Thermodynamic promoters can shift hydrate equilibrium to lower pressures and higher temperatures, but their application is often limited by dissolution, dilution, and hydrate cage occupation, which reduce gas storage capacity. In this work, a small amount of tetrabutylammonium bromide (TBAB) was locally attached to a capillary-active metal mesh to avoid direct mixing with the bulk liquid phase. This strategy alleviated mass transfer blockage caused by hydrate film formation at the gas-liquid interface in static systems, reduced the adverse effect of promoter cage occupation on storage capacity, and enhanced heat transfer. A 300 ppm tryptophan solution showed the best hydrate formation performance at 2 degrees C and 3.5 MPa for a natural gas mixture containing 90 mol% CH4, 7 mol% C2H6, and 3 mol% C3H8. The optimal mesh-to-solution height ratio was 2.6:1, and linear hydrates formed along the mesh facilitated upward water migration. When 0.04 g of 25 wt% TBAB solution was attached to the mesh edge 1 cm above the liquid surface, upward migration of most of the water in the 12 g solution was achieved within 10 min, and 90% of the maximum gas storage density was reached within 1 h. This work provides an effective route for rapid natural gas hydrate formation in static systems with minimal thermodynamic promoter usage.
Reconstructing causal dynamic networks from multivariate time series is a foundational problem in complex systems science. Yet, the key scientific issue is not simply causality detection, but causal interpretability. Interpretable causality is the foundation for testable mechanistic hypotheses, transferable forecasting, and principled decision-making for intervention and control. In real-world complex systems, causal inference is often compromised by noise, missing data, high dimensionality, nonlinearity, time delays, heterogeneity, and partial observability. Classic approaches to interpretable causality yield explicit, inspectable quantities such as causal graphs, coefficients, and governing equations. However, this methodological shift has heightened expectations: AI-extended approaches are increasingly required to recover explicit causal mechanisms rather than opaque predictive dependencies, thereby preserving interpretability. This review summarizes four classic methods and their AI-extended counterparts based on time series data, including Granger frameworks, information-theoretic measures, nonlinear state–space/manifold reconstruction, and mechanistic differential-equation learning. Next, we elucidate their motivations, core principles, the origins of interpretability, and the assumptions required for meaningful conclusions. Finally, we highlight representative applications across climate studies, neuroscience, epidemiology, finance, social science, ecology and molecular biology, followed by a discussion of comparative analysis, open challenges, and future research directions.
In high-pressure fuel injection systems, the rapid opening of the outlet valve induces sudden depressurization, generating transient pressure waves and flow rate oscillations that significantly affect engine power output and pollutant emissions. Nevertheless, the underlying mechanisms coupling the pressure surge and flow rate oscillation remain elusive. In this study, by constructing a simplified model of a high-pressure diesel fuel flow pipe, we elucidate the coupling mechanisms between pressure wave propagation and flow rate oscillations following sudden depressurization initiated by valve opening. Our computational fluid dynamics results reveal how transient pressure waves form and propagate back and forth within the pipe, inducing a segmental acceleration pattern in the local flow rate. We identify fluid compressibility as the source of flow rate oscillation and demonstrate the critical influence of pipe length on the periods of both pressure and flow rate oscillations. To interpret the intricate relationship between pressure wave attenuation and flow rate oscillations, we propose a multi-cycle segmental acceleration mechanism, highlighting the critical role of pressure wave propagation in flow rate oscillation.
This study first proposed a comprehensive separation strategy combining liquid–liquid extraction and selective crystallization to achieve the selective recovery of guaiacol from the phenolic wastewater present of biomass pyrolysis oil. Through high-throughput screening of 136 DSILs using the COSMO-RS model, [BMPY]0.5[HMIM]0.5[NTF2] was determined as the optimal extractant. The experimental results showed that the extraction efficiency of this DSIL for the mixed phenolic compounds exceeded 95%, and it exhibited excellent cycle stability. Subsequently, selective crystallization between piperazine and guaiacol was employed to selectively recover guaiacol from the complex phenolic mixture. Moreover, molecular simulation revealed the important roles of hydrogen bonds and van der Waals interactions in molecular recognition during extraction and crystallization. This study systematically clarified the specific recognition mechanism between molecules from microscopic mechanisms to macroscopic experiments, providing a new approach for the efficient recovery of phenolic compounds from wastewater.
In real-world scenarios, data are commonly represented in multiple views. Multi-view subspace clustering (MVSC) has attracted significant research attention owing to its capability to integrate complementary information across views while characterizing data structures through self-representation mechanisms. However, most existing MVSC algorithms exhibit two drawbacks: 1) They often overlook the negative impacts of redundant features, which not only increase data dimensionality but also introduce noise, adversely affecting the clustering results. 2) They are based on matrix self-representation, which inherently suffers from limitations in handling high-dimensional real-world data and fails to comprehensively capture the underlying cluster structures. Due to the varying dimensions among different views, tensor self-representation cannot be directly formulated. To address these two problems, we propose a multi-view subspace clustering algorithm that integrates tensor self-representation and feature extraction into a framework (JTSF-MVC). Specifically, JTSF-MVC utilizes the transformation matrices to extract discriminative features from the original data, while ensuring that all views have the same dimensionality. The transformed data for each view are stacked into a tensor. Subsequently, tensor self-representation can be successfully applied to this tensor to learn essential similarity relationships between samples. Furthermore, we propose an extended version (JTSF-IMVC) to tackle the challenging problem of missing multi-view data. To derive the optimal solution of the objective function, we employ the ADMM to optimize the proposed algorithms. Extensive experimental results on nine baseline datasets show that our proposed methods outperform their competitors. Our code is publicly available at github.com/ytccyw/JTSF-MVC.
