60th Annual Report on Research 2015

Abstract:

The project, which aims to understand the connection between microstructure and yielding of amorphous materials, has advanced in two main direction in the past year. First, we have developed techniques and performed experiments of shearing Brownian colloidal particles (1μm size). These experiments are designed to study the (ir)reversibility of particle motion under influence of both cyclic shear and thermal activation. Secondly, using previously gathered data on non-thermal particles and in collaboration with Prof. Andrea Liu (Physics, Penn) we have developed a machine-learning scheme to probe and predict ÒsoftnessÓ in our system when the system undergoes the so-called yielding transition.

Narrative:

The question of how a disordered material's microstructure translates into macroscopic mechanical response is central to the understanding and design of materials like pastes, foams, and metallic glasses. Here, we use a model colloidal suspension that is known to undergo a yielding transition under cyclic shear if the shear amplitude exceeds a critical value. Below the yielding point, the microstructure (i.e. particles) undergoes reversible limit cycles after each complete cycle, and the response is elastic. Above this limit, the particle trajectories become irreversible for complete cycles, and plastic behavior starts to develop. Our own previous investigations show that very close to yielding point, these reversible limit cycles demonstrate plastic deformations [1,2,3]. In other words, the limit cycles exhibit hysteresis in the trajectories, and has a considerable high non-affine deformation. This behavior starts just bellow the yielding point, and continues to affect the reversible limit cycles above the yield transition point.

We are now using Brownian particles to probe thermal effects on yielding and reversibility, and make connection to our previous studies on non-Brownian particles. The main question to be addressed is: Is there such a plastically reversible regime, when the particles become Brownian? In order to tackle the problem, we directly track particles through entire cyclic shear experiments, while performing rheology measurements. Tracking particles enables us to understand the changes in microstructure during yielding transition. Also of interest is the interplay between particle transport and thermal diffusivity. While the temperature is kept constant in our experiments, we can change the packing fraction to tune the distance from the glass transition point.

Recently, there has been much effort to understand and characterize the relationship between structure and dynamics. In particular, we would like to find the ÒweakestÓ regions or spots in a material that would be the most likely to experience re-arrangements once the material is sheared. A relevant method is through mode analysis of the system, which can identify the softest modes as the weakest regions of the disordered material [4]. An alternative method to identify and characterize such Òsoft-spotsÓ is via the use of machine-learning techniques, recently proposed by the group of Andrea Liu at Penn. We are collaborating with Andrea on using her novel machine-learning techniques on our experimental data.

Advances on experimental front: In our new sets of experiments, we adsorb Brownian colloidal particles, 1-μm and 0.7-μm in size, at a decane-water interface. We then perform cyclic shear with our custom-made interfacial stress rheometer.  The particles are sulfate-treated latex spheres, which are stabilized and have a nearly uniform negative charge distributed on their surface, and hence, are purely repulsive. When assembled at the interface, they model a thermal glass. The set up is very similar to the previous version used for larger particles (~6 μm). It consists of two parallel Helmholtz coils, which magnetize a needle (stainless steel) that is carefully placed at the interface and is confined between two parallel walls. Then, by applying an extra oscillatory current, we produce cyclic shear.

By reducing the size of particles, we face many experimental challenges such as amplified vibration per particle size, less stable interface, and difficulty with uniformly dispersing the particles. We were able to modify the dispersion protocol (by replacing Ethanol with Isoproryl Alcohol), as well as the rheometer cell (a shallower cell which suppresses Taylor vortexes), such that problems related to vibration and particle dispersion are alleviated. We were able to successfully perform experiment on mono-disperse and bi-disperse systems and we are in the stage of post-processing the data.

We have also implemented machine-learning techniques as a powerful tool to study “softness” in our colloidal systems. As a brief review of this technique, we use a subset of shearing experiment (below yielding point) for training a SVM. We build a large group of functions, which characterizes the structure of each particle's environment. Then we classify the particles to two groups based on a measure of non-affine displacement. Once we have this classifying hyper-plane, we define the softness as the distance to the hyper-plane of each particle is a new experimental dataset. We will then study the evolution of softness, when the system undergoes yielding transition.

Impact: The funds provided by ACS-PRF have been instrumental in the education and scientific training of undergraduate and graduate students. Funds have been leveraged so that the PI could involve 1 undergraduate student, 2 Master students (females), 1 doctoral student, and 1 postdoctoral research associate (female). Funds have also been used for conference support for the postdoc at APS-March Meeting, where preliminary findings were presented. We will be also presenting at the upcoming (November) APS-DFD in Boston. The PI has been able to obtain important preliminary data and will be applying for larger grants to continue to support this line of work.

References:

[1] Nathan C. Keim & Paulo E. Arratia, Soft Matter, 2013, 9, 6222-6225.

[2] Nathan C. Keim and Paulo E. Arratia, Phys. Rev. Lett. ,2014, 112, 028302.

[3] Nathan C. Keim and Paulo E. Arratia, Soft Matter, 2015, 11, 1539-1546.

[4] E. D. Cubuk, S. S. Schoenholz, J. M. Rieser, B. D. Malone, J. Rottler, D. J. Durian, E. Kaxiras, and A. J. Liu, Phys. Rev. Lett., 2015, 114, 108001