Search

2D transition metal dichalcogenides (TMDs) play increasingly significant roles in research and future optoelectronics. However, the large-scale deposition of 2D TMDs remains challenging due to sparse nucleation and substrate dependency. Liquid metals can offer effective solutions to meet these challenges due to their reactive, non-polarized, and templating properties. Here, self-deposition of 2D molybdenum sulfide is shown by introducing a molybdenum precursor onto the surface of a eutectic alloy of gallium and indium (EGaIn). EGaIn serves as an ultra-smooth template and reducing agent for the precursor to form large-scale planar molybdenum sulfides, which is transferrable to any substrate. The molybdenum sulfides form spontaneously on the surface of EGaIn, which has a sufficient potential to drive the cathodic reactions of the deposition process. A highly crystalline 2H-MoS2 is obtained after a final annealing step. This work demonstrates a fundamentally new capability for the formation of large-scale 2D TMDs.

Phonon-polaritons (PhPs) in layered crystals, including hexagonal boron nitride (hBN), have been investigated by combined scattering-type scanning near-field optical microscopy (s-SNOM) and Fourier transform infrared (FTIR) spectroscopy. Nevertheless, many of such s-SNOM-based FTIR spectra features remain unexplored, especially those originated from the impact of boundaries. Here we observe real-space PhP propagations in thin-layer hBN sheets either supported or suspended by s-SNOM imaging. Then with a high-power broadband IR laser source, we identify two major peaks and multiple auxiliary peaks in the near-field amplitude spectra, obtained using scattering-type near-field FTIR spectroscopy, from both supported and suspended hBN. The major PhP propagation interference peak moves toward the major in-plane phonon peak when the IR illumination moves away from the hBN edge. Specific differences between the auxiliary peaks in the near-field amplitude spectra from supported and suspended hBN sheets are investigated regarding different boundary conditions, associated with edges and substrate interfaces. The outcomes may be explored in heterostructures for advanced nanophotonic applications.

The nascent field of nanotechnology-enabled metallurgy has great potential. However, the role of eutectic alloys and the nature of alloy solidification in this field are still largely unknown. To demonstrate one of the promises of liquid metals in the field, we explore a model system of catalytically active Bi-Sn nano-alloys produced using a liquid-phase ultrasonication technique and investigate their phase separation, surface oxidation, and nucleation. The Bi-Sn ratio determines the grain boundary properties and the emergence of dislocations within the nano-alloys. The eutectic system gives rise to the smallest grain dimensions among all Bi-Sn ratios along with more pronounced dislocation formation within the nano-alloys. Using electrochemical CO2 reduction and photocatalysis, we demonstrate that the structural peculiarity of the eutectic nano-alloys offers the highest catalytic activity in comparison with their non-eutectic counterparts. The fundamentals of nano-alloy formation revealed here may establish the groundwork for creating bimetallic and multimetallic nano-alloys.

AbstractRoad network selection plays a key role in map generalization for creating multi-scale road network maps. Existing methods usually determine road importance based on road geometric and topological features, few evaluate road importance from the perspective of road utilization based on human travel data, ignoring the functional values of roads, which leads to a mismatch between the generated results and people’s needs. This paper develops two functional semantic features (i.e. travel path selection probability and regional attractiveness) to measure the functional importance of roads and proposes an automatic road network selection method based on graph convolutional networks (GCN), which models road network selection as a binary classification. Firstly, we create a dual graph representing the source road network and extract road features including six graphical and two functional semantic features. Then, we develop an extended GCN model with connectivity loss for generating multi-scale road networks and propose a refinement strategy based on the road continuity principle to ensure road topology. Experiments demonstrate the proposed model with functional features improves the quality of selection results, particularly for large and medium scale maps. The proposed method outperforms state-of-the-art methods and provides a meaningful attempt for artificial intelligence models empowering cartography. [ABSTRACT FROM AUTHOR]

Exploring and controlling surface tension‐driven phenomena in liquid metals may lead to unprecedented possibilities for next‐generation microfluidics, electronics, catalysis, and materials synthesis. In pursuit of these goals, the impact of minor constituents within liquid alloys is largely overlooked. Herein, it is showed that the presence of a fraction of solute metals such as tin, bismuth, and zinc in liquid gallium can significantly influence their electrocapillarity and electrochemistry. The instability‐driven fractal formation of liquid alloy droplets is investigated with different solutes and reveals the formation of distinctive non‐branched droplets, unstable fractals, and stable fractal modes under controlled voltage and alkaline solution conditions. In their individually unique fractal morphology diagrams, different liquid alloys demonstrate significantly shifted voltage thresholds in transition between the three fractal modes, depending on the choice of the solute metal. Surface tension measurements, cycle voltammetry and surface compositional characterizations provide strong evidence that the minor alloy components drastically alter the surface tension, surface electrochemical oxidation, and oxide dissolution processes that govern the droplet deformation and instability dynamics. The findings that minor components are able to regulate liquid alloys' surface tensions, surface element distributions and electrochemical activities offer great promises for harnessing the tunability and functionality of liquid metals. [ABSTRACT FROM AUTHOR]

Liquid metals provide new dimensions for controlling and governing of reactions. The concept of freely moving metal solutes in liquid metals can be potentially used for enhancing and tuning interfacial reactions. In this work, zinc (Zn), as the solute metal is shown that, can move to the interface, enriching the interfacial area for the efficient synthesis of highly crystalline and porous zeolitic imidazolate framework‐8 (ZIF‐8). The highest rate of reaction is illustrated, and the best quality ZIF‐8, are obtained when a eutectic liquid metal system of Zn with gallium (Ga), containing 3.6 wt.% of Zn, is implemented. It is computationally shown that a combination of Ga activation and freely moving Zn leads to the highest reaction rate and better coordination with organic linkers. The comparisons with solid samples and non‐eutectic systems of Ga liquid demonstrate the advantages of eutectic mix. The work can be expanded to a variety of future interfacial reactions of zeolite imidazole frameworks for commercial scale up. [ABSTRACT FROM AUTHOR]

Abstract\nHIGHLIGHTSWith the development of geographic information services, applications based on map data are increasing. Internet and mobile communication technologies have brought great convenience to the distribution, sharing, and acquisition of map data. Unfortunately, it also brings great challenges to protect the security and privacy of confidential map data. Although several encryption algorithms have been developed for map data, most of the existing methods are simple extensions of classical encryption algorithms for text data in cryptography, ignoring special structures and characteristics of geospatial data. Existing methods mainly take the entire map or layers as encryption units, which makes the encryption efficiency for large-scale map data low and limits the ability to perform local and incremental encryption for local areas, making it still challenging to meet the needs of map data in applications, such as autonomous driving. To solve the limitations, a local encryption approach for large-scale vector map data is proposed in this paper considering the structure characteristics of map data. Experiments on map data of different types (including points, lines, and polygons) demonstrate the effectiveness of the proposed method. With local and parallel encryption strategies, the proposed method has higher efficiency compared to available algorithms for large-scale map data.A local encryption approach for vector map data is proposed, which can be applied to large-scale vector map data and support localized encryption/decryption for user-interested areas instead of the whole map data.The proposed method estimates different encryption parameters for different units which could significantly enhance the resistance to attacks and naturally supports parallel computing.With local and parallel encryption strategies, the proposed method has higher efficiency compared to available algorithms for large-scale map data.The method has great value and potential for ensuring map data security in ubiquitous GIS-based applications, such as map data sharing and dynamic updating in automatic driving and crowdsourced mapping. [ABSTRACT FROM AUTHOR]

Crystallization of alloys from a molten state is a fundamental process underpinning metallurgy. Here the direct imaging of an intermetallic precipitation reaction at equilibrium in a liquid‐metal environment is demonstrated. It is shown that the outer layers of a solidified intermetallic are surprisingly unstable to the depths of several nanometers, fluctuating between a crystalline and a liquid state. This effect, referred to herein as crystal interface liquefaction, is observed at remarkably low temperatures and results in highly unstable crystal interfaces at temperatures exceeding 200 K below the bulk melting point of the solid. In general, any liquefaction process would occur at or close to the formal melting point of a solid, thus differentiating the observed liquefaction phenomenon from other processes such as surface pre‐melting or conventional bulk melting. Crystal interface liquefaction is observed in a variety of binary alloy systems and as such, the findings may impact the understanding of crystallization and solidification processes in metallic systems and alloys more generally. [ABSTRACT FROM AUTHOR]

Spatial community detection is a method that divides geographic spaces into several sub-regions based on spatial interactions, reflecting the regional spatial structure against the background of human mobility. In recent years, spatial community detection has attracted extensive research in the field of geographic information science. However, mining the community structures and their evolutionary patterns from spatial interaction data remains challenging. Most existing methods for spatial community detection rely on representing spatial interaction networks in Euclidean space, which results in significant distortion when modeling spatial interaction networks; since spatial community detection has no ground truth, this results in the detection and evaluation of communities being difficult. Furthermore, most methods usually ignore the dynamics of these spatial interaction networks, resulting in the dynamic evolution of spatial communities not being discussed in depth. Therefore, this study proposes a framework for community detection and evolutionary analysis for spatial interaction networks. Specifically, we construct a spatial interaction network based on network science theory, where geographic units serve as nodes and interaction relationships serve as edges. In order to fully learn the structural features of the spatial interaction network, we introduce a hyperbolic graph convolution module in the community detection phase to learn the spatial and non-spatial attributes of the spatial interaction network, obtain vector representations of the nodes, and optimize them based on a graph generation model to achieve the final community detection results. Considering the dynamics of spatial interactions, we analyze the evolution of the spatial community over time. Finally, using taxi trajectory data as an example, we conduct relevant experiments within the fifth ring road of Beijing. The empirical results validate the community detection capabilities of the proposed method, which can effectively describe the dynamic spatial structure of cities based on human mobility and provide an effective analytical method for urban spatial planning. [ABSTRACT FROM AUTHOR]

Low‐temperature polycrystalline silicon (LTPS) thin‐film transistor devices can be used as core components of pixel driving circuits for active matrix organic light‐emitting displays （AMOLED）. Poly silicon (P‐Si) film and device performance directly affects the display effect. This paper investigates polysilicon grain boundary reduction for LTPS devices on PI substrates, exploring the effects of grain boundary reduction on LTPS devices and image sticking. Reduction of polysilicon grain boundaries through optimization of a‐Si deposition process, a‐Si two‐step deposition and excimer laser annealing (ELA) doublescan process, ELA double‐scan process reduces grain boundaries by 67 %. We prepared LTPS devices with thinner gate interlayers (GI) based on P‐Si grain boundary reduction, LTPS devices with positive threshold voltage bias and increased mobility, AMOLED displays with thinner GI can have 23% better image sticking. [ABSTRACT FROM AUTHOR]

To efficiently establish a finite element model that is highly compatible with the intricate microstructure of foam materials, this study carries out simulation and experimental investigations into open cell foams and explores their compression behavior assuming isotropy. Using an in-house algorithm, we optimize the traditional power diagram model, which is widely used in foam finite element simulation. Specifically, a parameterized geometric foam model is developed based on the centroidal and capacity constrained power diagram (CCCPD), which accurately reflects the internal meso-structure of foam materials. The optimized foam model can restore the typical deformation process of foams under compressive load, thus addressing the limitations of existing foam models. The accuracy of the optimized model is validated by conducting uniaxial Quasi-static compression experiments on foamed thermoplastic polyurethane (TPU) material and comparing the experimental results of the 3D printing model with the simulation results of the optimized model. The improved foam model serves as a basis for future research on open cell foam materials. [ABSTRACT FROM AUTHOR]