Nanomechanical and viscoelastic measurements in biological Atomic Force Microscopy (AFM)Thursday (29.09.2016) 11:30 - 11:45 Part of:
The atomic force microscope (AFM) has become a standard tool in the biological sciences largely due to its ability to acquire high resolution images of native structures (molecules, cells, tissues) under fluid in near-physiological conditions. Its integration with an inverted optical microscope allows the AFM to be combined with such advanced techniques as epifluorescence, confocal, and TIRF which further enable researchers to use optical microscopy to precisely navigate the AFM probe to desired locations, acquire images of surface (or near-surface) features and then correlate them with labeled structures. In addition to imaging, AFMs can quantify the mechanical properties of a wide variety of biological samples providing novel insights to both cell function and cell-substrate interactions. This aspect is important for biological research as our appreciation of the role of mechanics in biological structure and function has been steadily increasing over the past few decades. Understanding this role is especially pertinent in the field of cell biology. It has been found that the geometrical and mechanical properties of the extracellular microenvironment are important in such processes as cancer, cardiovascular disease, muscular dystrophy, and cell life/death. Further expanding the role of AFM in this field are viscoelastic or dynamic measurements which allows for a model-independent approach to quantifying the dissipation of a material. It is becoming increasingly important to explore the rate dependence of these measurements as they appear to be nonlinear across a given frequency sweep. We will discuss the progress of AFM based modulus and viscoelastic measurements, including the performance envelope of the techniques and their associated data analyses. For model-based analyses, many factors must be considered including selecting the appropriate model and using accurate values for such parameters as tip geometry, contact point and sample Poisson ratio. Examples of the effect of different model assumptions will be shown.