Advancements in the Extraction of Viscoelastic Information from Soft and Biological Samples using the Atomic Force Microscope
Open AccessAtomic force microscopy (AFM) techniques have provided increasingly important insights into surface morphology, mechanics, and other critical material characteristics at the nanoscale. One attractive implementation involves extracting meaningful material properties, which demands physically accurate models specifically designed for AFM experimentation and simulation. The AFM community has pursued the precise quantification and extraction of rate-dependent material properties for a significant period of time, attempting to describe the standard viscoelastic response of materials. AFM static force spectroscopy (SFS) is one approach commonly used in pursuit of this goal. It is capable of acquiring rich temporal insight into the behavior of a sample. This dissertation seeks to build upon previous work and proposes a novel indentation framework that can be used to extract useful linear and nonlinear viscoelastic information from AFM-SFS experiments. In addition, several practical verification studies are presented with application to soft and biological samples, before the performance of both indentation frameworks is tested on experimental skin cell data. The first section of this work begins with a guided discussion that develops a fit function from fundamental laws, continues with conditioning a raw SFS experimental dataset, and concludes with the fit and prediction of viscoelastic response parameters such as storage modulus, loss modulus, and loss angle for Nylon 6,6. The second section focuses on the derivation of a novel indentation framework and discusses both useful applications and limitations. Afterward, verification studies are presented which are relevant to a wide range of viscoelastic samples (cells, tissues, polymers, etc.). Finally, results from the parameterization of experimental skin cell data is introduced to prove that both frameworks can be used for cells. These steps constitute a complete guide to leveraging AFM-SFS data to estimate key material parameters using multiple approaches, with a detailed review of both the overall methodology and supporting analytical choices.
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