Modifying Electronic Structure of Bismuth Telluride Through S Doping and Te Vacancy Engineering for Enhanced Zn-Ion Storage Ability in Aqueous Zn-proton Hybrid Ion Batteries.

Bismuth chalcogenides are used as cathode materials in Zn-proton hybrid ion batteries, which exhibit an ultraflat discharge plateau that is favorable for practical applications. Unfortunately, their capacity is not competitive, and their charge storage mechanisms are ambiguous, both of which hinder their further development. In this study, S-doped Bi2Te3- x (SBT) nanosheets are prepared by tellurizing a Bi2O2S precursor using a hydrothermal process. As revealed by density functional theory analyses, the S dopant and its induced Te vacancies can distinctly manipulate the electronic structure of SBT, resulting in decent electrical conductivity and more negative adsorption energy to Zn2+. These advantages boost the Zn2+ storage ability of SBT materials. Consequently, compared with defect-free Bi2Te3, the SBT cathodes have superior specific capacity, rate capability, and cycling stability.

Extended least squares support vector machine with applications to fault diagnosis of aircraft engine.

Recently, a robust least squares support vector machine (R-LSSVM) was proposed, but its computational complexity is very high compared with the traditional least squares support vector machine (LSSVM). To reduce R-LSSVM's computational complexity, an improved version, i.e., extended LSSVM (E-LSSVM), is developed in this paper. E-LSSVM and R-LSSVM are equivalent in terms of the generalization performance, but the former needs lower computational complexity than the latter. It is proved that the traditional LSSVM is a special case of E-LSSVM, and based on this fact, we know that the bias in the traditional LSSVM owns manifest physical meaning, i.e., the mean of the modeling error. To solve the mathematical model of E-LSSVM, two algorithms, DE-LSSVM (dual E-LSSVM) and PE-LSSVM (primal E-LSSVM), are proposed from dual and primal spaces, respectively. Even competing against the traditional LSSVM, DE-LSSVM takes the edge in term of the training time. In addition, the sparse problem and cross validation of DE-LSSVM are discussed as well. To verify the effectiveness and soundness of the proposed DE-LSSVM and PE-LSSVM, experiments on regression and classification problems are investigated. To be more important, DE-LSSM and PE-LSSVM are successfully applied to the fault diagnosis of aircraft engine, showing that they are eligible for potential techniques of the fault diagnosis of aircraft engine.