Abstract
The two-dimensional topological insulators host a full gap in the bulk band, induced by spin–orbit coupling (SOC) effect, together with the topologically protected gapless edge states. ...However, it is usually challenging to suppress the bulk conductance and thus to realize the quantum spin Hall (QSH) effect. In this study, we find a mechanism to effectively suppress the bulk conductance. By using the quasiparticle interference technique with scanning tunneling spectroscopy, we demonstrate that the QSH candidate single-layer 1
T
’-WTe
2
has a semimetal bulk band structure with no full SOC-induced gap. Surprisingly, in this two-dimensional system, we find the electron–electron interactions open a Coulomb gap which is always pinned at the Fermi energy (
E
F
). The opening of the Coulomb gap can efficiently diminish the bulk state at the
E
F
and supports the observation of the quantized conduction of topological edge states.
Atomically thin 2D crystals have gained tremendous attention owing to their potential impact on future electronics technologies, as well as the exotic phenomena emerging in these materials. ...Monolayers of α‐phase Sb (α‐antimonene), which shares the same puckered structure as black phosphorous, are predicted to be stable with precious properties. However, the experimental realization still remains challenging. Here, high‐quality monolayerα‐antimonene is successfully grown, with the thickness finely controlled. The α‐antimonene exhibits great stability upon exposure to air. Combining scanning tunneling microscopy, density functional theory calculations, and transport measurements, it is found that the electron band crossing the Fermi level exhibits a linear dispersion with a fairly small effective mass, and thus a good electrical conductivity. All of these properties make the α‐antimonene promising for future electronic applications.
Monolayer α‐phase antimonene, a structural analog to black phosphorous, is fabricated on a WTe2 substrate. The α‐antimonene exhibits great stability upon exposure to air. Its electron band crossing the Fermi level exhibits a linear dispersion with a fairly small effective mass, and thus a good electrical conductivity. All of these properties make α‐antimonene promising in future electronic applications.
A N2-bridged diiron complex Cp*(Ph2PC6H4S)Fe2(μ-N2) (1) has been found to catalyze the hydroboration of N-heteroarenes with pinacolborane, giving N-borylated 1,2-reduced products with high ...regioselectivity. The catalysis is initiated by coordination of N-heteroarenes to the iron center, while the B–H bond cleavage is the rate-determining step.
We prove several finite product-sum identities involving the q-binomial coefficient, one of which is used to prove an amazing identity of Gauss. We then use this identity to evaluate certain ...quadratic Gauss sums and, together with known properties of quadratic Gauss sums, we prove the quadratic reciprocity law for the Jacobi symbol. We end our article with a new proof of Jenkins’ lemma, a lemma analogous to Gauss’ lemma. This article aims to show that Gauss’ amazing identity and the properties of quadratic Gauss sums are sufficient to establish the quadratic reciprocity law for the Jacobi symbol.
Increased evidence shows that gut microbiota acts as the primary regulator of the liver; however, its role in sepsis-related liver injury (SLI) in the elderly is unclear. This study assessed whether ...metformin could attenuate SLI by modulating gut microbiota in septic-aged rats. Cecal ligation and puncture (CLP) was used to induce SLI in aged rats. Fecal microbiota transplantation (FMT) was used to validate the roles of gut microbiota in these pathologies. The composition of gut microbiota was analysed by 16S rRNA sequencing. Moreover, the liver and colon tissues were analysed by histopathology, immunofluorescence, immunohistochemistry, and reverse transcription polymerase chain reaction (RT-PCR). Metformin improved liver damage, colon barrier dysfunction in aged SLI rats. Moreover, metformin improved sepsis-induced liver inflammation and damage under gut microbiota. Importantly, FMT assay showed that rats gavaged with faeces from metformin-treated SLI rats displayed less severe liver damage and colon barrier dysfunctions than those gavaged with faeces from SLI rats. The gut microbiota composition among the sham-operated, CLP-operated and metformin-treated SLI rats was different. In particular, the proportion of Klebsiella and Escherichia_Shigella was higher in SLI rats than sham-operated and metformin-treated SLI rats; while metformin could increase the proportion of Bifidobacterium, Muribaculaceae, Parabacteroides_distasonis and Alloprevitella in aged SLI rats. Additionally, Klebsiella and Escherichia_Shigella correlated positively with the inflammatory factors in the liver. Our findings suggest that metformin may improve liver injury by regulating the gut microbiota and alleviating colon barrier dysfunction in septic-aged rats, which may be an effective therapy for SLI.
Pathogenic biofilms are up to 1000-fold more drug-resistant than planktonic pathogens and cause about 80% of all chronic infections worldwide. The lack of prompt and reliable biofilm identification ...methods seriously prohibits the diagnosis and treatment of biofilm infections. Here, we developed a machine-learning-aided cocktail assay for prompt and reliable biofilm detection. Lanthanide nanoparticles with different emissions, surface charges, and hydrophilicity are formulated into the cocktail kits. The lanthanide nanoparticles in the cocktail kits can offer competitive interactions with the biofilm and further maximize the charge and hydrophilicity differences between biofilms. The physicochemical heterogeneities of biofilms were transformed into luminescence intensity at different wavelengths by the cocktail kits. The luminescence signals were used as learning data to train the random forest algorithm, and the algorithm could identify the unknown biofilms within minutes after training. Electrostatic attractions and hydrophobic–hydrophobic interactions were demonstrated to dominate the binding of the cocktail kits to the biofilms. By rationally designing the charge and hydrophilicity of the cocktail kit, unknown biofilms of pathogenic clinical isolates were identified with an overall accuracy of over 80% based on the random forest algorithm. Moreover, the antibiotic-loaded cocktail nanoprobes efficiently eradicated biofilms since the nanoprobes could penetrate deep into the biofilms. This work can serve as a reliable technique for the diagnosis of biofilm infections and it can also provide instructions for the design of multiplex assays for detecting biochemical compounds beyond biofilms.
Anthocyanin is of great interest because of its many biological activities. However, its structure is extremely unstable, limiting its applications. In this study, we used polyethylene glycol (PEG) ...and chitosan as wall materials and purified mulberry marc (Morus alba L.) anthocyanin as the core materials to prepare a PEG- and chitosan-encapsulated anthocyanin complex. The best process, which was optimized with response surface methodology, used a 20:1 mass ratio of PEG4000 to chitosan and 1:3.8 ratio of core to wall materials. By regularly determining the total anthocyanin contents of the encapsulated anthocyanin complexes at different temperatures for one year, we found that its change was very small, and even at a higher temperature (35 °C), the total anthocyanin content was reduced only 3.11 ± 0.61% and with no significant difference. We also measured the antioxidant and anti-fatigue activities of the encapsulated anthocyanin after one year's storage and found that they were basically unchanged. Thus, anthocyanin's stability was enhanced significantly after encapsulation.
•The high hydrophilic PEG was tried to use as the main encapsulation material to encapsulate anthocyanin at the first time.•The formulation of preparing PEG- and chitosan-encapsulated anthocyanin complex was optimized by response surface methodology.•The stability of encapsulated anthocyanin was much higher than of unencapsulated.
Reported herein is a self-immobilizing near-infrared fluorogenic probe that can be used to image extracellular enzyme activity
. Using a fluorophore as a quinone methide precursor, this probe ...covalently anchors at sites of activation and greatly enhances the fluorescence intensity at 710 nm upon enzymatic stimulus, significantly boosting detection sensitivity in a highly dynamic
system.
Electrical treeing is a main cause of long term degradation of polymeric insulation and inorganic nanofillers have been added to improve the treeing resistance of polymers. In this work, a phase ...field model for the propagation of electrical tree in nanocomposites is presented, in which the damage status of insulation is described with a spatially and time dependent continuous variable. The evolution of damage phase is described with a modified Allen-Cahn equation, which is driven by the free energies originated from the phase separation, interface and electric field. The electric field distribution during growth of electrical tree is obtained by solving the iterative form of Poisson's equation with the spectral iterative perturbation method. Then the phase field model is applied to investigate the influence of nanofillers shape and distribution on propagation process of electrical tree. The results indicate that the phase field model can reproduce the dendritic shape of electrical tree, and the parallel nanosheet presents superior performance in hindering electrical tree than nanoparticle and random nanosheet.
•Machine learning methods were used to predict the thermal conductivity of polymers.•The training dataset was generated by large-scale molecular dynamics computations.•Machine learning models the ...polymers structure-thermal conductivity relationships.•Polymers containing chemical groups with strong bond strength give rise to high TC.•Polymer chains with well-ordered spatial structures usually present higher TC.
The ability to efficiently design new and advanced polymers with functional thermal properties is hampered by the high-cost and time-consuming experiments. Machine learning is an effective approach that can accelerate materials development by combining material science and big data techniques. Here, machine learning methods were used to predict the thermal conductivity of various single-chain polymers, and the relationship between molecular structures of polymer repeating units and thermal conductivity was also been investigated. The predict model starts from a benchmark dataset generated by large-scale molecular dynamics computations. In predict models, the polymers were ‘fingerprinted’ as simple, easily attainable numerical representations, which helps to develop an on-demand property prediction model. Further, potential quantitative relationship between molecular structures of polymer and thermal conductivity property was analyzed, and hypothetical polymers with ideal thermal conductivity were identified. The methods are shown to be general, and can hence guide the screening and systematic identification of high thermal conductivity.
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