Ruddlesden–Popper perovskite sulfides A3B2S7: A new family of ferroelectric photovoltaic materials for the visible spectrum

Perovskite ferroelectric materials exhibit the novel ferroelectric photovoltaic effect, where photon-excited electron–hole pairs can be separated by ferroelectric polarization. Especially, semiconducting ferroelectric materials with small band gaps (E[subscript g]) have been extensively studied for applications in solar energy conversion. Traditional route for creating semiconducting ferroelectrics requires cation doping, where E[subscript g] of the insulating perovskite ferroelectric oxides are reduced via substitution of certain cations. But cation doping tends to reduce the carrier mobility due to the scattering, and usually lead to poor photovoltaic efficiency. In the present work, based on first-principles calculations, we propose and demonstrate a new strategy for designing stoichiometric semiconducting perovskite ferroelectric materials. Specifically, we choose the parent non-polar semiconducting perovskite sulfides AB S[subscript 3] with Pnma symmetry, and turn them into ferroelectric Ruddlesden–Popper A[subscript 3]B[subscript 2]S[subscript 7] perovskites with spontaneous polarizations. Our predicted Ruddlesden–Popper Ca[subscript 3]Zr[subscript 2]S[subscript 7] and other derived compounds exhibit the room-temperature stable ferroelectricity, small band gaps (E[subscript g] < 2.2 eV) suitable for the absorption of visible light, and large visible-light absorption exceeding that of Si.National Basic Research Program of China (973 Program) (Contract 2012CB619402)National Natural Science Foundation (China) (Contract 11574244)China. Ministry of Education (Program for Innovative Research Team in University. Contract IRT13034)National Science Foundation (U.S.) (Grant DMR-1410636

Giant dynamical electron-magnon coupling in metal-metal-ferromagnetic insulator heterostructure

Magnon-mediated spin transport across nonmagnetic metal (NM) and ferromagnetic insulator (FI) interface depends critically on electron-magnon coupling. We propose a novel route to enhance electron-magnon coupling dynamically from transport viewpoint. Using non-equilibrium Green's function a theoretical formalism for magnon-mediated spin current is developed. In the language of transport, the effective electron-magnon coupling at NM/FI interface is determined by self-energy of FI lead, which is proportional to density of states (DOS) at NM/FI interface due to nonlinear process of electron-magnon conversion. By modifying interfacial DOS, the spin conductance of 2D and 3D NM/FI systems can be increased by almost three orders of magnitude, setting up a new platform of manipulating dynamical electron-magnon coupling

HRTEM Study on Resistive Switching ZrO2 Thin Films and Their Micro-Fabricated Thin Films

For the next-generation nonvolatile memory material, the most promising candidate is resistive random access memory (RRAM) which is nonvolatile memory with high density, high speed, and low power consumption. Resistive switching (RS) behavior had been reported in various films including transition metal oxides, perovskite, and chalcogenide. For further application, it is still a challenge to fabricate nanostructures of RS material. Micro-fabrication method involves traditional lithography, chemical etching, electron beam direct writing, nano-imprint, and so on. However, the procedure and the cost of these methods are relatively complex and high for semiconductors process. In this chapter, we demonstrate a method for fabricating sub-micro ZrO2 lattice by using sol-gel method combined with laser interference lithography and micro-analysis with high-resolution transmission electron microscopy (HRTEM)

Unified framework of the microscopic Landau-Lifshitz-Gilbert equation and its application to Skyrmion dynamics

The Landau-Lifshitz-Gilbert (LLG) equation is widely used to describe magnetization dynamics. We develop a unified framework of the microscopic LLG equation based on the nonequilibrium Green's function formalism. We present a unified treatment for expressing the microscopic LLG equation in several limiting cases, including the adiabatic, inertial, and nonadiabatic limits with respect to the precession frequency for a magnetization with fixed magnitude, as well as the spatial adiabatic limit for the magnetization with slow variation in both its magnitude and direction. The coefficients of those terms in the microscopic LLG equation are explicitly expressed in terms of nonequilibrium Green's functions. As a concrete example, this microscopic theory is applied to simulate the dynamics of a magnetic Skyrmion driven by quantum parametric pumping. Our work provides a practical formalism of the microscopic LLG equation for exploring magnetization dynamics

The impact of environmental information disclosure quality on green innovation of high-polluting enterprises

With the gradual increase of social awareness of environmental protection, environmental information disclosure has become the key for enterprises to accept social supervision and fulfill their social responsibility. This study examines the high-polluting enterprises that were listed on Chinese A-shares between 2008 and 2021. The influence of environmental information disclosure quality on green innovation is examined using ordinary least squares (OLS) as a benchmark model. The results show that the improvement of environmental information disclosure quality of high-polluting enterprises can significantly improve the quantity and quality of green innovation of enterprises and are mediated by alleviating financing constraints and enhancing cash reserves. Moreover, improving the quality of environmental information disclosure of highly polluting enterprises has a more significant contribution to the quantity and quality of green patents of non-state-owned enterprises, enterprises located in central and eastern China, and large enterprises. The findings of this paper provide theoretical support for achieving a “win-win” situation of environmental protection and green innovation

STF-based diagnosis of AUV thruster faults

The diagnosis of thruster faults of autonomous underwater vehicles is studied in this paper. Based on the theory of strong tracking filter (STF), the AUV motion model and the thruster fault model are established. The STFs are designed for each thruster for the purpose of fault diagnosis. The AUV state and the fault deviation of the thruster are estimated online before the thruster faults are diagnosed based on residual analysis. The simulation experiments were conducted to verify the feasibility and effectiveness of the STF-based diagnosis of AUV thruster faults

Involvement of CYP2E1 in the Course of Brain Edema Induced by Subacute Poisoning With 1,2-Dichloroethane in Mice

This study was designed to explore the role of cytochrome P4502E1 (CYP2E1) expression in the course of brain edema induced by subacute poisoning with 1,2-dichloroethane (1,2-DCE). Mice were randomly divided into five groups: the control group, the 1,2-DCE poisoned group, and the low-, medium- and high-dose diallyl sulfide (DAS) intervention groups. The present study found that CYP2E1 expression levels in the brains of the 1,2-DCE-poisoned group were upregulated transcriptionally; in contrast, the levels were suppressed by DAS pretreatment in the intervention groups. In addition, the expression levels of both Nrf2 and HO-1 were also upregulated transcriptionally in the brains of the 1,2-DCE-poisoned group, while they were suppressed dose-dependently in the intervention groups. Moreover, compared with the control group, MDA levels and water contents in the brains of the 1,2-DCE-poisoned group increased, whereas NPSH levels and tight junction (TJ) protein levels decreased significantly. Conversely, compared with the 1,2-DCE- poisoned group, MDA levels and water contents in the brains of the intervention groups decreased, and NPSH levels and TJ protein levels increased significantly. Furthermore, pathological changes of brain edema observed in the 1,2-DCE-poisoned group were markedly improved in the intervention groups. Collectively, our results suggested that CYP2E1 expression could be transcriptionally upregulated in 1,2-DCE-poisoned mice, which might enhance 1,2-DCE metabolism in vivo, and induce oxidative damage and TJ disruption in the brain, ultimately leading to brain edema

Mechanical, economic, and environmental assessment of recycling reclaimed asphalt rubber pavement using different rejuvenation schemes

Asphalt rubber (AR) is a prevailing sustainable paving material, but the recycling of reclaimed asphalt rubber pavement (RARP) faces the lack of proper rejuvenation strategies and the uncertainties in its economic and environmental values. To fill these gaps, this study assesses the comprehensive performance of RARP-included mixtures using different rejuvenation schemes through mechanical tests, life cycle cost analysis (LCCA), and life cycle assessment. The addition of rejuvenators increased the rutting depth of RARP mixture but their effects on the cracking resistance varied from different types of rejuvenators. The supplement of swollen rubber content was more effective in improving the cracking and aging resistances. Recycling 40 % of RARP into new AR mixture can save 24.5 % of greenhouse gas (GHG) emissions and 35 % of cost within the geographic scope of Hong Kong. The use of rejuvenator increased the GHG emission and cost, while incorporating extra rubber could further reduce the cost

Adaptive SPP–CNN–LSTM–ATT wind farm cluster short-term power prediction model based on transitional weather classification

With the expansion of the scale of wind power integration, the safe operation of the grid is challenged. At present, the research mainly focuses on the prediction of a single wind farm, lacking coordinated control of the cluster, and there is a large prediction error in transitional weather. In view of the above problems, this study proposes an adaptive wind farm cluster prediction model based on transitional weather classification, aiming to improve the prediction accuracy of the cluster under transitional weather conditions. First, the reference wind farm is selected, and then the improved snake algorithm is used to optimize the extreme gradient boosting tree (CBAMSO-XGB) to divide the transitional weather, and the sensitive meteorological factors under typical transitional weather conditions are optimized. A convolutional neural network (CNN) with a multi-layer spatial pyramid pooling (SPP) structure is utilized to extract variable dimensional features. Finally, the attention (ATT) mechanism is used to redistribute the weight of the long and short term memory (LSTM) network output to obtain the predicted value, and the cluster wind power prediction value is obtained by upscaling it. The results show that the classification accuracy of the CBAMSO-XGB algorithm in the transitional weather of the two test periods is 99.5833% and 95.4167%, respectively, which is higher than the snake optimization (SO) before the improvement and the other two algorithms; compared to the CNN–LSTM model, the mean absolute error (MAE) of the adaptive prediction model is decreased by approximately 42.49%–72.91% under various transitional weather conditions. The relative root mean square error (RMSE) of the cluster is lower than that of each reference wind farm and the prediction method without upscaling. The results show that the method proposed in this paper effectively improves the prediction accuracy of wind farm clusters during transitional weather