Advances in atomic force microscopy

This article reviews the progress of atomic force microscopy (AFM) in ultra-high vacuum, starting with its invention and covering most of the recent developments. Today, dynamic force microscopy allows to image surfaces of conductors \emph{and} insulators in vacuum with atomic resolution. The mostly used technique for atomic resolution AFM in vacuum is frequency modulation AFM (FM-AFM). This technique, as well as other dynamic AFM methods, are explained in detail in this article. In the last few years many groups have expanded the empirical knowledge and deepened the theoretical understanding of FM-AFM. Consequently, the spatial resolution and ease of use have been increased dramatically. Vacuum AFM opens up new classes of experiments, ranging from imaging of insulators with true atomic resolution to the measurement of forces between individual atoms.Comment: In press (Reviews of Modern Physics, scheduled for July 2003), 86 pages, 44 figure

Supplement 1. R script for analyzing the effects of environmental variables, linear features (roads, power lines, rivers) and linear feature combinations on moose step selection.

<h2>File List</h2><div> <p><a href="moose.r">moose.r</a> (MD5: eca62b33b44187ca6fe4c82269008b96) </p> </div><h2>Description</h2><div> <p>The R code includes the commands to run a basic ("mcb"), barrier ("mcr"), corridor ("mcor") and a combined ("mco") cox proportional hazard model testing for the effects of environmental variables, linear features (roads, power lines, rivers) and linear feature combinations on moose step selection. The code to calculate QIC values ("QIC.coxph", from Mathieu Basille), a customized predict function for cox proportional hazard models ("prcph2"), the code to reproduce model predictions, graphs and the code for seasonal models is also provided. The code should be run consecutively. </p> <p>The data file to be used with the R-script moose.r to analyze the effects of environmental variables, linear features (roads, power lines, rivers) and linear feature combinations on moose step selection may be obtained from the authors upon request. The data was derived from moose GPS relocation data collected in Central-Norway during 2006-2010. A description of the variables can be found in the R-script moose.r. Please ask the Norwegian Institute for Nature Research (NINA) for consent before using this data for publication. Contact [email protected] and [email protected].<b></b></p> </div