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Abstract Liquid water under nanoscale confinement has attracted intensive attention due to its pivotal role in understanding various phenomena across many scientific fields. MXenes serve an ideal paradigm for investigating the dynamic behaviors of nanoconfined water in a hydrophilic environment. Combining deep neural networks and an active learning scheme, here we elucidate the proton‐driven dynamics of water molecules confined between V₂CT_xsheets using molecular dynamics simulation. Firstly, we have found that the Eigen and Zundel cations can inhibit water‐induced oxidation by adjusting the orientation of water molecules, thus proposing a general antioxidant strategy. Besides, we also identified a hexagonal ice phase with abnormal bonding rules at room temperature, rather than only at ultralow temperatures as other studies reported, and further captured the proton‐induced water phase transition. This highlighted the importance of protons in the maintaining stable crystal phase and phase transition of water. Furthermore, we discussed the conversions of different water structures and water diffusivity with changing proton concentrations in detail. The results provide useful guidance in practical applications of MXenes including developing antioxidant strategies, identifying novel 2D water phases and optimizing energy storage and conversion. 

Graph neural networks (GNNs) rely on the assumption of graph homophily, which, however, does not hold in some real-world scenarios. Graph heterophily compromises them by smoothing node representations and degrading their discrimination capabilities. To address this limitation, we propose H^2GNN, which implements Homophilic and Heterophilic feature aggregations to advance GNNs in graphs with homophily or heterophily. H^2GNN proceeds by combining local feature separation and adaptive message aggregation, where each node separates local features into similar and dissimilar feature vectors, and aggregates similarities and dissimilarities from neighbors based on connection property. This allows both similar and dissimilar features for each node to be effectively preserved and propagated, and thus mitigates the impact of heterophily on graph learning process. As dual feature aggregations introduce extra model complexity, we also offer a simplified implementation of H^2GNN to reduce training time. Extensive experiments on seven benchmark datasets have demonstrated that H^2GNN can significantly improve node classification performance in graphs with different homophily ratios, which outperforms state-of-the-art GNN models. 

As fraudulent activities have shot up manifolds, fraud detection has emerged as a pivotal process in different fields (e.g., e-commerce, online reviews, and social networks). Since interactions among entities provide valuable insights into fraudulent activities, such behaviors can be naturally represented as graph structures, where graph neural networks (GNNs) have been developed as prominent models to boost the efficacy of fraud detection. In graph-based fraud detection, handling imbalanced datasets poses a significant challenge, as the minority class often gets overshadowed, diminishing the performance of conventional GNNs. While oversampling has recently been adapted for imbalanced graphs, it contends with issues such as graph heterophily and noisy edge synthesis. To address these limitations, this paper introduces DOS-GNN, incorporating Dual-feature aggregation with Over-Sampling to advance GNNs for class-imbalanced fraud detection on graphs. This model exploits feature separation and dual-feature aggregation to mitigate the impact of heterophily and acquire refined node embeddings that facilitate fraud oversampling to balance class distribution without the need for edge synthesis. Extensive experiments on four large and real-world fraud datasets demonstrate that DOS-GNN can significantly improve fraud detection performance on graphs with different imbalance ratios and homophily ratios, outperforming state-of-the-art GNN models. 

Abstract Developing responsive coatings and materials requires discovering a breadth of mechanisms by which external stimuli can be converted into useful signals. Here, we demonstrate an approach driven by supramolecular mechanochemistry, where mechanical input—molecular shape change—is translated into structural color variation. By embedding bistable, negatively photochromic hydrazone photoswitches into cholesteric polymer networks, we achieve a reversible, stable color shift through molecular‐scale pulling and pushing of the photonic scaffold. Unlike azobenzene‐based systems, which typically disrupt liquid crystal order, this approach modifies the pitch of a cross‐linked cholesteric helix without disrupting the organisation of the material. The long‐lived stability of both hydrazone isomers ensures durable optical switching. This effect provides a new strategy for designing mechanoresponsive photonic coatings and tunable optical materials. 

Graphs have emerged as one of the most important and powerful data structures to perform content analysis in many fields. In this line of work, node classification is a classic task, which is generally performed using graph neural networks (GNNs). Unfortunately, regular GNNs cannot be well generalized into the real-world application scenario when the labeled nodes are few. To address this challenge, we propose a novel few-shot node classification model that leverages pseudo-labeling with graph active learning. We first provide a theoretical analysis to argue that extra unlabeled data benefit few-shot classification. Inspired by this, our model proceeds by performing multi-level data augmentation with consistency and contrastive regularizations for better semi-supervised pseudo-labeling, and further devising graph active learning to facilitate pseudo-label selection and improve model effectiveness. Extensive experiments on four public citation networks have demonstrated that our model can effectively improve node classification accuracy with considerably few labeled data, which significantly outperforms all state-of-the-art baselines by large margins. 

Abstract Liquid metals (LMs) have compelling applications in stretchable electronics, wearable devices, and soft robotics ascribing to the unique combination of room temperature fluidity and metallic electrical/thermal conductivity. Adding metallic elements in gallium‐based LMs can produce heterophasic (i.e., solid and liquid) LMs with altered properties including morphology, surface energy, rheology, electrical/thermal conductivity, and chemical reactivity. Importantly, heterophasic LMs can respond to external stimuli such as magnetic fields, temperature, and force. Thus, heterophasic LMs can broaden the potential applications of LMs. This report reviews the recent progress about heterophasic LMs through metallic elements in the periodic table and discusses their functionalities. The heterophasic LMs are systematically organized into four categories based on their features and applications including electrical/thermal conductivity, magnetic property, catalysis/energy management, and biomedical applications. This comprehensive review is aimed to help summarize the field and identify new opportunities for future studies. 

Abstract Polymer‐derived amorphous SiCN has excellent high‐temperature stability and properties. To reduce the shrinkage during pyrolysis and to improve the high‐temperature oxidation resistance, Y₂O₃was added as a filler. In this study, polymer‐derived SiCN–Y₂O₃composites were fabricated by mixing a polymeric precursor of SiCN with Y₂O₃submicron powders in different ratios. The mixtures were cross‐linked and pyrolyzed in argon. SiCN–Y₂O₃composites were processed using field‐assisted sintering technology at 1350°C for 5 min under vacuum. Dense SiCN–Y₂O₃composite pellets were successfully made with relative density higher than 98% and homogeneous microstructure. Due to low temperature and short time of the heat‐treatment, the grain growth of Y₂O₃was substantially inhibited. The Y₂O₃grain size was ∼1 μm after sintering. The composites’ heat capacity, thermal diffusivity, and thermal expansion coefficients were characterized as a function of temperature. The thermal conductivity of the composites ceramics decreased as the amount of amorphous SiCN increased and the coefficient of thermal expansion (CTE) of the composites increased with Y₂O₃content. However, the thermal conductivity and CTE did not follow the rule of mixture. This is likely due to the partial oxidation of SiCN and the resultant impurity phases such as Y₂SiO₅, Y₂Si₂O₇, and Y_4.67(SiO₄)₃O. 

Using a starlike Be 6 Au 7 − cluster as a building block and following the bottom-up strategy, an intriguing two-dimensional (2D) binary s-block metal Be 2 Au monolayer with a P 6/ mmm space group was theoretically designed. Both the Be 6 Au 7 − cluster and the 2D monolayer are global minima featuring rule-breaking planar hexacoordinate motifs (anti-van't Hoff/Le Bel arrangement), and their high stabilities are attributed to good electron delocalization and electronic-stabilization-induced steric force. Strikingly, the Be 2 Au monolayer is a rare Dirac material with two perfect Dirac node-loops in the band structure and is a phonon-mediated superconductor with a critical temperature of 4.0 K. The critical temperature can be enhanced up to 11.0 K by applying compressive strain at only 1.6%. This study not only identifies a new binary s-block metal 2D material, namely Be 2 Au, which features planar hexacoordination, and a candidate superconducting material for further explorations, but also provides a new strategy to construct 2D materials with novel chemical bonding. 

Solid molecular hydrogen has been predicted to be metallic and high-temperature superconducting at ultrahigh hydrostatic pressures that push current experimental limits. Meanwhile, little is known about the influence of nonhydrostatic conditions on its electronic properties at extreme pressures where anisotropic stresses are inevitably present and may also be intentionally introduced. Here we show by first-principles calculations that solid molecular hydrogen compressed to multimegabar pressures can sustain large anisotropic compressive or shear stresses that, in turn, cause major crystal symmetry reduction and charge redistribution that accelerate bandgap closure and promote superconductivity relative to pure hydrostatic compression. Our findings highlight a hitherto largely unexplored mechanism for creating superconducting dense hydrogen, with implications for exploring similar phenomena in hydrogen-rich compounds and other molecular crystals.