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Title: Multiscale Mechanical Characterization of Soft Matter
Keywords: Soft matter, Biopolymer, Nonlinear mechanics, Microrheology, Image correlation spectroscopy, Collagen
Issue Date: 5-Aug-2011
Citation: NICHOLAS AGUNG KURNIAWAN (2011-08-05). Multiscale Mechanical Characterization of Soft Matter. ScholarBank@NUS Repository.
Abstract: This thesis presents a phenomenological study of the mechanics of soft matter systems, particularly polymer networks. Due to the length- and time-scale dependence of the mechanical properties of these networks, it is necessary to utilize multiple characterization techniques. Using a combination of bulk mechanical rheology (MR), microscopy, particle tracking microrheology (PTM), image correlation spectroscopy (ICS), as well as numerical simulation, we investigate the interplay between the mechanics of polymer networks at different length and time scales. In the first part of the thesis, we focus on studying the mechanics of collagen networks, a type of biopolymer network that significantly determines the mechanics of biological tissues. Collagen forms highly heterogeneous networks and exhibits strain-dependent mechanical behavior. We systematically dissect the roles of collagen concentration, fiber entanglement, and network connectivity in governing the mechanics at different length scales and strain levels. Based on the results obtained from MR, PTM, and computer simulations, we propose a deformation mechanism that can explain the full spectrum of collagen network mechanical response. Despite the valuable insights gained through the combination of techniques, this work underscores the importance of accounting for system heterogeneity and some of the limitations of existing mechanical characterization techniques. In the second part of the thesis, we develop a novel microrheological technique based on ICS that we call ICS-μR. ICS is an emerging biophysical tool that allows quantitative measurements of the dynamics of imaged fluorescent molecules. We present a mathematical framework for extracting the microrheological information from the correlation data and further extend the capability of ICS to perform dynamic measurement in a probe-independent manner. We validate the method on both Newtonian and complex fluids (homogeneous polymer networks) with various viscoelastic properties. The potential of simultaneously obtaining spatiotemporal measurements and microrheological information from a single set of image data makes ICS-μR a prospective tool in many applications, biological or otherwise.
Appears in Collections:Ph.D Theses (Open)

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