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Biomaterials capable of continuously inactivating pathogens are essential for suppressing transmission of infectious diseases, such as epidemic cerebrospinal meningitis and pulmonary tuberculosis. Here, capillary condensation of air moisture within nano‐confined spaces between superhydrophilic rigid nanorods is shown and target microbiology spontaneously stretch and inactivate aerosolized microorganisms. Specifically, the negative Gaussian curvature‐shaped water condensate causes fluidic straining, comprising surface tension and Laplace pressure, strong enough to deform and eliminate the selected bacteria. Plate counting quantifies the sharply reduced contact‐killing period for superhydrophilic and bare nanorods (6 vs 100 min for E. coli, 20 vs 120 min for S. aureus) under relative humidity of 70%. Theoretical calculations and experimental studies indicate increased mechanical straining and mechano‐bactericidal by improving air moisture content. To further illustrate utility, long‐term antibacterial medical masks are fabricated by integrating such nanorods onto commercial fabrics. Collectively, these findings highlight the immense potential of capillary condensation‐induced fluidic straining as an eco‐friendly, broad‐spectrum, and highly efficient antibacterial strategy. [ABSTRACT FROM AUTHOR]

Radial injection of shear-thinning fluids into rock fractures is ubiquitous in subsurface engineering practices, including drilling, hydraulic fracturing, and rock grouting. Yet, the effect of injection-induced fracture deformation on radial displacement behavior of shear-thinning fluids remains unclear. Through radial injection experiments of shear-thinning fluids displacing an immiscible Newtonian fluid in a Hele–Shaw cell, we investigate the fracture deformation behavior during injection and the fluid–fluid displacement patterns under this impact. A mixed displacement pattern is observed where the invasion front gradually evolves from unstable (viscous fingering) to stable (compact displacement) as the injection proceeds. We demonstrate that the combined effect of shear-thinning property and radial flow geometry plays a controlling role in the evolution of the patterns. At high flow rates, the fracture dilation induced by high injection pressure tends to reduce the displacement efficiency in stages. Based on linear stability analysis, we propose a theoretical criterion for the transition of interfacial stability considering the viscosity of injected fluids and fracture deformation, which agrees well with the experimental observations. This research underscores the importance of rock deformation on two-phase flow dynamics in fractured media. [ABSTRACT FROM AUTHOR]

When a more viscous fluid displaces a less viscous one in porous media, viscous pressure drop stabilizes the displacement front against capillary pressure fluctuation. For this favorable viscous ratio conditions, previous studies focused on the front instability under slow flow conditions but did not address competing effects of wettability and flow rate. Here we study how this competition controls displacement patterns. We propose a theoretical model that describes the crossover from fingering to stable flow as a function of invading fluid contact angle θ and capillary number Ca. The phase diagram predicted by the model shows that decreasing θ stabilizes the displacement for θ≥45° and the critical contact angle θc increases with Ca. The boundary between corner flow and cooperative filling for θ < 45° is also described. This work extends the classic phase diagram and has potential applications in predicting CO2 capillary trapping and manipulating wettability to enhance gas/oil displacement efficiency.

The complexity and heterogeneity of pore structure present significant challenges in accurate permeability estimation. Commonly used empirical formulas neglect its microscopic and topological characteristics, thus lacking accuracy and adaptability. While machine learning (ML) and deep learning (DL) models demonstrate promising performance, but encounter challenges of data availability, computational cost, and model interpretability. The present study aims to develop a more robust and accurate permeability prediction model via knowledge extraction from ML model. We first establish an ML model between permeability and the geometry‐topology characteristics of porous media using Extreme Gradient Boosting (XGBoost) algorithm. The data set used to fit ML model is prepared from 458 samples of different types of porous media. Using the SHapley Additive exPlanations (SHAP) value, the influence of each feature on permeability prediction is quantified. It is found that the closeness centrality (topology feature), tortuosity, porosity (macroscopic features) and throat diameter, throat length, pore diameter (pore network features) are vital for permeability prediction. Guided by partial dependence calculation, the unknown function relationship between permeability and the top six important features is established. The novel permeability prediction model incorporating topology feature improves the prediction accuracy and demonstrates strong applicability across diverse data sets. This new model presents an optimal balance between simplicity and performance, rendering it a compelling alternative for permeability prediction in porous media. The research provides a novel referable framework of knowledge extraction via ML to reveal the important features and establish the potential relationship that can be extended and applied in other research fields. Key Points: A universal framework of knowledge extraction is provided to discover the important features and establish the unknown relationshipKnowledge discovering based on machine learning helps to understand the prediction pattern and the impact of features on permeabilityThe new model incorporating topology feature improves permeability prediction performance and demonstrates strong applicability [ABSTRACT FROM AUTHOR]