Patterning Configuration of Surface Hydrophilicity by Graphene Nanosheet towards the Inhibition of Ice Nucleation and Growth

Freezing of liquid water occurs in many natural phenomena and affects countless human activities. The freezing process mainly involves ice nucleation and continuous growth, which are determined by the energy and structure fluctuation in supercooled water. Herein, considering the surface hydrophilicity and crystal structure differences between metal and graphene, we proposed a kind of surface configuration design, which was realized by graphene nanosheets being alternately anchored on a metal substrate. Ice nucleation and growth were investigated by molecular dynamics simulations. The surface configuration could induce ice nucleation to occur preferentially on the metal substrate where the surface hydrophilicity was higher than the lateral graphene nanosheet. However, ice nucleation could be delayed to a certain extent under the hindering effect of the interfacial water layer formed by the high surface hydrophilicity of the metal substrate. Furthermore, the graphene nanosheets restricted lateral expansion of the ice nucleus at the clearance, leading to the formation of a curved surface of the ice nucleus as it grew. As a result, ice growth was suppressed effectively due to the Gibbs–Thomson effect, and the growth rate decreased by 71.08% compared to the pure metal surface. Meanwhile, boundary misorientation between ice crystals was an important issue, which also prejudiced the growth of the ice crystal. The present results reveal the microscopic details of ice nucleation and growth inhibition of the special surface configuration and provide guidelines for the rational design of an anti-icing surface.

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Ice nucleation on nanotextured surfaces: the influence of surface fraction, pillar height and wetting states

Physical Chemistry Chemical Physics ◽

10.1039/c6cp04382h ◽

2016 ◽

Vol 18 (38) ◽

pp. 26796-26806 ◽

Cited By ~ 18

Author(s):

Atanu K. Metya ◽

Jayant K. Singh ◽

Florian Müller-Plathe

Keyword(s):

Nucleation And Growth ◽

Ice Nucleation ◽

Nanostructured Surfaces ◽

Pillar Height ◽

Surface Fraction

Ice nucleation and growth on nanostructured surfaces.

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Nucleation and growth mechanisms of interfacial carbide in graphene nanosheet/Al composites

Carbon ◽

10.1016/j.carbon.2020.01.032 ◽

2020 ◽

Vol 161 ◽

pp. 17-24 ◽

Cited By ~ 4

Author(s):

Yuanyuan Jiang ◽

Zhanqiu Tan ◽

Genlian Fan ◽

Zhibo Zhang ◽

Ding-Bang Xiong ◽

...

Keyword(s):

Nucleation And Growth ◽

Growth Mechanisms ◽

Graphene Nanosheet ◽

Nucleation And Growth Mechanisms

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Analysis of isothermal and cooling rate dependent immersion freezing by a unifying stochastic ice nucleation model

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-15-13109-2015 ◽

2015 ◽

Vol 15 (9) ◽

pp. 13109-13166

Author(s):

P. A. Alpert ◽

D. A. Knopf

Keyword(s):

Cooling Rate ◽

Surface Area ◽

Ice Nucleation ◽

Rate Dependence ◽

Nucleation Theory ◽

Freezing Process ◽

Nucleation Kinetics ◽

Rate Dependent ◽

Immersion Freezing ◽

The Impact

Abstract. Immersion freezing is an important ice nucleation pathway involved in the formation of cirrus and mixed-phase clouds. Laboratory immersion freezing experiments are necessary to determine the range in temperature (T) and relative humidity (RH) at which ice nucleation occurs and to quantify the associated nucleation kinetics. Typically, isothermal (applying a constant temperature) and cooling rate dependent immersion freezing experiments are conducted. In these experiments it is usually assumed that the droplets containing ice nuclei (IN) all have the same IN surface area (ISA), however the validity of this assumption or the impact it may have on analysis and interpretation of the experimental data is rarely questioned. A stochastic immersion freezing model based on first principles of statistics is presented, which accounts for variable ISA per droplet and uses physically observable parameters including the total number of droplets (Ntot) and the heterogeneous ice nucleation rate coefficient, Jhet(T). This model is applied to address if (i) a time and ISA dependent stochastic immersion freezing process can explain laboratory immersion freezing data for different experimental methods and (ii) the assumption that all droplets contain identical ISA is a valid conjecture with subsequent consequences for analysis and interpretation of immersion freezing. The simple stochastic model can reproduce the observed time and surface area dependence in immersion freezing experiments for a variety of methods such as: droplets on a cold-stage exposed to air or surrounded by an oil matrix, wind and acoustically levitated droplets, droplets in a continuous flow diffusion chamber (CFDC), the Leipzig aerosol cloud interaction simulator (LACIS), and the aerosol interaction and dynamics in the atmosphere (AIDA) cloud chamber. Observed time dependent isothermal frozen fractions exhibiting non-exponential behavior with time can be readily explained by this model considering varying ISA. An apparent cooling rate dependence ofJhet is explained by assuming identical ISA in each droplet. When accounting for ISA variability, the cooling rate dependence of ice nucleation kinetics vanishes as expected from classical nucleation theory. The model simulations allow for a quantitative experimental uncertainty analysis for parameters Ntot, T, RH, and the ISA variability. In an idealized cloud parcel model applying variability in ISAs for each droplet, the model predicts enhanced immersion freezing temperatures and greater ice crystal production compared to a case when ISAs are uniform in each droplet. The implications of our results for experimental analysis and interpretation of the immersion freezing process are discussed.

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Chemiresistive gas sensor for the sensitive detection of nitrogen dioxide based on nitrogen doped graphene nanosheets

RSC Advances ◽

10.1039/c5ra21184k ◽

2016 ◽

Vol 6 (2) ◽

pp. 1527-1534 ◽

Cited By ~ 35

Author(s):

Mahabul Shaik ◽

V. K. Rao ◽

Manish Gupta ◽

K. S. R. C. Murthy ◽

Rajeev Jain

Keyword(s):

Nitrogen Dioxide ◽

Gas Sensor ◽

Room Temperature ◽

Graphene Nanosheets ◽

Sensitive Detection ◽

Graphene Nanosheet ◽

Interdigitated Electrodes ◽

Nitrogen Doped ◽

Nitrogen Doped Graphene ◽

Doped Graphene

Nitrogen doped graphene nanosheet coated interdigitated electrodes for sensitive detection of NO2 gas at room temperature.

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Heterogeneous Ice Nucleation Controlled by the Coupling of Surface Crystallinity and Surface Hydrophilicity

The Journal of Physical Chemistry C ◽

10.1021/acs.jpcc.5b09740 ◽

2016 ◽

Vol 120 (3) ◽

pp. 1507-1514 ◽

Cited By ~ 61

Author(s):

Yuanfei Bi ◽

Raffaela Cabriolu ◽

Tianshu Li

Keyword(s):

Ice Nucleation ◽

Surface Hydrophilicity ◽

Heterogeneous Ice Nucleation

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Direct comparisons of ice cloud macro- and microphysical properties simulated by the Community Atmosphere Model version 5 with HIPPO aircraft observations

Atmospheric Chemistry and Physics ◽

10.5194/acp-17-4731-2017 ◽

2017 ◽

Vol 17 (7) ◽

pp. 4731-4749 ◽

Cited By ~ 6

Author(s):

Chenglai Wu ◽

Xiaohong Liu ◽

Minghui Diao ◽

Kai Zhang ◽

Andrew Gettelman ◽

...

Keyword(s):

Nucleation And Growth ◽

Ice Nucleation ◽

Ice Crystal ◽

Ice Clouds ◽

Model Version ◽

Microphysical Properties ◽

Cloud Properties ◽

Community Atmosphere Model ◽

Atmosphere Model ◽

Ice Water

Abstract. In this study we evaluate cloud properties simulated by the Community Atmosphere Model version 5 (CAM5) using in situ measurements from the HIAPER Pole-to-Pole Observations (HIPPO) campaign for the period of 2009 to 2011. The modeled wind and temperature are nudged towards reanalysis. Model results collocated with HIPPO flight tracks are directly compared with the observations, and model sensitivities to the representations of ice nucleation and growth are also examined. Generally, CAM5 is able to capture specific cloud systems in terms of vertical configuration and horizontal extension. In total, the model reproduces 79.8 % of observed cloud occurrences inside model grid boxes and even higher (94.3 %) for ice clouds (T ≤ −40 °C). The missing cloud occurrences in the model are primarily ascribed to the fact that the model cannot account for the high spatial variability of observed relative humidity (RH). Furthermore, model RH biases are mostly attributed to the discrepancies in water vapor, rather than temperature. At the micro-scale of ice clouds, the model captures the observed increase of ice crystal mean sizes with temperature, albeit with smaller sizes than the observations. The model underestimates the observed ice number concentration (Ni) and ice water content (IWC) for ice crystals larger than 75 µm in diameter. Modeled IWC and Ni are more sensitive to the threshold diameter for autoconversion of cloud ice to snow (Dcs), while simulated ice crystal mean size is more sensitive to ice nucleation parameterizations than to Dcs. Our results highlight the need for further improvements to the sub-grid RH variability and ice nucleation and growth in the model.

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