Directly Deposited Antimony on a Copper Silicide Nanowire Array as a High-Performance Potassium-Ion Battery Anode with a Long Cycle Life

Antimony (Sb) is a promising anode material for potassium-ion batteries (PIBs) due to its high capacity and moderate working potential. Achieving stable electrochemical performance for Sb is hindered by the enormous volume variation that occurs during cycling, causing a significant loss of the active material and disconnection from conventional current collectors (CCs). Herein, the direct growth of a highly dense copper silicide (Cu₁₅Si₄) nanowire (NW) array from a Cu mesh substrate to form a 3D CC is reported that facilitates the direct deposition of Sb in a core-shell arrangement (Sb@Cu₁₅Si₄ NWs). The 3D Cu₁₅Si₄ NW array provides a strong anchoring effect for Sb, while the spaces between the NWs act as a buffer zone for Sb expansion/contraction during K–cycling. The binder-free Sb@Cu₁₅Si₄ anode displays a stable capacity of 250.2 mAh g⁻¹ at 200 mA g⁻¹ for over 1250 cycles with a capacity drop of ≈0.028% per cycle. Ex situ electron microscopy revealed that the stable performance is due to the complete restructuring of the Sb shell into a porous interconnected network of mechanically robust ligaments. Notably, the 3D Cu₁₅Si₄ NW CC is expected to be widely applicable for the development of alloying-type anodes for next-generation energy storage devices.     

Study on growth,optical and dielectric properties of ‘Nd’ DOPED NBT-BT (0.94(Na0.5Bi0.5tio3)-0.06batio3) relaxor ferroelectric single crystals

Journal of Electroceramics - The present work describes the effect of Neodymium (Nd) in the NBT-BT (0.94Na0.5Bi0.5TiO3-0.06BaTiO3 (NBT-BT (94/06))?+?xNd (x?=?0, 0.4, 0.6,...     

High Energetic High Density Materials: A BASIC Program (FCALC) for the design and selection of efficient high energetic materials containing nitro and azido groups

A program named “FCALC” has been developed in BASIC language to calculate the detonation volumes (through a factor F) for various explosives as well as for any new organic structure. The number and kind of substiments that need to be incorporated into an unsubstituted organic compound in order to achieve maximum F factor, and thus maximum detonation velocity, can also be predicted. The program calculates F factors for a user-defined set of combinations of substituents.     

Light-Regulated Pro-Angiogenic Engineered Living Materials

Regenerative medicine aims to restore damaged cells, tissues, and organs, for which growth factors are vital to stimulate regenerative cellular transformations. Major advances have been made in growth factor engineering and delivery like the development of robust peptidomimetics and controlled release matrices. However, their clinical applicability remains limited due to their poor stability in the body and need for careful regulation of their local concentration to avoid unwanted side-effects. In this study, a strategy to overcome these limitations is explored using engineered living materials (ELMs), which contain live microorganisms that can be programmed with stimuli-responsive functionalities. Specifically, the development of an ELM that releases a pro-angiogenic protein in a light-regulated manner is described. This is achieved by optogenetically engineering bacteria to synthesize and secrete a vascular endothelial growth factor peptidomimetic (QK) linked to a collagen-binding domain. The bacteria are securely encapsulated in bilayer hydrogel constructs that support bacterial functionality but prevent their escape from the ELM. In situ control over the release profiles of the pro-angiogenic protein using light is demonstrated. Finally, it is shown that the released protein is able to bind collagen and promote angiogenic network formation among vascular endothelial cells, indicating the regenerative potential of these ELMs.     

Thermal Conductivity and Strength Properties of Nanosilica and GGBS Incorporated Concrete Specimens

Silicon - An important property which affects the heat transfer process in buildings and to minimize the usage of artificial energy in buildings is thermal conductivity. The transfer of heat...     

Metaheuristic Based Clustering with Deep Learning Model for Big Data Classification

Recently, a massive quantity of data is being produced from a distinct number of sources and the size of the daily created on the Internet has crossed two Exabytes. At the same time, clustering is one of the efficient techniques for mining big data to extract the useful and hidden patterns that exist in it. Density-based clustering techniques have gained significant attention owing to the fact that it helps to effectively recognize complex patterns in spatial dataset. Big data clustering is a trivial process owing to the increasing quantity of data which can be solved by the use of Map Reduce tool. With this motivation, this paper presents an efficient Map Reduce based hybrid density based clustering and classification algorithm for big data analytics (MR-HDBCC). The proposed MR-HDBCC technique is executed on Map Reduce tool for handling the big data. In addition, the MR-HDBCC technique involves three distinct processes namely pre-processing, clustering, and classification. The proposed model utilizes the Density-Based Spatial Clustering of Applications with Noise (DBSCAN) technique which is capable of detecting random shapes and diverse clusters with noisy data. For improving the performance of the DBSCAN technique, a hybrid model using cockroach swarm optimization (CSO) algorithm is developed for the exploration of the search space and determine the optimal parameters for density based clustering. Finally, bidirectional gated recurrent neural network (BGRNN) is employed for the classification of big data. The experimental validation of the proposed MR-HDBCC technique takes place using the benchmark dataset and the simulation outcomes demonstrate the promising performance of the proposed model interms of different measures.