Revisiting Wetting, Freezing, and Evaporation Mechanisms of Water on Copper

Wetting of metal surfaces plays an important role in fuel cells, corrosion science, and heat-transfer devices. It has been recently stipulated that Cu surface is hydrophobic. In order to address this issue we use high purity (1 1 1) Cu prepared without oxygen, and resistant to oxidation. Using the modern Fringe Projection Phase-Shifting method of surface roughness determination, together with a new cell allowing the vacuum and thermal desorption of samples, we define the relation between the copper surface roughness and water contact angle (WCA). Next by a simple extrapolation, we determine the WCA for the perfectly smooth copper surface (WCA = 34°). Additionally, the kinetics of airborne hydrocarbons adsorption on copper was measured. It is shown for the first time that the presence of surface hydrocarbons strongly affects not only WCA, but also water droplet evaporation and the temperature of water droplet freezing. The different behavior and features of the surfaces were observed once the atmosphere of the experiment was changed from argon to air. The evaporation results are well described by the theoretical framework proposed by Semenov, and the freezing process by the dynamic growth angle model.

Figures A and C show the elemental EDS analysis for the initial copper sample (Cu-0-Air). EDS analysis shows that on the surface cleaned with an argon stream there are only traces of corundum, but only in not very numerous fragments of hard-to-penetrate deep grooves, in which the Al2O3 grains were jammed (B). The outer surface of the copper samples shows zero (B1), or within the measurement error, of traces of the element aluminum (compare B2-3 with A and C). EDS analysis also shows a remarkably large amount of the hydrocarbon-derived element C on the sample saturated with Cu-0-Air (A and C) compared to a much smaller amount of this element on the surface freshly exposed by treatment with sandpaper (Cu-2000-Air-0min) (B).
S-4 Figure S3. The relation between temperature and the time of cooling.

Fringe projection phase-shifting method
The contact angle corrected by roughness it is the value of the contact angle on the Young surface.
An implementation of the fringe projection phase-shifting method is giving a unique opportunity to analyze surface roughness and contact angle of the same location of the sample. As a result, the contact angle on the rough surface, flat surface, roughness, and area factor of roughness are determined.
In Fig. S4 and Fig. S5 the principal of the method is presented. Figure S4. Fringe Projection Phase-Shifting schematics (A) and an example of a surface with the projected pattern (B). A sinusoidal pattern is sequentially projected on the sample surface and a camera is utilized to capture the fringe patterns and reconstruct the 3D image by phase-shift coding. Figure S5. In the sinusoidal phase-shifting method a series of phase-shifted sinusoidal patterns are recorded (top), from which the phase information at every pixel is obtained (bottom). The phase shift correlates with sample surface topography from pixel to the pixel that defines the resolution.
As illustrated in Fig. S4 the 3D topography module consists of a projector with a LED light source and a slide with sinusoidal fringe patterns on a grey-scale. These illumination patterns are sequentially projected onto the studied surface, and a digital camera captures the fringe patterns from which the 3D shape of the object is reconstructed by phase-shift coding. This enables pixel level measurement resolution. In the 3D Topography Module, the pixel size is 1.1 μm x 1.1 μm for analysis of micron-scale surface features.
The sinusoidal fringes can be expressed by: where (x, y) is the coordinate in the slide frame plane, a is background intensity, b is amplitude modulation, p is the sinusoidal grating wavelength, φ0 is the additional phase shift caused by the surface height and δn is the phase shift from the slide movement.
As an example, for a color pattern with red, green, and blue light, it can be dem onstrated the case of three divided wavelengths where Ir, Ig, and Ib are the corresponding intensities for each of the S-7 colors. The phase shifts can be plotted as in Fig. S3. Then the spatial phase shift can be expressed with the following equation: φ(x, y) = arc tan |√ I r − I b 2I g −I r − I b | The phase shift indicates the horizontal coordinate, i.e. the height differences in every pixel providing the sample topography.

MD simulation details
We We assumed the additivity of diameters and the Lorentz-Berthelot mixing rule. The Coulomb and LJ interactions between atoms separated by three bonds within the same molecule were scaled down by multiplying them by 0.833333 and 0.5, respectively. All dispersion interaction has a cutoff radius rcut = 1.5nm and the same distance was used to switch from real-space to Fourier space calculations of the electrostatics.
The computational procedure is analogous to that used in our previous paper. 6 The solid fluid interaction parameter was determined by matching the WCA of a cylindrical drop to the experimental value. To this end 3900 TIP4P/2005 water molecules were placed on top of the S-8 copper surface. After preemptive energy minimization the drop was equilibrated for 10 ns, and the averages were gathered for up to 40 ns. S-10