Structure, indentation and corrosion characterizations of high-silicon Ni-Si nano-composite coatings prepared by modified electrodeposition process

Ni-Si nano-composite coatings with various silicon contents were prepared by a modified electrodeposition process using electrolytes containing ball-milled Si/Ni particles. The effects of the concentration of the ball-milled Si/Ni particles in the electrolyte on the silicon content, structure, microhardness and corrosion behaviors of the coatings were investigated. Scanning electron microscopy and X-ray diffractometry were used for structural characterization. Also, the microhardness and corrosion behaviors of the deposited coatings were evaluated. According to the results, the Si level reaches about 10 wt.% in the coating, which is a significant content of Si incorporation for electrodeposition. It was also found that the crystallite size of the coatings was progressively decreased and the hardness was increased, by increasing the content of Si. Typically, the crystallite size and microhardness of the Ni-10 wt.%Si coating were 0.39 and 2.1 times those of the pure Ni coating, respectively. Also, the results showed that there is an optimal content of Si to meet the best acidic corrosion resistance of the coatings.

Strength prediction of multi-layered copper-based composites fabricated by accumulative roll bonding

This work aims to evaluate the feasibility of the fabrication of nanostructured Cu/Al/Ag multi-layered composites by accumulative roll bonding(ARB),and to analyze the tensile properties and electrical conductivity of the produced composites.A theoretical model using strengthening mechanisms and some structural parameters extracted from X-ray diffraction is also developed to predict the tensile strength of the composites.It was found that by progression of ARB,the experimental and calculated tensile strengths are enhanced,reach a maximum of about 450 and 510 MPa at the fifth cycle of ARB,respectively and then are reduced.The electrical conductivity decreased slightly by increasing the number of ARB cycles at initial ARB cycles,but the decrease was intensified at the final ARB cycles.In conclusion,the merit of ARB to fabricate this type of multi-layered nanocomposites and the accuracy of the developed model to predict tensile strength were realized.

Structural and Mechanical Properties of Al/B_4C Composites Fabricated by Wet Attrition Milling and Hot Extrusion

In this study, Al matrix composites reinforced by 7.5 and 15 vol.% B4C particles and also monolithic Al （Al without the B4C particles） were produced by wet attrition milling and subsequent hot forward extrusion processes. The microstructure of the composites, evaluated by scanning electron microscopy （SEM）, showed that the B4C particles were properly distributed in the Al matrix. Mechanical properties of the Al/B4C composites and monolithic Al were investigated by tensile, wear and hardness tests, The results revealed that with increasing content of B4C particles, the tensile strength and microhardness of composites increased but the elongation decreased. In addition, the tensile strength and microhardness of composite samples were higher than those of monolithic Al. The density measurements revealed that the density of composites decreased with increasing content of the B4C particles.

DHT-and blockchain-based smart identification for video conferencing

Video conferencing applications help people communicate via the Internet and provide a significant and consistent basis for virtual meetings.However,integrity,security,identification,and authentication problems are still universal.Current video conference technologies typically rely on cloud systems to provide a stable and secure basis for executing tasks and processes.At the same time,video conferencing applications are being migrated from centralized to decentralized solutions for better performance without the need for third-party interactions.This article demonstrates a decentralized smart identification scheme for video conferencing applications based on biometric technology,machine learning,and a decentralized hash table combined with blockchain technology.We store users'information on a distributed hash table and transactional events on the distributed ledger after identifying users by implementing machine learning functions.Furthermore,we leverage distributed ledger technology's immutability and traceability properties and distributed hash table unlimited storage feature to improve the system's storage capacity and immutability by evaluating three possible architectures.The experimental results show that an architecture based on blockchain and distributed hash table has better efficiency but needs a longer time to execute than the two other architectures using a centralized database.