controlling the mechanics of colloidal gels. Recent advances in scattering techniques 29 – 31 as well as detailed computer simulations 22 , 23 , 32 – 34 have brought an invaluable insight into understanding the evolution of the colloidal structures under different flowing conditions. A focal point of many studies have been the fundamentals of yielding in colloidal gels 35 .…”

“…There is a growing body of literature now suggesting that it is the competition between the interparticle attractive forces and the hydrodynamic shearing forces that determine the state of the particulate microstructure and hence the mechanical response of a colloidal gel 14 , 22 , 23 , 31 , 34 , 36 – 39 . This competition can be understood using a dimensionless group referred to as the Mason number, ( ), in which the η 0 , , a , and U 0 are solvent viscosity, applied deformation rate, particle radius, and the strength of attractive potential between the particles.…”

Rheology-Informed Neural Networks (RhINNs) for forward and inverse metamodelling of complex fluids

“…Thixotropy observed in many complex fluids generally manifests in the sensitivity of the viscosity to the history of the applied strain rate 17 – 19 . Thixotropic effects originate from evolution of the material’s microstructure as a result of the interplay between shearing forces exerted by the flow and the natural structure formation 20 – 22 . Thus, in thixotropic constitutive equations, one will critically need to solve for the time evolution of a structure parameter under flow.…”