Direct Observation of Topological Phonons in Graphene.

Phonons, as the most fundamental emergent bosons in condensed matter systems, play an essential role in the thermal, mechanical, and electronic properties of crystalline materials. Recently, the concept of topology has been introduced to phonon systems, and the nontrivial topological states also exist in phonons due to the constraint by the crystal symmetry of the space group. Although the classification of various topological phonons has been enriched theoretically, experimental studies were limited to several three-dimensional (3D) single crystals with inelastic x-ray or neutron scatterings. The experimental evidence of topological phonons in two-dimensional (2D) materials is absent. Here, using high-resolution electron energy loss spectroscopy following our theoretical predictions, we directly map out the phonon spectra of the atomically thin graphene in the entire 2D Brillouin zone, and observe two nodal-ring phonons and four Dirac phonons. The closed loops of nodal-ring phonons and the conical structure of Dirac phonons in 2D momentum space are clearly revealed by our measurements, in nice agreement with our theoretical calculations. The ability of 3D mapping (2D momentum space and energy space) of phonon spectra opens up a new avenue to the systematic identification of the topological phononic states. Our work lays a solid foundation for potential applications of topological phonons in superconductivity, dynamic instability, and phonon diode.

Fluorine-Containing Triazole-Decorated Silver(I)-Based Cationic Metal-Organic Framework for Separating Organic Dyes and Removing Oxoanions from Water.

Four new triazole-decorated silver(I)-based cationic metal-organic frameworks (MOFs), {[Ag(L1)](BF4)}n (1), {[Ag(L1)](NO3)}n (2), {[Ag(L2)](BF4)}n (3), and {[Ag(L2)](NO3)}n (4), have been synthesized using two newly designed ligands, 3-fluoro-5-(4H-1,2,4-triazol-4-yl)pyridine (L1) and 3-(4H-1,2,4-triazol-4-yl)-5-(trifluoromethyl)pyridine (L2). When the fluorine atom was changed to a trifluoromethyl group at the same position, tremendous enhancement in the MOF dimensionality was achieved [two-dimensional to three-dimensional (3D)]. However, changing the metal salt (used for the synthesis) had no effect. The higher electron-withdrawing tendency of the trifluoromethyl group in L2 aided in the formation of higher-dimensional MOFs with different properties compared with those of the fluoro derivatives. The fluoride group was introduced in the ligand to make highly electron-deficient pores inside the MOFs that can accelerate the anion-exchange process. The concept was proved by density functional theory calculation of the MOFs. Both 3D cationic MOFs were used for dye adsorption, and a remarkable amount of dye was adsorbed in the MOFs. In addition, owing to their cationic nature, the MOFs selectively removed anionic dyes from a mixture of anionic, cationic, and neutral dyes in the aqueous phase. Interestingly, the present MOFs were also highly effective for the removal of oxoanions (MnO4- and Cr2O72-) from water.

Observation of the nonanalytic behavior of optical phonons in monolayer hexagonal boron nitride.

Phonon splitting of the longitudinal and transverse optical modes (LO-TO splitting), a ubiquitous phenomenon in three-dimensional polar materials, will break down in two-dimensional (2D) polar systems. Theoretical predictions propose that the LO phonon in 2D polar monolayers becomes degenerate with the TO phonon, displaying a distinctive "V-shaped" nonanalytic behavior near the center of the Brillouin zone. However, the full experimental verification of these nonanalytic behaviors has been lacking. Here, using monolayer hexagonal boron nitride (h-BN) as a prototypical example, we report the comprehensive and direct experimental verification of the nonanalytic behavior of LO phonons by inelastic electron scattering spectroscopy. Interestingly, the slope of the LO phonon in our measurements is lower than the theoretically predicted value for a freestanding monolayer due to the screening of the Cu foil substrate. This enables the phonon polaritons in monolayer h-BN/Cu foil to exhibit ultra-slow group velocity (~5 × 10-6 c, c is the speed of light) and ultra-high confinement (~ 4000 times smaller wavelength than that of light). These exotic behaviors of the optical phonons in h-BN presents promising prospects for future optoelectronic applications.

Evaluations on supervised learning methods in the calibration of seven-hole pressure probes.

Machine learning method has become a popular, convenient and efficient computing tool applied to many industries at present. Multi-hole pressure probe is an important technique widely used in flow vector measurement. It is a new attempt to integrate machine learning method into multi-hole probe measurement. In this work, six typical supervised learning methods in scikit-learn library are selected for parameter adjustment at first. Based on the optimal parameters, a comprehensive evaluation is conducted from four aspects: prediction accuracy, prediction efficiency, feature sensitivity and robustness on the failure of some hole port. As results, random forests and K-nearest neighbors' algorithms have the better comprehensive prediction performance. Compared with the in-house traditional algorithm, the machine learning algorithms have the great advantages in the computational efficiency and the convenience of writing code. Multi-layer perceptron and support vector machines are the most time-consuming algorithms among the six algorithms. The prediction accuracy of all the algorithms is very sensitive to the features. Using the features based on the physical knowledge can obtain a high accuracy predicted results. Finally, KNN algorithm is successfully applied to field measurements on the angle of attack of a wind turbine blades. These findings provided a new reference for the application of machine learning method in multi-hole probe calibration and measurement.

Discussion on the mechanism of Lingguizhugan Decoction in treating hypertension based on network pharmacology and molecular simulation technology.

To explore the mechanism of Lingguizhugan Decoction in treating hypertension based on network pharmacology and molecular simulation. The active ingredients and potential targets were screened by the Systematic Pharmacological Analysis Platform of Traditional Chinese Medicine (TCMSP). Hypertension-related targets were obtained from OMIM and GeneCards databases. Common targets between drug and hypertension were screened in the Venny platform. A protein-protein interaction (PPI) network was constructed in the STRING database using intersection targets. Key targets in PPI network were analyzed by Cytoscape. R language program was used for Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Finally, the binding abilities of the main active ingredients to critical targets were verified by molecular simulation. Naringenin, quercetin, kaempferol, and ß-sitosterol in Lingguizhugan Decoction, and potential targets such as STAT3, AKT1, TNF, IL6, JUN, PTGS2, MMP9, CASP3, TP53, and MAPK3, were screened out. KEGG Enrichment analysis revealed that the common targets of Lingguizhugan Decoction and hypertension are mainly involved in the lipid and atherosclerosis signaling pathway, AGE-RAGE signaling pathway in diabetic complications, fluid shear stress and atherosclerosis, and IL17 signaling pathway. The molecular simulation results showed that naringenin-MAPK3, quercetin-MMP9, quercetin-PTGS2, and quercetin-TP53 were the top four in the docking scores. Naringenin-MAPK3 and quercetin-MMP9 were stable, with binding free energies of -27.97 ± 1.41 kcal/mol and -21.15 ± 3.17 kcal/mol, respectively. The possible mechanism of Lingguizhugan Decoction in treating hypertension is characterized of multi-component, multi-target, and multi-pathway.Communicated by Ramaswamy H. Sarma.

Fabricated Metal-Organic Frameworks (MOFs) as luminescent and electrochemical biosensors for cancer biomarkers detection.

In the health domain, a major challenge is the detection of diseases using rapid and cost-effective techniques. Most of the existing cancer detection methods show poor sensitivity and selectivity and are time consuming with high cost. To overcome this challenge, we analyzed porous fabricated metal-organic frameworks (MOFs) that have better structures and porosities for enhanced biomarker sensing. Here, we summarize the use of fabricated MOF luminescence and electrochemical sensors in devices for cancer biomarker detection. Various strategies of fabrication and the role of fabricated materials in sensing cancer biomarkers have been studied and described. The structural properties, sensing mechanisms, roles of noncovalent interactions, limits of detection, modeling, advantages, and limitations of MOF sensors have been well-discussed. The study presents an innovative technique to detect the cancer biomarkers by the use of luminescence and electrochemical MOF sensors. In addition, the potential association studies have been opening the way for personalized patient treatments and the development of new cancer-detecting devices.

A Method for Identifying Vesicle Transport Proteins Based on LibSVM and MRMD.

With the development of computer technology, many machine learning algorithms have been applied to the field of biology, forming the discipline of bioinformatics. Protein function prediction is a classic research topic in this subject area. Though many scholars have made achievements in identifying protein by different algorithms, they often extract a large number of feature types and use very complex classification methods to obtain little improvement in the classification effect, and this process is very time-consuming. In this research, we attempt to utilize as few features as possible to classify vesicular transportation proteins and to simultaneously obtain a comparative satisfactory classification result. We adopt CTDC which is a submethod of the method of composition, transition, and distribution (CTD) to extract only 39 features from each sequence, and LibSVM is used as the classification method. We use the SMOTE method to deal with the problem of dataset imbalance. There are 11619 protein sequences in our dataset. We selected 4428 sequences to train our classification model and selected other 1832 sequences from our dataset to test the classification effect and finally achieved an accuracy of 71.77%. After dimension reduction by MRMD, the accuracy is 72.16%.