Bi-C monolayer as a promising 2D anode material for Li, Na, and K-ion batteries.

Electrode materials with high electrochemical efficiency are required for battery technology that can be used to store renewable energy. Bismuth (Bi) has shown great potential as an electrode material for metal ion batteries due to its large volumetric capacity and reasonable operating potential. However, the cycling performance deteriorates due to the drastic volume changes that occur during alloying and dealloying. Herein, we design a 2D Bi-C metal sheet using density functional theory and investigate the feasibility of this nanosheet for alkali metal ion batteries. The predicted metallic Bi-C monolayer (ML) are highly stable and show sound electrode performance. Moreover, alkali metal atoms exhibit high diffusivities on both sides (Bi and C sides) with low energy barriers of 0.252/0.201, 0.217/0.169, and 0.179/0.136 eV for Li, Na, and K ions, respectively. Furthermore, the Bi-C ML shows high theoretical storage capacities of (485 mA h g-1) for Li and Na and (364 mA h g-1) for K and low open-circuit voltage of 0.12, 0.24, and 0.32 V for Li, Na, and K ions, respectively. These exciting findings show that the predicted Bi-C ML can be used as an anode material for Li-, Na- and K-ion batteries.

Recent advancement in conjugated polymers based photocatalytic technology for air pollutants abatement: Cases of CO2, NOx, and VOCs.

According to World Health Organization (WHO) survey, air pollution has become the major reason of several fatal diseases, which had led to the death of 7 million peoples around the globe. The 9 people out of 10 breathe air, which exceeds WHO recommendations. Several strategies are in practice to reduce the emission of pollutants into the air, and also strict industrial, scientific, and health recommendations to use sustainable green technologies to reduce the emission of contaminants into the air. Photocatalysis technology recently has been raised as a green technology to be in practice towards the removal of air pollutants. The scientific community has passed a long pathway to develop such technology from the material, and reactor points of view. Many classes of photoactive materials have been suggested to achieve such a target. In this context, the contribution of conjugated polymers (CPs), and their modification with some common inorganic semiconductors as novel photocatalysts, has never been addressed in literature till now for said application, and is critically evaluated in this review. As we know that CPs have unique characteristics compared to inorganic semiconductors, because of their conductivity, excellent light response, good sorption ability, better redox charge generation, and separation along with a delocalized π-electrons system. The advances in photocatalytic removal/reduction of three primary air-polluting compounds such as CO2, NOX, and VOCs using CPs based photocatalysts are discussed in detail. Furthermore, the synergetic effects, obtained in CPs after combining with inorganic semiconductors are also comprehensively summarized in this review. However, such a combined system, on to better charges generation and separation, may make the Adsorb & Shuttle process into action, wherein, CPs may play the sorbing area. And, we hope that, the critical discussion on the further enhancement of photoactivity and future recommendations will open the doors for up-to-date technology transfer in modern research.

Magnetic and Magnetostrictive Behaviors of Laves-Phase Rare-Earth-Transition-Metal Compounds Tb1-xDyxCo1.95.

The magnetic morphotropic phase boundary (MPB) was first discovered in Laves-phase magnetoelastic system Tb-Dy-Co alloys (PRL 104, 197201 (2010)). However, the composition-dependent and temperature-dependent magnetostrictive behavior for this system, which is crucial to both practical application and the understanding of transitions across the MPB, is still lacking. In this work, the composition-dependence and temperature-dependence of magnetostriction for Tb1-xDyxCo1.95 (x = 0.3~0.8) are presented. In a ferrimagnetic state (as selected 100 K in the present work), the near-MPB compositions x = 0.6 and 0.7, exhibit the largest saturation magnetization MS and the lowest coercive field HC; by contrast, the off-MPB composition x = 0.5, exhibits the largest magnetostriction, the lowest MS, and the largest HC. Besides, a sign change of magnetostriction is observed, which occurs with the magnetic transition across the MPB. Our results suggest the combining effect from the lattice strain induced from structure phase transition, and the influence of the MPB on magnetocrystalline anisotropy. This work may stimulate the research interests on the transition behavior around the MPB and its relationship with physical properties, and also provide guidance in designing high-performance magnetostrictive materials for practical applications.

Giant Vertical Magnetization Shift Caused by Field-Induced Ferromagnetic Spin Reconfiguration in Ni50Mn36Ga14 Alloy.

Vertical magnetization shift (VMS) is a special type of exchange bias effect that may lead to a revolution in future ultrahigh-density magnetic recording technology. However, there are very few reports focusing on the performance of VMS due to the unclear mechanism. In this paper, a giant vertical magnetization shift (ME) of 6.34 emu/g is reported in the Ni50Mn36Ga14 alloy. The VMS can be attributed to small ferromagnetic ordered regions formed by spin reconfiguration after field cooling, which are embedded in an antiferromagnetic matrix. The strong cooling-field dependence, temperature dependence, and training effect all corroborate the presence of spin reconfiguration and its role in the VMS. This work can enrich VMS research and increase its potential in practical applications as well.

Salient region detection through salient and non-salient dictionaries.

Low-rank representation-based frameworks are becoming popular for the saliency and the object detection because of their easiness and simplicity. These frameworks only need global features to extract the salient objects while the local features are compromised. To deal with this issue, we regularize the low-rank representation through a local graph-regularization and a maximum mean-discrepancy regularization terms. Firstly, we introduce a novel feature space that is extracted by combining the four feature spaces like CIELab, RGB, HOG and LBP. Secondly, we combine a boundary metric, a candidate objectness metric and a candidate distance metric to compute the low-level saliency map. Thirdly, we extract salient and non-salient dictionaries from the low-level saliency. Finally, we regularize the low-rank representation through the Laplacian regularization term that saves the structural and geometrical features and using the mean discrepancy term that reduces the distribution divergence and connections among similar regions. The proposed model is tested against seven latest salient region detection methods using the precision-recall curve, receiver operating characteristics curve, F-measure and mean absolute error. The proposed model remains persistent in all the tests and outperformed against the selected models with higher precision value.

Appearance-Based Salient Regions Detection Using Side-Specific Dictionaries.

Image saliency detection is a very helpful step in many computer vision-based smart systems to reduce the computational complexity by only focusing on the salient parts of the image. Currently, the image saliency is detected through representation-based generative schemes, as these schemes are helpful for extracting the concise representations of the stimuli and to capture the high-level semantics in visual information with a small number of active coefficients. In this paper, we propose a novel framework for salient region detection that uses appearance-based and regression-based schemes. The framework segments the image and forms reconstructive dictionaries from four sides of the image. These side-specific dictionaries are further utilized to obtain the saliency maps of the sides. A unified version of these maps is subsequently employed by a representation-based model to obtain a contrast-based salient region map. The map is used to obtain two regression-based maps with LAB and RGB color features that are unified through the optimization-based method to achieve the final saliency map. Furthermore, the side-specific reconstructive dictionaries are extracted from the boundary and the background pixels, which are enriched with geometrical and visual information. The approach has been thoroughly evaluated on five datasets and compared with the seven most recent approaches. The simulation results reveal that our model performs favorably in comparison with the current saliency detection schemes.