# Relationships between Surface Properties and Snow Adhesion and Its Shedding Mechanisms
## 2 Experimental Section
To prevent the LWC gradient from rapidly changing during measurements, a flat configuration is used to design a snow adhesion measurement setup as shown schematically in Figure 3 (see also Figure S3 for the photo of the setup). An LWC sensor is centered on the sample furthest from the shear wall. Initially, two sensors were placed on the sample during testing. Once it was verified that the LWC remained unchanged, a single sensor was used to maximize the surface area of the substrate in contact with the snow. Figure 2: **(a)** Schematic of snow in the pendular regime; this typically occurs when the liquid water content in snow is below \(\sim\)27%. Air and small amounts of water fill the gaps between the ice particles. **(b)** Schematic of snow in the funicular regime in which gaps between ice particles are mostly filled by liquid water with minimal air. At around 27% LWC in snow, the transition between pendular and funicular regimes occurs. **(c)** Schematically shows the liquid water content gradient that occurs in snow on an inclined substrate during shedding. The LWC sensors confirm this water movement in snow on inclined substrates. **(d)** A photo of a snow pile accumulated on an aluminum substrate, indicating migration of water to the bottom of the snowpack due to gravity. A liquid water content gradient forms, resulting in lower LWC at the top of the snowpack, when compared to its bottom. To conduct adhesion measurements multiple 6 \(\times\) 6 in samples made of various materials were prepared and the artificial snow formed by the snow gun was collected and sifted onto the test samples at a minimum distance of 2 cm. The excess snow was cut from the samples using a straight edge to ensure the snow surface area was not larger than the 6 in \(\times\) 6 in each square sample. The sample was then placed on the test apparatus. The shear wall height was adjusted to the interface of the snow and the substrate. The motor was turned on and the cart was pulled at a speed of 1.7 mm/s. Various speeds were tested to understand how velocity impacts snow adhesion. The failure mechanisms described are apparent regardless of the cart velocity. A force probe (DS2-4 digital force gauge from GaugeCity.com) with a capacity of 20.00 N and a resolution of 0.01 N was used to measure shear forces. At the end of the first pull, the snow remained on the end of the cart, not on the test substrate. Then, the cart was reset to its initial position and the motor was turned on again. The force was recorded a second time to determine the specific friction for the test case. Because the weight of the snow changes with varying LWC, the friction test was conducted with each trial. The average friction force (it remained the same throughout the experiment) was subtracted from the maximum force recorded from the first pull with snow. The resultant is the force in newtons required to shear the snow. This resultant is divided by the area of the substrate to obtain the adhesion strength of snow in pascals (see Supporting Information). Each experiment was conducted a minimum of five times and at a temperature of 0 \({}^{\circ}\)C in order to mimic a realistic scenario of snow adhesion where both ice and liquid water can be present. The detailed results of the experiments are shown in Table S1. In addition, calculations of adhesion strength are shown with raw data in Figure S4 and Table S2. To study the impact of roughness on snow adhesion strength, samples of different materials were treated with an aluminum oxide sandpaper with sizes ranging from 60- to 4000-grit. Standard stainless steel, aluminum 6061, and copper samples were used to study the impact of surface energy and roughness. In addition, high-density polyethylene (HDPE) and photovoltaic glass were purchased from Amazon and Cat I Glass Manufacturing Inc., respectively. A Keyence VHX 6000 optical microscope was used to determine the surface roughness of each material. The microscope has multi-angle observation with lighting data from every direction that enables building of 3D structures.