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High-Speed Atomic Force Microscopy Enables New Applications


Lars Mininni,* Andrea Slade, Johannes Kindt, and Shuiqing Hu Bruker Nano Surfaces Division, 112 Robin Hill Rd, Santa Barbara, CA 93117


* lars.mininni@bruker-nano.com


Introduction Atomic force microscopy (AFM) [1] is one of the most


powerful and dynamic methods for performing nanoscale imaging and materials characterization, enabling scientists and researchers to attain atomic resolution and measure nano-mechanical material properties in-situ, all while requiring minimal sample preparation. In spite of these clear advantages, user adoption of AFM has been limited by the technique’s slow imaging speed as compared to light microscopy, scanning electron microscopy (SEM), and transmission electron microscopy (TEM). However, recent advances in AFM technology have increased AFM imaging speeds by over an order of magnitude [2–4], opening up a wide range of new applications while greatly improving the user experience. One of the most beneficial outcomes of higher-speed


AFMs is the increase in productivity for everyday nanoscale investigation. Reducing the time to first explore a heterogeneous surface, find the region of interest representing the sample’s morphology, and capture a publication-quality image is a clear benefit for scientists. Te time reduction allows researchers to reveal nanometer-level characteristics in minutes instead of hours. High-speed AFM also greatly increases productivity in capturing the large number of images needed to understand the synthesis of materials with statistically valid data quantities. Perhaps the most interesting application that benefits from high-speed AFM is the ability to observe dynamic processes with sufficient time resolution to study phenomena such as crystal formation, protein dynamics, or aging processes. Tis article discusses some examples of these high-speed AFM applications and the new technology that is making them possible.


Sample Exploration A commonplace use of an


AFM involves the exploration of an unknown heterogeneous sample to understand the different morphologies that best represent the surface and to capture a representative set of publication-quality images to document what was found. Especially for complex samples, the majority of imaging time is oſten spent looking at enough sample surface to understand what is important. On a rough


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sample, a faster AFM makes it possible to engage and image more sites in a shorter amount of time. A powerful use of high-speed AFM is to capture a large scan area with a high pixel resolution (for example, 5,000 lines with 5,000 points per line). Normally, such an image would take hours to capture; but, with the new generation of high-speed AFMs, a large-area high-resolution image can be acquired in minutes. A single image that contains spatial information ranging three orders of magnitude (from nanometer scales to tens of micrometer scales) can yield whole new levels of understanding. In addition, the data can then be zoomed into, and representative areas can be magnified and published. An advantage of this method is that the user can decide on the best scale and framing aſter taking the data. By drastically reducing the time investment required to capture a high-resolution image, a high-speed AFM encourages users to explore their samples in this more productive manner (Figure 1).


Screening Applications In screening applications, the types of expected surface


phenomena may be known. Te challenge is to determine the relationships between one or more independent variables and one or more responses or dependent variables. Quantifying these relationships requires efficient imaging of multiple sites on multiple samples and analysis of the responses. Imaging speed is obviously critical to accomplishing this goal, but multi-sample loading and automation, consistent operation


Figure 1: A high-resolution, 16-megapixel image of PTFE polymer film captured by high-speed AFM for later exploration offline.


doi:10.1017/S1551929511001209 www.microscopy-today.com • 2011 November


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