Liquid layer generators for excellent icephobicity at extremely low temperatures

Wang, Feng; Xiao, Senbo; Zhuo, Yizhi; Ding, Wenwu; He, Jianying; Zhang, Zhiliang

doi:10.1039/c9mh00859d

Cited by 63 publications

(55 citation statements)

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“…For the larger systems (Systems C and D in Table 2), the temperature to test the water contact angle was increased to 300 K in order to decrease the remnants of ice crystal in the system after equilibration. It should be noted that lower temperature can lead to higher ice adhesion in experiments, owing to the unclear changes of the ice-substrate interfaces at low temperature [62]. The atomistic surface model used here can not capture this temperature effect on ice adhesion.…”

Section: Simulation Detailsmentioning

confidence: 93%

Nanoscale Correlations of Ice Adhesion Strength and Water Contact Angle

Rønneberg

Xiao

et al. 2020

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Surfaces with low ice adhesion represent a promising strategy to achieve passive anti-icing performance. However, as a successful and robust low ice adhesion surface must be tested under realistic conditions at low temperatures and for several types of ice, the initial screening of potential low ice adhesion surfaces requires large resources. A theoretical relation between ice adhesion and water wettability in the form of water contact angle exists, but there is disagreement on whether this relation holds for experiments. In this study, we utilised molecular dynamics simulations to examine the fundamental relations between ice adhesion and water contact angle on an ideal graphene surface. The results show a significant correlation according to the theoretic predictions, indicating that the theoretical relation holds for the ice and water when discarding surface material deformations and other experimental factors. The reproduction of the thermodynamic theory at the nanoscale is important due to the gap between experimental observations and theoretical models. The results in this study represent a step forward towards understanding the fundamental mechanisms of water–solid and ice–solid interactions, and the relationship between them.

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Section: Simulation Detailsmentioning

confidence: 93%

Nanoscale Correlations of Ice Adhesion Strength and Water Contact Angle

Rønneberg

Xiao

et al. 2020

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“…The VST is very common due to its simple and economical set-up and performance, although the location of the force probe impacts the ice adhesion strength greatly [32], and the stress distribution may not be completely uniform [8,17,18]. The VST is commonly in use by several research groups [7,11,[32][33][34][35][36][37][38][39], and has been attempted as a standard for ice adhesion measurement utilizing only commercially available instruments [14]. When comparing reported ice adhesion strengths, it is also necessary to include the type of ice tested.…”

Section: Introductionmentioning

confidence: 99%

Interlaboratory Study of Ice Adhesion Using Different Techniques

et al. 2019

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Low ice adhesion surfaces are a promising anti-icing strategy. However, reported ice adhesion strengths cannot be directly compared between research groups. This study compares results obtained from testing the ice adhesion strength on the same surface at two different laboratories, testing two different types of ice with different ice adhesion test methods at temperatures of -10 o C and -18 o C. One laboratory uses the centrifuge adhesion test and tests precipitation ice and bulk water ice, while the other laboratory uses a vertical shear test and tests only bulk water ice. The surfaces tested were bare aluminum and a commercial icephobic coating, with all samples prepared in the same manner. The results showed comparability in the general trends, surprisingly, with the greatest differences for bare aluminum surfaces at temperature -10 o C. For bulk water ice, the vertical shear test resulted in systematically higher ice adhesion strength than the centrifugal adhesion test. The standard deviation depends on the surface type and seems to scale with the absolute value of the ice adhesion strength. The experiments capture the overall trends in which the ice adhesion strength surprisingly decreases from -10 o C to -18 o C for aluminum and is almost independent of temperature for a commercial icephobic coating. In addition, the study captures similar trends in the effect of ice type on ice adhesion strength as previously reported and substantiates that ice formation is a key parameter for ice adhesion mechanisms.

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“…The high amount of interfacial amorphous water on the rough surface can act as a lubricant, facilitating the easy sliding of the ice along the uphill slopes of the surface under tension stress. The effect of the interfacial amorphous water is thus reminiscent of the so-called interfacial slippage effect for the mobility of ice [44,61]. Surprisingly, the amount of amorphous water increases with the ice-substrate LJ potential energy depth epsilon, as observed on the rough surface systems and verified by the long equilibration time shown in Figure S1.…”

Section: Nanoscale Ice Adhesion On Rough Surfacesmentioning

confidence: 72%

“…It is important to note that all the surfaces in this study are at the nanoscale (10 × 10 nm 2 ) and the ice adhesion of focus is intrinsic adhesion (Figure 1), which is different from the so-called macroscopic mechanical locking that enhances ice adhesion in experiments [8,63]. For a higher roughness scale in experiments, other strategies for the generation of lubrication are needed [44].…”

Section: Nanoscale Ice Adhesion On Rough Surfacesmentioning

confidence: 92%

“…The most appropriate tool, atomistic modeling and molecular dynamics (MD) simulations, has been utilized to gain further understanding of the nanoscale ice adhesion and detachment mechanisms [23][24][25]. Phenomena such as the quasi-liquid layer, the lubricating effects, and the role of surface topography can therefore be studied in detail, and the ice adhesion strength can thus be predicted [23][24][25]44,45]. Such predictions are white-box models, in that the approach applies carefully stated laws of physics and mathematical time integration in order to reach a conclusive prediction.…”

Section: Introductionmentioning

confidence: 99%

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Machine Learning Based Prediction of Nanoscale Ice Adhesion on Rough Surfaces

Ringdahl

Xiao

et al. 2020

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It is widely recognized that surface roughness plays an important role in ice adhesion strength, although the correlation between the two is far from understood. In this paper, two approaches, molecular dynamics (MD) simulations and machine learning (ML), were utilized to study the nanoscale intrinsic ice adhesion strength on rough surfaces. A systematic algorithm for making random rough surfaces was developed and the surfaces were tested for their ice adhesion strength, with varying interatomic potentials. Using MD simulations, the intrinsic ice adhesion strength was found to be significantly lower on rougher surfaces, which was attributed to the lubricating effect of a thin quasi-liquid layer. An increase in the substrate–ice interatomic potential increased the thickness of the quasi-liquid layer on rough surfaces. Two different ML algorithms, regression and classification, were trained using the results from the MD simulations, with support vector machines (SVM) emerging as the best for classifying. The ML approach showed an encouraging prediction accuracy, and for the first time shed light on using ML for anti-icing surface design. The findings provide a better understanding of the role of nanoscale roughness in intrinsic ice adhesion and suggest that ML can be a powerful tool in finding materials with a low ice adhesion strength.

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Liquid layer generators for excellent icephobicity at extremely low temperatures

Abstract: The liquid layer generators enable excellent dynamic anti-icing performance and show great potential at temperature of −60 °C.

Cited by 63 publications

References 50 publications

Nanoscale Correlations of Ice Adhesion Strength and Water Contact Angle

Nanoscale Correlations of Ice Adhesion Strength and Water Contact Angle

Interlaboratory Study of Ice Adhesion Using Different Techniques

Machine Learning Based Prediction of Nanoscale Ice Adhesion on Rough Surfaces

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