(7bx) Nanorheology of Entangled Polymer Melts

Microrheology is a powerful technique to measure the viscoelasticity of a medium through tracking the motion of embedded probe particles. The particles are often much larger than any structural length scale of the medium, and their motion is coupled to the bulk viscoelasticity. We explore the extension of microrheology to nanorheology, in which nanoparticles (NPs) smaller than or comparable to the structural length scales of the medium are used. Specifically, we study NPs in a melt of entangled polymer. As in conventional microrheology, the generalized Stokes-Einstein relation is employed to extract an effective stress relaxation function G_GSE(t) from the mean square displacement of NPs. G_GSE(t) for different NP diameters are compared with the stress relaxation function G(t) of a pure polymer melt. The deviation of G_GSE(t) from G(t) reflects the incomplete coupling between NPs and the dynamic modes of the melt. For linear polymers, a plateau in G_GSE(t) emerges as d exceeds the entanglement mesh size a and approaches the entanglement plateau in G(t) for a pure melt with increasing d. For ring polymers, as d increases towards the spanning size R of ring polymers, G_GSE(t) approaches G(t) of ring melt with no entanglement plateau.

Research Interests:

I am a scientist in the field of computational and theoretical polymer physics. Below I describe three of my major research interests.

1. In the age of big data, machine learning is an indispensable tool to recognize and quantify patterns in astronomical amount of data. I am enthusiastic about employing machine learning to guide the design of novel polymeric materials. One theme I am interested in is the connections between the self-assembly behavior of polymers and monomeric sequences, such as the sequence of various monomer species in hetero-polymers and the sequence of positively and negatively charged monomers in polyelectrolytes. To enumerate possible monomeric sequences and investigate the role of a specific sequence in polymer self-assembly, one can combine computer simulations and machine learning. Such a computational approach should help the rational design of sequence specific polymeric materials.

2. Mechanical properties are critical to the performance of polymeric materials in artificial replacements for human tissues, additive manufacturing, stretchable electronics, and many other applications. Mechanical response of a polymeric material, particularly during large deformation and fracture, involves many interconnected length scales. The spectrum of length scales spans from individual bond length, polymer entanglement length, and polymer mesh size, to characteristic scales in continuum mechanics, such as the size of process zone ahead of a crack tip, the size of irreversible plastic deformation zone, and the dimensions of the entire sample. The multi-scale nature of polymer mechanics poses a great challenge to computational and theoretical research. To tackle this problem, one can construct a multi-scale framework to explicitly model a polymeric material. The ambitious goal is to model or simulate mechanical processes at their respective length scales and seamlessly couple them across multiple scales.

3. The future of polymer physics is arguably in the shift of research focus from synthetic polymers to biopolymers. One paradigmatic example of biopolymers is actin filament, which is a linear polymer of actin subunits. Actin filaments occur in a living cell and form a network with cross-linkers between them. The network of actin filament affects the shape, mechanical response and migration of a living cell, and thus has important ramifications for the functions of the cell. Two features characterize an actin filament network. One is reversibility resulting from the binding/unbinding kinetics of the cross-linkers. The other is activeness associated with the directed motion of some cross-linkers along actin filaments. I am interested in conducting computational research on the network of actin filaments. The computer simulations will be based on a molecular model that explicitly describes both reversibility and activeness. The simulations will provide unparalleled microscopic information often not easily accessible in experiments.

Teaching Interests:

One course I plan to design and teach is Soft Matter Physics. The aim of the course is to provide an introduction into the physics underlying a broad range of soft matter materials such as polymer, colloids, and liquid crystals. With my extensive research experiences in polymer physics, which is a major field in soft matter science, I am interested in teaching a separate course on Polymer Physics. I will also be prepared to teach other courses in materials science, computational materials, and related areas of chemical engineering, at both undergraduate and graduate levels.

Selected Publications:

“Molecular Dynamics Simulation of Polymer Welding: Strength From Entanglements”, T. Ge, F. Pierce, D. Perahia, G. S. Grest, and M. O. Robbins, Phys. Rev. Lett. 110, 098301 (2013)

“Structure and Strength at Immiscible Polymer Interfaces”, T. Ge, G. S. Grest, and M. O. Robbins, ACS Macro Lett. 2, 882 (2013)

“Strong Selective Adsorption of Polymers”, T. Ge and M. Rubinstein, Macromolecules 48, 3788 (2015)

“Self-Similar Conformations and Dynamics in Entangled Melts and Solutions of Nonconcatenated Ring Polymers”, T. Ge, S. Panyukov, and M. Rubinstein, Macromolecules 49, 708 (2016)

“Nanoparticle Motion in Entangled Melts of Linear and Non-Concatenated Ring Polymers”, T. Ge, J. T. Kalathi, J. D. Halverson, G. S. Grest, and M. Rubinstein, Macromolecules, 50, 1749 (2017)