Search

The atomic substitution in two-dimensional (2D) materials is propitious to achieving compositionally engineered semiconductor heterostructures. However, elucidating the mechanism and developing methods to synthesize 2D heterostructures with atomic-scale precision are crucial. Here, we demonstrate the synthesis of monolayer WSe2–WS2heterostructures with a relatively sharp interface from monolayer WSe2using a chalcogen atom-exchange synthesis route at high temperatures for short periods. The substitution was initiated at the edges of monolayer WSe2and the lateral diffuse along the heterointerface, and the reaction can be controlled by the precise reaction time and temperature. The lateral heterostructure and substitution process are studied by Raman and photoluminescence (PL) spectroscopies, electron microscopy, and device characterization, revealing a possible mechanism of strain-induced transformation. Our findings demonstrate a highly controllable synthesis of 2D layered materials through atom substitutional chemistry and provide a simple route to control the atomic structure.

Due to protection of extracellular polymeric substances, the therapeutic efficiency of conventional antimicrobial agents is often impeded by their poor infiltration and accumulation in biofilm. Herein, one type of surface charge adaptable nitric oxide (NO) nanogenerator was developed for biofilm permeation, retention and eradication. This nanogenerator (PDG@Au–NO/PBAM) is composed of a core-shell structure: thermo-sensitive NO donor conjugated AuNPs on cationic poly(dopamine-co-glucosamine) nanoparticle (PDG@Au–NO) served as core, and anionic phenylboronic acid-acryloylmorpholine (PBAM) copolymer was employed as a shell. The NO nanogenerator featured long circulation and good biocompatibility. Once the nanogenerator reached acidic biofilm, its surface charge would be switched to positive after shell dissociation and cationic core exposure, which was conducive for the nanogenerator to infiltrate and accumulate in the depth of biofilm. In addition, the nanogenerator could sustainably generate NO to disturb the integrity of biofilm at physiological temperature, then generate hyperthermia and explosive NO release upon NIR irradiation to efficiently eradicate drug-resistant bacteria biofilm. Such rational design offers a promising approach for developing nanosystems against biofilm-associated infections.

The contamination of pathogenic micro-organisms and heavy metals in drinking water sources poses a serious threat to human health, which raises the demand for efficient water treatments. Herein, multi-functional capacitive deionization (CDI) electrodes were developed for the simultaneous decontamination of bacteria and heavy metal contaminants. Polyhexamethylene guanidine (PHMG), an antibacterial polymer, was deposited on the surface of the activated carbon (AC) electrode with the assistance of mussel-inspired polydopamine (PDA) chemistry. The main characterization results proved successful co-deposition of PDA and PHMG on the AC electrode, forming a hydrophilic coating layer in one step. Electrochemical analyses indicated that the AC-PDA/PHMG electrodes presented satisfactory capacitive behaviors, with outstanding salt adsorption capacity and cycling stability. The modified electrodes also exhibit excellent disinfection performance and heavy metal adsorption performance. The bacterial elimination rate of co-deposited electrodes grew along with the increase in the PHMG content. Particularly, AC-PDA/PHMG2electrodes successfully removed and deactivated 99.11% Escherichia coliand 98.67% Pseudomonas aeruginosa(104CFU mL–1) in water within 60 min. Furthermore, three flow cells made by AC-PDA/PHMG2electrodes connected in series achieved efficient removal of salt, heavy metals such as lead and cadmium, and bacteria simultaneously, which indicated that the adsorption performance is significantly improved compared with pristine AC electrodes. These results denote the enormous potential of this one-step prepared multi-functional electrodes for facile and effective water purification using CDI technology.

The discovery of intrinsic ferromagnetism in ultrathin two-dimensional van der Waals crystals opens up exciting prospects for exploring magnetism in the ultimate two-dimensional limit. Here, we show that environmentally stable CrSe2nanosheets can be readily grown on a dangling-bond-free WSe2substrate with systematically tunable thickness down to the monolayer limit. These CrSe2/WSe2heterostructures display high-quality van der Waals interfaces with well-resolved moiré superlattices and ferromagnetic behaviour. We find no apparent change in surface roughness or magnetic properties after months of exposure in air. Our calculations suggest that charge transfer from the WSe2substrate and interlayer coupling within CrSe2play a critical role in the magnetic order in few-layer CrSe2nanosheets. The highly controllable growth of environmentally stable CrSe2nanosheets with tunable thickness defines a robust two-dimensional magnet for fundamental studies and potential applications in magnetoelectronic and spintronic devices.

Two-dimensional (2D) materials1,2and the associated van der Waals (vdW) heterostructures3–7have provided great flexibility for integrating distinct atomic layers beyond the traditional limits of lattice-matching requirements, through layer-by-layer mechanical restacking or sequential synthesis. However, the 2D vdW heterostructures explored so far have been usually limited to relatively simple heterostructures with a small number of blocks8–18. The preparation of high-order vdW superlattices with larger number of alternating units is exponentially more difficult, owing to the limited yield and material damage associated with each sequential restacking or synthesis step8–29. Here we report a straightforward approach to realizing high-order vdW superlattices by rolling up vdW heterostructures. We show that a capillary-force-driven rolling-up process can be used to delaminate synthetic SnS2/WSe2vdW heterostructures from the growth substrate and produce SnS2/WSe2roll-ups with alternating monolayers of WSe2and SnS2, thus forming high-order SnS2/WSe2vdW superlattices. The formation of these superlattices modulates the electronic band structure and the dimensionality, resulting in a transition of the transport characteristics from semiconducting to metallic, from 2D to one-dimensional (1D), with an angle-dependent linear magnetoresistance. This strategy can be extended to create diverse 2D/2D vdW superlattices, more complex 2D/2D/2D vdW superlattices, and beyond-2D materials, including three-dimensional (3D) thin-film materials and 1D nanowires, to generate mixed-dimensional vdW superlattices, such as 3D/2D, 3D/2D/2D, 1D/2D and 1D/3D/2D vdW superlattices. This study demonstrates a general approach to producing high-order vdW superlattices with widely variable material compositions, dimensions, chirality and topology, and defines a rich material platform for both fundamental studies and technological applications.

Two-dimensional layered transition-metal dichalcogenides (TMDs) have attracted intense interest for their layer number-dependent electronic properties and have exciting potential for atomically thin electronics and optoelectronics. The studies to date have primarily been limited to exfoliated materials with limited control of the size, layer number, and yield. Despite considerable efforts to date, it remains a significant challenge to produce large-sized TMD single crystals with precise control of the layer number. Here, we report the robust growth of high-quality WSe2single-crystal domains with a selectively controlled thickness in a reverse-flow chemical vapor deposition system with a solid precursor. By introducing a pre-annealing step to tune elemental distribution and volatilization rate of the solid precursor, we stabilize the vapor supply to achieve a highly uniform nucleation and growth, and thus ensure the precise control of the layer number for the highly selective growth of monolayer or bilayer WSe2single crystals (>500 μm). The transmission electron microscopy and optical characterizations of the resulting WSe2single crystals exhibit excellent crystalline quality with systematically tunable optical properties. Electrical transport studies further show that the WSe2field-effect transistors exhibit p-type semiconductor characteristics with effective hole carrier mobility up to 92 cm2V–1s–1in monolayer and 145 cm2V–1s–1in bilayer materials at room temperature. This simple approach opens up a new avenue for the highly controlled synthesis of WSe2atomic layers for both fundamental studies and technological applications.

Two-dimensional materials (2DMs) with excellent mechanical, thermal, optical, and catalytic properties have attracted a great deal of attention in recent years. Chemical vapor deposition (CVD) is an important method to realize the synthesis of high-quality 2DMs. In the growth of 2DMs through the CVD method, the substrates play an important role and can greatly affect the lateral size, composition, thickness, orientation, and crystal quality of 2DMs. In this review, we first introduce the growth mechanism and the key parameters in the CVD system for the synthesis of 2DMs. Then, the unique physical and chemical properties, advantages, and disadvantages of various substrates used in the CVD technique are summarized. Finally, the opportunities and challenges about the use of the substrate in the CVD process in the future are discussed.

Monolayer transition metal dichalcogenides (TMDs) have attracted considerable attention as atomically thin semiconductors for the ultimate transistor scaling. For practical applications in integrated electronics, large monolayer single crystals are essential for ensuring consistent electronic properties and high device yield. The TMDs available today are generally obtained by mechanical exfoliation or chemical vapor deposition (CVD) growth, but are often of mixed layer thickness, limited single crystal domain size or have very slow growth rate. Scalable and rapid growth of large single crystals of monolayer TMDs requires maximization of lateral growth rate while completely suppressing the vertical growth, which represents a fundamental synthetic challenge and has motivated considerable efforts. Herein we report a modified CVD approach with controllable reverse flow for rapid growth of large domain single crystals of monolayer TMDs. With the use of reverse flow to precisely control the chemical vapor supply in the thermal CVD process, we can effectively prevent undesired nucleation before reaching optimum growth temperature and enable rapid nucleation and growth of monolayer TMD single crystals at a high temperature that is difficult to attain with use of a typical thermal CVD process. We show that monolayer single crystals of 450 μm lateral size can be prepared in 10 s, with the highest lateral growth rate up to 45 μm/s. Electronic characterization shows that the resulting monolayer WSe2material exhibits excellent electronic properties with carrier mobility up to 90 cm2V−1s−1, comparable to that of the best exfoliated monolayers. Our study provides a robust pathway for rapid growth of high-quality TMD single crystals.With a reverse flow to control the chemical vapour supply in CVD process, we can effectively prevent the undesired nucleation before reaching optimum growth temperature and enable rapid growth of monolayer TMD single crystals.

Two-dimensional van der Waals heterostructures (vdWHs) have attracted considerable interest1–4. However, most vdWHs reported so far  are created by an arduous micromechanical exfoliation and manual restacking process5, which—although versatile for proof-of-concept demonstrations6–16and fundamental studies17–30—is clearly not scalable for practical technologies. Here we report a general synthetic strategy for two-dimensional vdWH arrays between metallic transition-metal dichalcogenides (m-TMDs) and semiconducting TMDs (s-TMDs). By selectively patterning nucleation sites on monolayer or bilayer s-TMDs, we precisely control the nucleation and growth of diverse m-TMDs with designable periodic arrangements and tunable lateral dimensions at the predesignated spatial locations, producing a series of vdWH arrays, including VSe2/WSe2, NiTe2/WSe2, CoTe2/WSe2, NbTe2/WSe2, VS2/WSe2, VSe2/MoS2and VSe2/WS2. Systematic scanning transmission electron microscopy studies reveal nearly ideal vdW interfaces with widely tunable moiré superlattices. With the atomically clean vdW interface, we further show that the m-TMDs function as highly reliable synthetic vdW contacts for the underlying WSe2with excellent device performance and yield, delivering a high ON-current density of up to 900 microamperes per micrometre in bilayer WSe2transistors. This general synthesis of diverse two-dimensional vdWH arrays provides a versatile material platform for exploring exotic physics and promises a scalable pathway to high-performance devices.

With the worldwide increasing concern regarding environmental protection and controlling carbon emission, carbon dioxide (CO2) displacement in the subsurface becomes a vital process in environmental engineering and petroleum engineering. In this study, we propose a new “D3Q27” three fluid phase lattice Boltzmann method (TPLBM) based on a multiple relaxation time algorithm to study the flow behavior of three fluids, water, CO2, and oil in this study. A series of TPLBM simulation studies are carried out on a three-dimensional digital replica of a rock (sandstone) to investigate the microprocesses and mechanisms of CO2displacement from its oil–water–CO2systems. Data gathered from high-resolution X-ray computed tomography scanning of a sandstone sample from Daqing oilfield, Longhupao, Songliao Basin, China, is used to construct the rock replica. TPLBM numerical simulations are conducted to analyze the microdisplacement of CO2in sandstone under different injection conditions. The results show that the displacement efficiency is higher at higher injection rates and pressure differences, with a constant total volume of injected CO2. The optimal injection rate and pressure difference for CO2displacement in sandstone are identified. The average ultimate CO2displacement efficiency is 90.1% when the injection rate remains constant, whereas the average ultimate CO2displacement efficiency is 95.8% when the injection pressure difference remains constant. The proposed method offers a new method for analyzing the pore-scale three fluid phase flow in sandstone.

In this paper, we propose the community search framework searching polarized communities via adaptively fusing structure and attribute in attributed signed networks, which searches for two polarized subgraphs on an attributed signed network for given query nodes. We first conduct a analysis by the similarity of attributes between nodes. And we adaptively integrate topology and node attributes into an augmented signed network. Then, a spectral method based on generalized Rayleigh quotient is proposed. Finally, a linear programming problem is designed to detect polarized communities by local eigenspace. Experiments on real-world datasets demonstrate the effectiveness of our method.

Heterogeneous information network (HIN) has recently been widely adopted to describe complex graph structure in recommendation systems, proving its effectiveness in modeling complex graph data. Although existing HIN-based recommendation studies have achieved great success by performing message propagation between connected nodes on the defined metapaths, they have the following major limitations. Existing works mainly convert heterogeneous graphs into homogeneous graphs via defining metapaths, which are not expressive enough to capture more complicated dependency relationships involved on the metapath. Besides, the heterogeneous information is more likely to be provided by item attributes while social relations between users are not adequately considered. To tackle these limitations, we propose a novel social recommendation model MPISR, which models MetaPath Interaction for Social Recommendation on heterogeneous information network. Specifically, our model first learns the initial node representation through a pretraining module, and then identifies potential social friends and item relations based on their similarity to construct a unified HIN. We then develop the two-way encoder module with similarity encoder and instance encoder to capture the similarity collaborative signals and relational dependency on different metapaths. Extensive experiments on five real datasets demonstrate the effectiveness of our method.

In order to further improve the yield of aromatic monomers from renewable resources, the selective cleavage of C–C bonds in lignin get more and more attention. In this work, Cs3Bi2Br9(CBB) and covalent triazine frameworks (CTFs) complexes were firstly prepared by facile melting salt method. Due to CBB quantum dots embedded in CTF frame structure, the conversion rate of the composite photocatalyst for 2-phenoxy-1-phenylethanol (pp-ol) reached 100% under visible-light irradiation in 6 h. In-depth investigations verified that the electronic structure of the photocatalyst could be adjusted by changing calcination temperature. The electronic structure of the photocatalyst affected the type and the tendency of free radical, and then affected the selectivity of C-C bond cleavage. This work provides a simple method to prepare photocatalytic and used for aromatics production from renewable biomass feedstocks.

Surface adsorption plays a crucial role in various natural and industrial processes, particularly in the field of energy storage. The adsorption of sodium atoms on 2D layered materials can significantly impact their performance as carriers and electrodes in ion batteries. While it is commonly acknowledged that pristine graphene is not favorable for sodium ion adsorption, the suitability of other 2D materials with similar honeycomb symmetry remains unclear. In this study, we employ systematic first-principles calculations to explore interlayer interactions and electron transfer effects on sodium adsorption on 2D van der Waals (vdW) heterostructures (HTSs) surfaces. Our results demonstrate that sodium adsorption is energetically favorable on these substrates. Moreover, we find that the adsorption strength can be effectively tuned by manipulation of the electron accumulation or depletion of the layer directly interacting with the sodium atom. By stacking these layered materials with different electron abundancy to form vdW HTSs, the charge density of the substrate becomes tunable through interlayer charge transfer. In these vdW HTSs, the adsorption behavior of sodium is primarily controlled by the absorption layer and exhibits a linear correlation with its pz-band center. Additionally, we identify linear correlations between the sodium adsorption energies, the electron loss of the sodium atom, the interlayer charge transfer, and the heights of the adsorbed sodium atom. These discoveries underscore the impact of interlayer electron transfer and interactions on sodium ion adsorption on 2D vdW HTSs and providing new insights into material design for alkali atom adsorption.

Organic–inorganic hybrid perovskites have attracted tremendous attention owing to their fascinating optoelectronic properties. However, their poor air stability seriously hinders practical applications, which becomes more serious with thickness down to the nanoscale. Here we report a one-step vapor phase growth of HC(NH2)2PbBr3(FAPbBr3) single-crystalline nanosheets of tunable size up to 50 μm and thickness down to 20 nm. The FAPbBr3nanosheets demonstrate high stability for over months of exposure to air with no degradation in surface roughness and photoluminescence efficiency. Besides, the FAPbBr3photodetectors exhibit superior overall performance as compared to previous devices based on nonlayered perovskite nanosheets, such as an ultralow dark current of 24 pA, an ultrahigh responsivity of 1033 A/W, an external quantum efficiency over 3000%, a rapid response time around 25 ms, and a high on/off ratio of 104. This work provides a strategy to tackle the challenges of hybrid perovskites toward integrated optoelectronics with requirements of nanoscale thickness, high stability, and excellent performance.

Dynamic capillary pressure (DCP) is the capillary pressure defined under transient flow condition during displacement, and it is vital for predicting two‐phase behavior in porous media. This article studies the effect of pore scale force and interfacial area by using captivating Lattice Boltzmann method based on a pseudo‐potential model developed for simulating incompressible multiphase flow in porous media. We analyze the relationship between pore scale forces and DCP based on the energy conservation law and define DCP as a function of energy rate of pressure and viscosity of each phase. We present two‐phase displacement simulations on a computed tomography (CT) image‐based porous model and analyze the effects of injection rate and wettability on DCP based on the proposed upscaling method, where the wettability is defined as the contact angle of the nonwetting phase. The primary results show that (1) the DCP curves are higher than that of the quasi‐static capillary pressure, and a higher injection rate leads to a larger DCP and a faster saturation change. (2) Significant effects of wettability on DCP and the DCP coefficients are observed, where the DCP coefficient is defined as the ratio of the difference between DCP and static capillary pressure over the rate of change of saturation. A larger contact angle results in a higher DCP and a lower change rate of saturation, and consequently induces a larger DCP coefficient. The average DCP coefficient is found to vary from 4.56 × 106to 3.55 × 105Pa· mswhen the nonwetting phase contact angle changes from 140° to 105° in the saturation range of 0.3 to 0.8. This study indicates that the proposed upscaling method is valid to investigate the DCP of two‐phase flow in sandstone. A novel upscaling method of dynamic capillary pressure is proposedRate dependent dynamic capillary pressure is observed based on 3‐D CT image‐based porous modelSignificant effects of wettability on the dynamic capillary pressure and the dynamic capillary pressure coefficient are observed

Two-dimensional (2D) metallic transition metal dichalcogenides (MTMDs) have recently drawn increasing interest for fundamental studies and potential applications in catalysis, charge density wave (CDW), interconnections, spin-torque devices, as well superconductors. Despite some initial efforts, the thickness-tunable synthesis of atomically thin MTMDs remains a considerable challenge. Here we report controlled synthesis of 2D cobalt telluride (CoTe2) nanosheets with tunable thickness using an atmospheric pressure chemical vapor deposition (APCVD) approach and investigate their thickness-dependent electronic properties. The resulting nanosheets show a well-faceted hexagonal or triangular geometry with a lateral dimension up to ∼200 μm. Systematic studies of growth at varying growth temperatures or flow rates demonstrate that nanosheets thickness is readily tunable from over 30 nm down to 3.1 nm. X-ray diffraction (XRD), transmission electron microscopy (TEM), and high-resolution scanning transmission electron microscope (STEM) studies reveal the obtained CoTe2nanosheets are high-quality single crystals in the hexagonal 1T phase. Electrical transport studies show the 2D CoTe2nanosheets display excellent electrical conductivities up to 4.0 × 105S m–1and very high breakdown current densities up to 2.1 × 107A/cm2, both with strong thickness tunability.

Two-dimensional (2D) layered materials have stimulated extensive research interest for their unique thickness-dependent electronic and optical properties. However, the layer-number-dependent studies on 2D materials to date are largely limited to exfoliated flakes with relatively small lateral size and poor yield. The direct synthesis of 2D materials with a precise control of the number of atomic layers remains a substantial synthetic challenge. Here we report a systematic study of chemical vapor deposition synthesis of large-area atomically thin 2D nickel telluride (NiTe2) single crystals and investigate the thickness dependent electronic properties. By controlling the growth temperature, we show that the highly uniform NiTe2single crystals can be synthesized with precisely tunable thickness varying from 1, 2, 3, . . . to multilayers with a standard deviation (∼0.3 nm) of less than the thickness of a monolayer layer NiTe2. Our studies further reveal a systematic evolution of single crystal domain size and nucleation density with the largest lateral domain size up to ∼440 μm. X-ray diffraction, transmission electron microscopy, and high resolution scanning transmission electron microscope studies demonstrate that the resulting 2D crystals are high quality single crystals and adopt hexagonal 1T phase. Electrical transport studies reveal that the 2D NiTe2single crystals show a strong thickness-tunable electrical properties, with an excellent conductivity up to 7.8 × 105S m–1and extraordinary breakdown current density up to 4.7 × 107A/cm2. The systematic study and robust synthesis of NiTe2nanosheets defines a reliable chemical route to 2D single crystals with precisely tailored thickness and could enable the design of new device architectures based on thickness-tunable electrical properties.

The deep learning methods based on syntactic dependency tree have achieved great success on Aspect-based Sentiment Analysis (ABSA). However, the accuracy of the dependency parser cannot be determined, which may keep aspect words away from its related opinion words in a dependency tree. Moreover, few models incorporate external affective knowledge for ABSA. Based on this, we propose a novel architecture to tackle the above two limitations, while fills up the gap in applying heterogeneous graphs convolution network to ABSA. Specially, we employ affective knowledge as an sentiment node to augment the representation of words. Then, linking sentiment node which have different attributes with word node through a specific edge to form a heterogeneous graph based on dependency tree. Finally, we design a multi-level semantic heterogeneous graph convolution network (Semantic-HGCN) to encode the heterogeneous graph for sentiment prediction. Extensive experiments are conducted on the datasets SemEval 2014 Task 4, SemEval 2015 task 12, SemEval 2016 task 5 and ACL 14 Twitter. The experimental results show that our method achieves the state-of-the-art performance.

Community search is an important problem in network analysis, which has attracted much attention in recent years. As a query-oriented variant of community detection problem, community search starts with some given nodes, pays more attention to local network structures, and gets personalized resultant communities quickly. The existing community search method typically returns a single target community containing query nodes by default. This is a strict requirement and does not allow much flexibility. In many real-world applications, however, query nodes are expected to be located in multiple communities with different semantics. To address this limitation of existing methods, an efficient spectral-based Multi-Scale Community Search method (MSCS) is proposed, which can simultaneously identify the multi-scale target local communities to which query node belong. In MSCS, each node is equipped with a graph Fourier multiplier operator. The access of the graph Fourier multiplier operator helps nodes to obtain feature representations at various community scales. In addition, an efficient algorithm is proposed for avoiding the large number of matrix operations due to spectral methods. Comprehensive experimental evaluations on a variety of real-world datasets demonstrate the effectiveness and efficiency of the proposed method.

Determining the side effects of multidrug combinations is a very important issue in drug risk studies. However, designing clinical trials to determine frequencies is often time-consuming and expensive, and previous work has often been limited to using the target protein of a drug without screening. Although this alleviates the sparsity of the raw data to some extent, blindly introducing proteins as auxiliary information can lead to a large amount of noisy information being added, which in turn leads to less efficient models. For this reason, we propose a new method called Gorge (graph convolutional networks on heterogeneous multi-relational graphs for polypharmacy side effect prediction). Specifically, we designed two protein auxiliary pathways directly related to drugs and combined these two auxiliary pathways with a multi-relational graph of drug side effects, which both alleviates data sparsity and filters noisy data. Then, we introduce a query-aware attention mechanism that generates different attention pathways for drug entities based on different drug pairs, fine-grained to determine the extent of information delivery. Finally, we output the exact frequency of drug side effects occurring through a tensor factorization decoder, in contrast to most existing methods that can only predict the presence or association of side effects, but not their frequency. We found that Gorge achieves excellent performance on real-world datasets (average AUROC of 0.822 and average AUPR of 0.775), outperforming existing methods. Further examination provides literature evidence for highly ranked predictions.

Molybdenum phosphide (MoP) has been extensively experimentally shown to possess high and surprisingly increasing hydrodesulfurization (HDS) activities during the HDS process. In order to understand the HDS mechanism, we investigate the HDS of thiophene on clean and single-sulfur-atom-modified MoP(001) using self-consistent periodic density functional theory (DFT). Thiophene strongly prefers flatadsorption, which is slightly weakened in the presence of a surface S atom. Thermodynamic and kinetic analyses of the elementary steps show that the HDS of thiophene takes place along the direct desulfurization (DDS) pathway on both clean and S-modified MoP(001), because of the very low C–S bond activation barriers as well as very high exothermicities involved. More importantly, the surface S atom does not elevate the C–S bond activation barriers but opens a new concerted pathway for the simultaneous rupture of both C–S bonds in thiophene. These results indicate that the presence of a surface S atom could be helpful for thiophene desulfurization. For comparison, we also investigate the influence of a surface S atom on the HDS of thiophene on Pt(111). The results show clearly a negative effect of the surface S atom, in accordance with the lower sulfur resistance of noble metals.

Community detection methods based on random walks are widely adopted in various network analysis tasks. It could capture structures and attributed information while alleviating the issues of noises. Though random walks on plain networks have been studied before, in real-world networks, nodes are often not pure vertices, but own different characteristics, described by the rich set of data associated with them. These node attributes contain plentiful information that often complements the network, and bring opportunities to the random-walk-based analysis. However, node attributes make the node interactions more complicated and are heterogeneous with respect to topological structures. Accordingly, attributed community detection based on random walk is challenging as it requires joint modelling of graph structures and node attributes. To bridge this gap, we propose a Community detection with Attributed random walk via Seed replacement (CAS). Our model is able to conquer the limitation of directly utilize the original network topology and ignore the attribute information. In particular, the algorithm consists of four stages to better identify communities. (1) Select initial seed nodes in the network; (2) Capture the better-quality seed replacement path set; (3) Generate the structure-attribute interaction transition matrix and perform the colored random walk; (4) Utilize the parallel conductance to expand the communities. Experiments on synthetic and real-world networks demonstrate the effectiveness of CAS.

Community search enables personalized community discovery and has wide applications in real-life scenarios. Existing attributed community search algorithms use personalized information provided by attributes to locate desired community. Though achieved promising results, existing works suffer from two major limitations: (i) the precision of the algorithm decreases significantly when the seed comes from the boundary regions of the community. (ii) Most attributed community search methods mainly take the attribute information as edge weights to reveal semantic strength (e.g., attribute similarity, attribute distance, etc.), but largely ignore that attribute may serve as heterogeneous vertex. To make up for these deficiencies, in this paper, we propose a novel two-stage attributed community search method with seed replacement and joint random walk (SRRW). Specifically, in the seed replacement stage, we replace the initial query node with a core node; in the random walk stage, attributes are taken as heterogeneous nodes and the augmented graph is modeled based on the affiliation of the attributes via an overlapping clustering algorithm. And finally, a joint random walk is performed on the augmented graph to explore the desired local community. We conduct extensive experiments on both synthetic and real-world benchmarks, demonstrating its effectiveness for attributed community search.

This paper studies the problem of community search,which aims to search a community for a query vertex in an HIN. Specifically, we design a weighting strategy and query node replacement for the community search problem on heterogeneous networks. The weighting strategy enables the induced homogeneous graph to contain more semantic information; the query node replacement strategy can find better quality nodes for community search. We conduct extensive experiments on real-world graphs to show that our proposed methods can provide high-quality results over HINs.

Based on the generalized stochastic nonlinear Schrödinger equation, the effect of intrapulse Raman scattering (IRS) on broadband amplitude noise of supercontinuum (SC) generated in the normal dispersion regime is investigated numerically. The results show that, in the normal dispersion regime, where the IRS contributes less to the bandwidth of the SC spectrum, the broadband amplitude noise of SC is amplified significantly in the process of SC generation because of the existence of IRS effect. Using fiber with an optimal negative dispersion slope, the IRS effect can be suppressed, and thus the SC amplitude noise is reduced without spectral bandwidth loss.

Social recommendation has become an important technique of various online commerce platforms, which aims to predict the user preference based on the social network and the interactive network. Social recommendation, which can naturally integrate social information and interactive structure, has been demonstrated to be powerful in solving data sparsity and cold-start problems. Although some of the existing methods have been proven effective, the following two insights are often neglected. First, except for the explicit connections, social information contains implicit connections, e.g., indirect social relations. Indirect social relations can effectively improve the quality of recommendation when users only have few direct social relations. Second, the strength of social influence between users is different. In other words, users have different degrees of trust in different friends. These insights motivate us to propose a novel social recommendation model SIER (short for Social Influence-based Effective Recommendation) in this paper, which incorporates interactive information and social information into personal latent factors learning for social influence-based recommendation. Specifically, user preferences are captured in behavior history and social relations, i.e., user latent factors are shared in interactive network and social network. In particular, we utilize an overlapping community detection method to sufficiently capture the implicit relations in the social network. Extensive experiments on two real-world datasets demonstrate the effectiveness of the proposed method.

Circularly polarized light (CPL) is essential for optoelectronic and chiro-spintronic applications. Hybrid perovskites, as star optoelectronic materials, have demonstrated CPL activity, which is, however, mostly limited to chiral perovskites. Here, we develop a simple, general, and efficient strategy to stimulate CPL activity in achiral perovskites, which possess rich species, efficient luminescence, and tunable bandgaps. With the formation of van der Waals heterojunctions between chiral and achiral perovskites, a nonequilibrium spin population and thus CPL activity are realized in achiral perovskites by receiving spin-polarized electrons from chiral perovskites. The polarization degree of room-temperature CPL in achiral perovskites is at least one order of magnitude higher than in chiral ones. The CPL polarization degree and emission wavelengths of achiral perovskites can be flexibly designed by tuning chemical compositions, operating temperature, or excitation wavelengths. We anticipate that unlimited types of achiral perovskites can be endowed with CPL activity, benefiting their applications in integrated CPL sources and detectors.

The Li2S-based cathodes with a high theoretical capacity of 1,166 mAh/g are regarded as the commercially available lithium-sulfur battery that can be coupled with Li-free anodes to avoid the safety concern of lithium metal. However, the instinct Li2S passivation leads to a high activation potential and low sulfur utilization with the notorious polysulfide migration. Herein, the single-atom tailoring strategy in designing liquid Li2S2catholyte is created without the use of any complicated manufacturing processes or additives, where the Li2S2cell enables the 3.0 V activation voltage without potential barrier. Meanwhile, the conductive and polar tungsten boride offer the active center to anchor polysulfides migration. As expected, the tungsten boride-Li2S2cathode exhibits excellent cycling stability, impressive cycling performance without initial potential barrier.

Collaborator recommendation aims to seek suitable collaborators for a given author. In this paper, we model all authors and their features as an attributed graph, and then perform community search on the attributed graph to locate the best collaborator community. From the early collaborative filtering-based methods to the recent deep learning-based methods, most existing works usually unilaterally weigh the network structure or node attributes, or directly search the community via the given node. We argue that the inherent disadvantage of these methods is that the quality of the node to be recommended may not be high, which can lead to suboptimal recommendation results.