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STIFFNESS CALIBRATION OF ATOMIC FORCE MICROSCOPY PROBES UNDER HEAVY FLUID LOADING

<p>This research presents new calibration techniques for the characterization of atomic force microscopy cantilevers. Atomic force microscopy cantilevers are sensors that detect forces on the order of pico- to nanonewtons and displacements on the order of nano- to micrometers. Several calibration techniques exist with a variety of strengths and weaknesses. This research presents techniques that enable the noncontact calibration of the output sensor voltage-to-displacement sensitivity and the cantilever stiffness through the analysis of the unscaled thermal vibration of a cantilever in a liquid environment.</p><p>A noncontact stiffness calibration method is presented that identifies cantilever characteristics by fitting a dynamic model of the cantilever reaction to a thermal bath according to the fluctuation-dissipation theorem. The fitting algorithm incorporates an assumption of heavy fluid loading, which is present in liquid environments.</p><p>The use of the Lorentzian line function and a variable-slope noise model as an alternate approach to the thermal noise method was found to reduce the difference between calibrations preformed on the same cantilever in air and in water relative to existing techniques. This alternate approach was used in combination with the new stiffness calibration technique to determine the voltage-to-displacement sensitivity without requiring contact loading of the cantilever.</p><p>Additionally, computational techniques are presented in the investigation of alternate cantilever geometries, including V-shaped cantilevers and warped cantilevers. These techniques offer opportunities for future research to further reduce the uncertainty of atomic force microscopy calibration.</p> / Dissertation

Identiferoai:union.ndltd.org:DUKE/oai:dukespace.lib.duke.edu:10161/3121
Date January 2010
CreatorsKennedy, Scott Joseph
ContributorsClark, Robert L, Cole, Daniel G
Source SetsDuke University
Detected LanguageEnglish
TypeDissertation

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