ABSTRACT
The properties of clouds, such as their reflectivity or their likelihood to precipitate, depend on whether the cloud droplets are liquid or frozen. Thus, understanding the ice nucleation mechanisms is essential for the development of reliable climate models. Most ice nucleation in the atmosphere is heterogeneous, i.e., caused by ice nucleating particles such as mineral dusts or organic aerosols. In this regard, K-feldspar minerals have attracted great interest recently as they have been identified as one of the most important ice nucleating particles under mixed-phase cloud conditions. The mechanism by which feldspar minerals facilitate ice nucleation remains, however, elusive. Here, we present atomic force microscopy (AFM) experiments on microcline (001) performed in an ultrahigh vacuum and at the solid-water interface together with density functional theory (DFT) and molecular dynamics (MD) calculations. Our ultrahigh vacuum data reveal features consistent with a hydroxyl-terminated surface. This finding suggests that water in the residual gas readily reacts with the surface. Indeed, the corresponding DFT calculations confirm a dissociative water adsorption. Three-dimensional AFM measurements performed at the mineral-water interface unravel a layered hydration structure with two features per surface unit cell. A comparison with MD calculations suggests that the structure observed in AFM corresponds to the second hydration layer rather than the first water layer. In agreement with previous computation results, no ice-like structure is seen, questioning an explanation of the ice nucleation ability by lattice match. Our results provide an atomic-scale benchmark for the clean and water-covered microcline (001) plane, which is mandatory for understanding the ice nucleation mechanism on feldspar minerals.
ABSTRACT
Scanning probe microscopy (SPM) techniques are widely used to study the structure and properties of surfaces and interfaces across a variety of disciplines in chemistry and physics. One of the major artifacts in SPM is (thermal) drift, an unintended movement between sample and probe, which causes a distortion of the recorded SPM data. Literature holds a multitude of strategies to compensate for drift during the measurement (online drift correction) or afterwards (offline drift correction). With the currently available software tools, however, offline drift correction of SPM data is often a tedious and time-consuming task. This is particularly disadvantageous when analyzing long image series. Here, we present unDrift, an easy-to-use scientific software for fast and reliable drift correction of SPM images. unDrift provides three different algorithms to determine the drift velocity based on two consecutive SPM images. All algorithms can drift-correct the input data without any additional reference. The first semi-automatic drift correction algorithm analyzes the different distortion of periodic structures in two consecutive up and down (down and up) images, which enables unDrift to correct SPM images without stationary features or overlapping scan areas. The other two algorithms determine the drift velocity from the apparent movement of stationary features either by automatic evaluation of the cross-correlation image or based on positions identified manually by the user. We demonstrate the performance and reliability of unDrift using three challenging examples, namely images distorted by a very high drift velocity, only partly usable images, and images exhibiting an overall weak contrast. Moreover, we show that the semi-automatic analysis of periodic images can be applied to a long series containing hundreds of images measured at the calcite-water interface.
