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  1. Abstract. Heterogeneous ice nucleation is thought to be the primary pathway for the formation of ice in mixed-phase clouds, with the number of active ice-nucleating particles (INPs) increasing rapidly with decreasing temperature. Here, molecular-dynamics simulations of heterogeneous ice nucleation demonstrate that the ice nucleation rate is also sensitive to pressure and that negative pressure within supercooled water shifts freezing temperatures to higher temperatures. Negative pressure, or tension, occurs naturally in water capillary bridges and pores and can also result from water agitation. Capillary bridge simulations presented in this study confirm that negative Laplace pressure within the water increases heterogeneous-freezing temperatures. The increase in freezing temperatures with negative pressure is approximately linear within the atmospherically relevant range of 1 to −1000 atm. An equation describing the slope depends on the latent heat of freezing and the molar volume difference between liquid water and ice. Results indicate that negative pressures of −500 atm, which correspond to nanometer-scale water surface curvatures, lead to a roughly 4 K increase in heterogeneous-freezing temperatures. In mixed-phase clouds, this would result in an increase of approximately 1 order of magnitude in active INP concentrations. The findings presented here indicate that any process leading to negative pressure in supercooled water may play a role in ice formation, consistent with experimental evidence of enhanced ice nucleation due to surface geometry or mechanical agitation of water droplets. This points towards the potential for dynamic processes such as contact nucleation and droplet collision or breakup to increase ice nucleation rates through pressure perturbations.

     
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  2. We have developed a lattice Monte Carlo (MC) simulation based on the diffusion-limited aggregation model that accounts for the effect of the physical properties of small ions such as inorganic ions and large salt ions that mimic ionic liquids (ILs) on lithium dendrite growth. In our cellular automaton model, molecular and atomistic details are largely coarse-grained to reduce the number of model parameters. During lithium deposition, the cations of the salt and ILs form positively charged electrostatic shields around the tip of the dendrites, and the anions of the salt and ILs form negative local potential lumps in adjacent areas to the dendrite. Both of the effects change the distribution of the electrostatic potential and notably inhibit dendrite formation between electrodes. The applied voltage and the physical properties of the salt ions and ILs, such as the size of the ions, the size asymmetry between the cation and anion, the dielectric constant, the excluded volume of the ions, and the model parameter η , notably affect electric-field screening and hence the variation in the local potential, resulting in substantial changes in the aspect ratio and the average height of the dendrites. Our present results suggest that the large salts such as ILs more significantly inhibit the dendrite growth than the small ions, mainly because the ions highly dissociated in ILs can participate in electrostatic shielding to a greater degree. To reduce the computational complexity and burden of the MC simulation, we also constructed a surrogate model with ensemble neural networks. 
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  3. Abstract

    Ionic liquid (IL)‐containing polymers garner attention for electrochemical applications. This article overviews recent experimental and theoretical studies of polymer electrolytes that would be likely to cultivate new theoretical and computational frameworks for IL‐containing polymers. The first two sections outline the uniqueness of ILs that differentiates them from inorganic salts in polymers and explore deviation from the concept of the metaphor “room‐temperature molten salt.” Such distinct properties include (1) large intrinsic dipole moment and electronic polarizability, (2) hydrogen bonding, (3) π‐interactions, (4) a broad distribution of charges over the entire ion, and (5) the anisotropy of the ions. Moreover, the complexity of these properties substantially increases when the ions are polymerized. Indeed, their exceptional features would overcome the hurdle due to a trade‐off between ionic conductivity and mechanical robustness in inorganic salt‐doped polymers. Given these facts, the rest of the article focuses on emerging trends in the study of the dielectric response, phase separation, ion conductivity, and mechanical robustness of the polymer electrolytes, highlighting outstanding observations in experiments that may inspire existing theory and simulation. Our discussion also includes improving computational complexity for IL‐containing polymers. To this end, recent machine learning studies that consider ILs and polymer liquids are presented.

     
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