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Topological insulators possess strong spin-orbit coupling, which potentially presents efficient charge-spin interconversion. The effective manipulation of this conversion plays a central role in spin-based device applications and is attracting increasing attention nowadays. In this study, by constructing a multifunctional hybrid device Cr-BST/Py/PMN-PT and applying spin-torque ferromagnetic resonance measurement, continuously controllable charge-spin conversion efficiency and even the enhancement of its value up to about 450% are realized via regulation of the ferroelectric polarization in the topological insulator Cr-BST. The band structure of Cr-BST characterized by angle-resolved photoelectron spectroscopy measurement presents an apparent Dirac-like state located at the large band gap of the bulk state near the Fermi level, which indicates a surface state-dominated contribution to the charge-spin conversion. Further investigation via density functional theory on the electronic structure of BST verifies that the controllable conversion efficiency dominantly originates from the evolution of the band structure under strain modulation. These findings demonstrate TIs as one of the promising materials for the charge-spin interconversion and its regulation, which are instructive for low-dissipation spintronics devices.

In magnetic topological insulators (TIs), the interplay between magnetic order and nontrivial topology can induce fascinating topological quantum phenomena, such as the quantum anomalous Hall effect, chiral Majorana fermions, and axion electrodynamics. Recently, a great deal of attention has been focused on the intrinsic magnetic TIs, where disorder effects can be eliminated to a large extent, which is expected to facilitate the emergence of topological quantum phenomena. Despite intensive efforts, experimental evidence of the topological surface states (SSs) remains elusive. Here, by combining first-principles calculations and angle-resolved photoemission spectroscopy (ARPES) experiments, we reveal that EuSn_{2}As_{2} is an antiferromagnetic TI with the observation of Dirac SSs consistent with our prediction. We also observe nearly gapless Dirac SSs in antiferromagnetic TIs MnBi_{2n}Te_{3n+1} (n=1 and 2), which are absent in previous ARPES results. These results provide clear evidence for nontrivial topology of these intrinsic magnetic TIs. Furthermore, we find that the topological SSs show no observable changes across the magnetic transition within the experimental resolution, indicating that the magnetic order has a quite small effect on the topological SSs, which can be attributed to weak hybridization between the localized magnetic moments, from either 4f or 3d orbitals, and the topological electronic states. This finding provides insights for further research that the correlations between magnetism and topological states need to be strengthened to induce larger gaps in the topological SSs, which will facilitate the realization of topological quantum phenomena at higher temperatures.

Background The diagnosis of gastric cancer mainly relies on endoscopy, which is invasive and costly. The aim of this study is to develop a predictive model for the diagnosis of gastric cancer based on noninvasive characteristics. Aims To construct a predictive model for the diagnosis of gastric cancer with high accuracy based on noninvasive characteristics. Methods A retrospective study of 709 patients at Zhejiang Provincial People's Hospital was conducted. Variables of age, gender, blood cell count, liver function, kidney function, blood lipids, tumor markers and pathological results were analyzed. We used gradient boosting decision tree (GBDT), a type of machine learning method, to construct a predictive model for the diagnosis of gastric cancer and evaluate the accuracy of the model. Results Of the 709 patients, 398 were diagnosed with gastric cancer; 311 were health people or diagnosed with benign gastric disease. Multivariate analysis showed that gender, age, neutrophil lymphocyte ratio, hemoglobin, albumin, carcinoembryonic antigen (CEA), carbohydrate antigen 125 (CA125) and carbohydrate antigen 199 (CA199) were independent characteristics associated with gastric cancer. We constructed a predictive model using GBDT, and the area under the receiver operating characteristic curve (AUC) of the model was 91%. For the test dataset, sensitivity was 87.0% and specificity 84.1% at the optimal threshold value of 0.56. The overall accuracy was 83.0%. Positive and negative predictive values were 83.0% and 87.8%, respectively. Conclusion We construct a predictive model to diagnose gastric cancer with high sensitivity and specificity. The model is noninvasive and may reduce the medical cost.