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Relationships Among Structure, Memory, and Ow in Sheared Relationships among structure, memory, and ow in sheared disordered materials Kevin Galloway Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania https://orcid.org/0000-0001-8407-6923 Erin Teich Department of Bioengineering, University of Pennsylvania Xiaoguang Ma University of Pennsylvania Christoph Kammer Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania Ian Graham Department of Physics and Astronomy, University of Pennsylvania Nathan Keim Department of Physics, Pennsylvania State University Celia Reina Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania Douglas Jerolmack Department of Earth and Environmental Science, University of Pennsylvania Arjun Yodh University of Pennsylvania https://orcid.org/0000-0003-4744-2706 Paulo Arratia ( [email protected] ) University of Pennsylvania Article Keywords: disordered solids, microscopy, microstructural properties, rheological response Posted Date: May 26th, 2021 DOI: https://doi.org/10.21203/rs.3.rs-523661/v1 License: This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License 1 Relationships among structure, memory, and flow in 2 sheared disordered materials 1 2 3 1 3 3 K.L. Galloway ,E.G.Teich, X-g Ma ,Ch.Kammer,I.R.Graham & N.C. Keim4,C.Reina1, D.J. Jerolmack5,1, A. G. Yodh3, P.E. Arratia∗,1 1Department of Mechanical Engineering and Applied Mechanics, University of Pennsylvania 2Department of Bioengineering, University of Pennsylvania 3Department of Physics and Astronomy, University of Pennsylvania 4Department of Physics, Pennsylvania State University 5Department of Earth and Environmental Science, University of Pennsylvania 4 May 13, 2021 5 Abstract 6 A fundamental challenge for disordered solids is predicting macroscopic yield from the 7 microscopic arrangements of constituent particles. Yield is accompanied by a sudden and 8 large increase in energy dissipation due to the onset of plastic rearrangements. This sug- 9 gests that one path to understanding bulk rheology is to map particle configurations to 10 their mode of deformation. Here, we perform laboratory experiments and numerical sim- 11 ulations that are designed to do just that: 2D dense colloidal systems are subjected to 12 oscillatory shear, and particle trajectories and bulk rheology are measured. We quantify 13 particle microstructure using excess entropy. Results reveal a direct relation between ex- 14 cess entropy and energy dissipation, that is insensitive to the nature of interactions among 15 particles. We use this relation to build a physically-informed model that connects rheology 16 to microstructure. Our findings suggest a framework for tailoring the rheological response 17 of disordered materials by tuning microstructural properties. 18 1 Introduction 1 19 Disordered solids are ubiquitous. They are found, for example, in our foods as pastes and gels , and amidst our 2 3,4 20 homes in the form of concrete and mud . Frustratingly, these materials can experience sudden mechanical failure, 21 such as the collapse of soil during rapid mudslides. Indeed, when sufficiently stressed, all disordered materials exhibit 22 a swift decrease in ability to support load. In the vicinity of this “yield” transition, the solid material shifts from 23 a state wherein energy is stored via internal elastic forces, to a state in which energy is dissipated via irreversible 5–9 24 plastic rearrangements . Microscopic spatiotemporal features are associated with this yield transition and affect 25 macroscopic material responses such as ductile versus brittle behavior. Unfortunately, in contrast to the case for 26 crystalline materials, our ability to predict and control yield in disordered solids based on their constituents and their 6,10 27 interactions is still limited . To build such microstructural models, we need to identify key microscopic metrics 11 12–14 28 relevant to plasticity in disordered materials . Recently, excess entropy has been explored for this purpose .In 15–18 29 equilibrium systems, excess entropy has been utilized to connect viscosity with interparticle structure .Recently 30 in far-from-equilibrium systems, excess entropy scaling has been shown to facilitate a relationship between microscopic 12–14 31 structure and dynamics . Thus, excess entropy offers an untapped signature for plasticity and a potential tool 32 for modeling the mechanical response of disordered solids. 1 19 33 The study of rheology and particle dynamics in disordered systems has a venerable history . As a result of 11 34 this research, theories have proliferated in recent decades. Two of the most successful are Mode Coupling Theory, 20,21 35 wherein the interplay of dynamical modes causes the emergence of rearrangements , and Shear Transformation 19,22–24 36 Zone theory, which posits that local configurations determine where rearrangements occur . More recently, 18,25–27 37 structural signatures for rearrangement have been revealed by machine learning approaches , by study of low- 7,28–32 33 34 38 frequency excitations , and via local yield stress and near-neighbor cage dynamics .Despitetheirusefulness, 20,35 39 difficulties remain in applying these theories to experiments because of the need for fitting parameters and the 34 40 use of empirical relations that are difficult to measure. Moreover, these theories typically do not account for 41 history-dependent behavior such as material memory, which is necessary to understand plasticity. 42 Generally, disordered materials contain memories, i.e., microscopic signatures related to how the material has 36–42 43 been processed . Memory of a previous shearing direction, for example, can be encoded into a material’s response; 44 once a material is sheared sufficiently in a given direction, continued shear in that direction requires more force than 37,43 45 in the opposite direction . In jammed systems, recent experiments and simulations have studied formation of 46 directional memory at low strain amplitudes, both below and near the yield transition; far above yield, memories 44–46 47 are erased . These observations, in turn, raise important new questions: do memories require elastic storage? 48 Is plasticity synonymous with erasure? How do these phenomena manifest during yield, e.g., in storage and loss 49 moduli? 50 In this contribution, we utilize excess entropy to quantify material memory and construct a microstructural 51 model for disordered-material response and energy dissipation. Experiments and simulations show that three non- 52 dimensional parameters govern the connections between microstructure and bulk rheology: packing density, a nor- 53 malized (non-dimensional) form of the imposed stress, and an excess entropy (microstructure-related) ratio that 54 quantifies the material’s ability to retain information about its initial state. Our results confirm that memory is 55 stored elastically and lost plastically, and show how yield and the ductile/brittle response emerge from knowledge 56 about particle configurations at the microscopic scale. 57 2 Results 58 The experiments investigate disordered solids. The solids are colloidal monolayers of athermal, spherical particles (∼ 59 40,000) adsorbed at an oil-water interface (Fig. 1a). The charged particle surfaces generate a dipole-dipole repulsion 60 between particles. This repulsion is strong enough to jam the entire material, arresting particle motions. To probe 61 the effects of disorder, we study both mono-disperse and bi-disperse spherical particle systems with diameters of 62 5.6 µm and 4.1 µm-5.6 µm, respectively. In the bi-disperse system, crystalline domains tend to be much smaller 63 (See Supplementary Materials). We impose many cycles of sinusoidal stress on these samples using a custom- 47 64 made interfacial stress rheometer that permits measurement of the bulk response of the colloidal monolayer while 65 simultaneously recording trajectories of individual particles (see Methods). Cyclic stress is quasi-static, insofar as 66 the time scale for a completion of a rearrangement (∼0.5s) is much shorter than the shortest driving period (5s) or 67 largest inverse strain rate (20s). 68 We investigate particle rearrangements by identifying non-affine deformations within each particle’s neighbor- 23,47 69 hood . The degree of non-affinity is quantified by the mean-squared displacement after subtracting the best ffi 2 70 fit a ne transformation, Dmin (see references [23, 47] for more information). Within cyclically sheared disordered 71 materials, two types of non-affine events occur (Fig. 1a): those wherein particles return to their original position at 72 the end of a strain cycle but along different paths, and those wherein particles escape their nearest neighbors and 48–52 2 ± 2 2 2 2 73 do not return . For visualization we define Dmin,C ≡ (Dmin,R) +(Dmin,E) ,wherethesubscriptsreferto q 2 74 returning (R) and escaping (E) events, respectively, and sign corresponds to the greater Dmin. Both types of events 48–53 75 dissipate energy . Returning non-affine events are known to emerge near the yield point when elasticity begins 8,47 47 76 to diminish and plasticity starts to increase ; escaping events arise well beyond yield (Fig. 1b). The fraction 77 of particles undergoing non-affine events is fd. By following the rearrangements, we develop understanding about 78 trajectory dynamics within the microstructure, and we take steps towards our ultimate goal to relate microstructure 79 to rheology. 80 To quantify
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