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This study introduces an anisotropic interfacial potential that provides an accurate description of the van der Waals (vdW) interactions between water and hexagonal boron nitride (h-BN) at their interface. Benchmarked against the strongly constrained and appropriately normed (SCAN) functional, the developed force field demonstrates remarkable consistency with reference data sets, including binding energy curves and sliding potential energy surfaces for various configurations involving a water molecule adsorbed atop the h-BN surface. These findings highlight the significant improvement achieved by the developed force field in empirically describing the anisotropic vdW interactions of the water/h-BN heterointerfaces. Utilizing this anisotropic force field, molecular dynamics simulations demonstrate that atomically-flat pristine h-BN exhibits inherent hydrophobicity. However, when atomic-step surface roughness is introduced, the wettability of h-BN undergoes a significant change, leading to a hydrophilic nature. The calculated water contact angle (WCA) for the roughened h-BN surface is approximately 64{\deg}, which closely aligns with experimental WCA values ranging from 52{\deg} to 67{\deg}. These findings indicate the high probability of the presence of atomic steps on the surfaces of experimental h-BN samples, emphasizing the need for further experimental verification. The development of the anisotropic interfacial force field for accurately describing interactions at the water/h-BN heterointerfaces is a significant advancement in accurately simulating the wettability of two-dimensional (2D) materials, offering a reliable tool for studying the dynamic and transport properties of water at these interfaces, with implications for materials science and nanotechnology., Comment: 22 pages, 5 figures

An anisotropic interlayer potential that can accurately describe the van der Waals interaction of the water-graphene interface is presented. The force field is benchmarked against the many-body dispersion-corrected density functional theory. The parameterization of ILP yields good agreement with the reference dataset of binding energy curves and sliding potential energy surfaces for various configurations of a water molecule deposited on monolayer graphene, indicating the developed force field enhancing significantly the accuracy in the empirical description of water-graphene interfacial interactions. The water contact angles of monolayer and multilayer graphene extracted from molecular dynamics simulations based on this force field are close to the experimental measurements and predict the hydrophilic nature of graphene. The theoretical approach proposed in this work can be easily extended to mimic the van der Waals interactions between water molecules and various two-dimensional layered materials for studying their wetting properties., Comment: 20 pages, 5 figures

In the audio magnetotellurics (AMT) sounding data processing, the absence of sferic signals in some time ranges typically results in a lack of energy in the AMT dead band, which may cause unreliable resistivity estimate. We propose a deep convolutional neural network (CNN) to automatically recognize sferic signals from redundantly recorded data in a long time range and use them to compensate for the resistivity estimation. We train the CNN by using field time series data with different signal to noise rations that were acquired from different regions in mainland China. To solve the potential overfitting problem due to the limited number of sferic labels, we propose a training strategy that randomly generates training samples (with random data augmentations) while optimizing the CNN model parameters. We stop the training process and data generation until the training loss converges. In addition, we use a weighted binary cross-entropy loss function to solve the sample imbalance problem to better optimize the network, use multiple reasonable metrics to evaluate network performance, and carry out ablation experiments to optimally choose the model hyperparameters. Extensive field data applications show that our trained CNN can robustly recognize sferic signals from noisy time series for subsequent impedance estimation. The subsequent processing results show that our method can significantly improve S/N and effectively solve the problem of lack of energy in dead band. Compared to the traditional processing method without sferic compensation, our method can generate a smoother and more reasonable apparent resistivity-phase curves and depolarized phase tensor, correct the estimation error of sudden drop of high-frequency apparent resistivity and abnormal behavior of phase reversal, and finally better restore the real shallow subsurface resistivity structure.