Jihong Shi

Permeability is a fundamental property governing fluid transport in porous media and is critical to enhanced oil recovery. Nevertheless, existing methods remain limited by experimental uncertainty, computational cost, and insufficient interpretability. This study proposed an enhanced permeability prediction equation. The equation was developed by combining machine learning with theoretical analysis. The proposed equation outperformed the calibrated Kozeny–Carman (K–C) and baseline models and achieved performance comparable to that of XGBoost. It therefore provides a concise and physically interpretable description of permeability within the adopted pore network modeling framework.

We computationally demonstrate that subnanometer aqueous clusters containing a single trivalent metal ion (Fe³⁺, Lu³⁺, or La³⁺) can exhibit charge-induced structural instability. These clusters dynamically evolve across their potential energy landscape, adopting triangular, elongated two-point, single-point, and more spherical configurations often with distinct conical surface protrusions. The manifestation of this instability differs from that observed in mesoscopic and microscopic droplets containing macroions, where stable "star-like" structures form, characterized by a specific number of conical protrusions that varies with the droplet size. We find that the orientation of the H₂O molecules surrounding the metal ion is influenced not only by the electric field of the trivalent ion but also by the local conical protrusions. To further investigate the local acidity in the conical protrusions, a model system consisting of an aqueous nanocluster containing three H₃O⁺ ions was simulated using ab initio molecular dynamics. Within the conical regions of the cluster, protons exhibit mobility across several water molecules forming cyclic structures, in contrast to the more localized proton delocalization observed in the compact body of the cluster. These findings suggest that local geometry can significantly modulate acidity in highly charged nanoclusters, with potential implications for understanding charge-transfer and ionization mechanisms in techniques such as electrospray ionization mass spectrometry. Additionally, the structural motifs and solvent organization reported here provide a molecular-level framework that can complement interpretations from infrared spectroscopic data.

We show that sub-nanometer aqueous clusters containing a single trivalent metal ion (\ce{Fe^{3+}}, \ce{Lu^{3+}}, or \ce{La^{3+}}) can exhibit charge-induced structural instabilities. These clusters dynamically evolve across their potential energy landscape, adopting triangular, elongated two-point, single-point, and more spherical configurations often with distinct conical surface protrusions. The manifestation of this instability differs from that observed in mesoscopic and microscopic droplets containing macroions, where stable ``star-like'' structures form, characterized by a specific number of conical protrusions that varies with the droplet size. We find that the orientation of the \ce{H2O} molecules surrounding the metal ion is influenced not only by the electric field of the trivalent ion but also by the local conical protrusions. To further investigate the local acidity in the conical protrusions, a model system consisting of an aqueous nano-cluster containing three \ce{H3O+} ions was simulated using ab initio molecular dynamics. Within the conical regions of the cluster, protons exhibit mobility across several water molecules forming cyclic structures, in contrast to the more localized proton delocalization observed in the compact body of the cluster. These findings suggest that local geometry can significantly modulate acidity in highly charged nano-clusters, with potential implications for understanding charge-transfer and ionization mechanisms in techniques such as electrospray ionization mass spectrometry. Additionally, the structural motifs and solvent organization reported here provide a molecular-level framework that can complement interpretations from infrared spectroscopic data.

Hydrogen is a promising energy carrier that plays a crucial role in the transition toward a low-carbon economy. While most hydrogen is currently produced from fossil fuels or through water electrolysis using renewable energy, natural hydrogen (also known as white, native, geological hydrogen) represents an underexplored and potentially abundant source. This paper aims to provide a comprehensive overview of the current state of knowledge on the development of natural hydrogen, highlighting the technical challenges in detection, exploration, production and transportation. To resolve these challenges, a number of deep learning-aided models have been developed, including geological feature detection models, phase equilibrium models and gas transportation models. Concluding remarks are discussed for insight into future research directions.

Ice nucleation and growth are critical in many fields, including atmospheric science, cryobiology, and aviation. However, understanding the detailed mechanisms of ice crystal growth remains challenging. In this work, crystallization at the ice/quasi-liquid layer (QLL) interface of the basal and primary prism (prism1) surfaces of hexagonal ice (Ih) was investigated using molecular dynamics simulations across a wide range of temperatures for the TIP4P/Ice model, with comparisons to the mW coarse-grained model. Together with elucidating the temperature-dependent mechanisms of crystallization, face-specific growth rates were systematically estimated. While the prism surface generally exhibits faster growth rates than the basal surface, a temperature-dependent crossover in growth rates between the basal and prism surfaces is observed in TIP4P/Ice simulations, which correlates with crossovers in QLL thickness and properties and with the well-known column to platelets transition in ice-crystal habits at low vapor pressure. This observation helps decode the complex dependence between crystal morphology and temperature in ice crystals.

The global scarcity of freshwater resources has greatly contributed to the development of desalination technologies, in which electrodialysis desalination is one of the most widely used and highly regarded methods. In this work, the first step was to design and assemble a experiment module for electrodialysis desalination. The ion removal efficiency and single membrane mass transfer flux of electrodialysis desalination were investigated. The results show that the desalination performance of the module is improved by increasing the voltage gradient, increasing the concentration of seawater and electrolyte and decreasing the membrane surface flux and that the optimum operating conditions for the module at 24 V operating voltage are feedstock concentration of 35 g/L, electrolyte concentration of 1.42 g/L which and system flow rate of 15 L/h. The results of the study will help to better investigate electrodialysis desalination technology.

The temperature dependence of the quasi-liquid layer on the surfaces of hexagonal and cubic ice is investigated with molecular dynamics simulations and a selection of different methods to discriminate the phase of molecules.