Enabling sequential rupture for lowering atomistic ice adhesion

Xiao, Senbo; Skallerud, Bjørn; Wang, Feng; Zhang, Zhiliang

doi:10.1039/c9nr00104b

Cited by 26 publications

(43 citation statements)

References 42 publications

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“…The surface roughness also altered the surface de-icing process-namely ice was detached in a sudden concurrent manner from the flat surfaces but was sequentially slipped/glided uphill along the steep parts on the rough surface. The sequential rupture mode of de-icing on the rough surfaces is effective in reducing atomistic ice adhesion strength, as was observed in former studies [24].…”

Section: Nanoscale Ice Adhesion On Rough Surfacessupporting

confidence: 70%

“…on the rough surface. The sequential rupture mode of de-icing on the rough surfaces is effective in reducing atomistic ice adhesion strength, as was observed in former studies [24]. The above results therefore present a rather counterintuitive conclusion: in order to make enable low intrinsic ice adhesion, the pairwise attraction between the surface and ice should be high, and the surface should be rough.…”

Section: Nanoscale Ice Adhesion On Rough Surfacessupporting

confidence: 65%

“…The nanoscale surface roughness also altered the rupture mode of ice adhesion [24] and subsequently changed the ice adhesion strength. Compared to ice on the flat surface with perfect contact, the ice adhered on the rough surfaces had an uneven distribution of local interfacial structures owing to the corners and curvature of the roughness landscape [60].…”

Section: Nanoscale Ice Adhesion On Rough Surfacesmentioning

confidence: 99%

“…Macroscopic ice adhesion strength assumes that the force applied to the ice is evenly distributed across the ice-surface interface, but it is well known that even seemingly flat surfaces exhibit a roughness at the micro-or nanoscale [22], resulting in a different load distribution and direction relative to the surface [1]. As experiments mainly focus on macroscopic apparent ice adhesion, atomistic modeling and simulations concentrate on intrinsic ice adhesion at the length scale of nanometer range [23][24][25]. Understanding nanoscale ice adhesion is not only essential for deciphering macroscopic adhesion behavior but also critical for achieving multiscale ice adhesion prediction in material surface design and optimization.…”

Section: Introductionmentioning

confidence: 99%

“…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

Coatings

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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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Section: Nanoscale Ice Adhesion On Rough Surfacessupporting

confidence: 70%

Section: Nanoscale Ice Adhesion On Rough Surfacessupporting

confidence: 65%

Section: Nanoscale Ice Adhesion On Rough Surfacesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Machine Learning Based Prediction of Nanoscale Ice Adhesion on Rough Surfaces

Ringdahl

Xiao

et al. 2020

Coatings

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Dynamic Anti‐Icing Surfaces (DAIS)

et al. 2021

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Remarkable progress has been made in surface icephobicity in the recent years. The mainstream standpoint of the reported antiicing surfaces yet only considers the ice-substrate interface and its adjacent regions being of static nature. In reality, the local structures and the overall properties of ice-substrate interfaces evolve with time, temperature and various external stimuli. Understanding the dynamic properties of the icing interface is crucial for shedding new light on the design of new anti-icing surfaces to meet challenges of harsh conditions including extremely low temperature and/or long working time. This article surveys the state-of-the-art anti-icing surfaces and dissects their dynamic changes of the chemical/physical states at icing interface. According to the focused critical ice-substrate contacting locations, namely the most important ice-substrate interface and the adjacent regions in the substrate and in the ice, the available anti-icing surfaces are for the first time re-assessed by taking the dynamic evolution into account. Subsequently, the recent works in the preparation of dynamic anti-icing surfaces (DAIS) that consider time-evolving properties, with their potentials in practical applications, and the challenges confronted are summarized and discussed, aiming for providing a thorough review of the promising concept of DAIS for guiding the future icephobic materials designs.

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Synergistic Effects of the Superhydrophilic and Superhydrophobic Components on the Antifreezing Performances of Latex Particles and Anti‐Icing Properties of Latex Films

Zhang,

Zhao

2024

Macromol. Rapid Commun.

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The development of new materials for antifreezing and anti‐icing applications is a big challenge in industry and academic area. Inspired by the antifreeze proteins, latex particles with superhydrophilic zwitterionic shells and superhydrophobic cores are synthesized by reversible addition‐fragmentation chain transfer emulsion polymerization, and the applications of the latex particles in antifreezing and anti‐icing applications are investigated. In antifreezing study, the critical aggregate temperature (CAT) of the latex particles decreases, and the separation of the melting and freezing temperature of ice increases with the particle concentration. Enzyme molecules can be cryopreserved in the particle solution, and their bioactivities are well maintained. Latex particles are casted into latex films with dynamic surfaces. Anti‐icing performances, including antifrosting properties, freezing delay time, and ice adhesion strengths, are studied; and the water‐treated latex films present stronger anti‐icing properties than other films, due to the synergistic effects of the superhydrophilic and superhydrophobic components. In addition, latex particles with zwitterionic shells and poly(n‐butyl methacrylate) cores, and latex particles with small molecular surfactant on the surfaces are synthesized. The antifreezing performances of the latex particles and anti‐icing properties of the latex films are compared.

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Enabling sequential rupture for lowering atomistic ice adhesion

Abstract: Embedding the intrinsic sequential rupture mode into surfaces as an interfacial mechanical function can lead to low atomistic ice adhesion strength.

Cited by 26 publications

References 42 publications

Machine Learning Based Prediction of Nanoscale Ice Adhesion on Rough Surfaces

Machine Learning Based Prediction of Nanoscale Ice Adhesion on Rough Surfaces

Dynamic Anti‐Icing Surfaces (DAIS)

Synergistic Effects of the Superhydrophilic and Superhydrophobic Components on the Antifreezing Performances of Latex Particles and Anti‐Icing Properties of Latex Films

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