Dynamic evolution of oxide scale on the surfaces of feed stock particles from cracking and segmenting to peel-off while cold spraying copper powder having a high oxygen content

The oxide scale present on the feedstock particles is critical for inter-particle bond formation in the cold spray(CS)coating process,therefore,oxide scale break-up is a prerequisite for clean metallic contact which greatly improves the quality of inter-particle bonding within the deposited coating.In general,a spray powder which contains a thicker oxide scale on its surface(i.e.,powders having high oxygen content)requires a higher critical particle velocity for coating formation,which also lowers the deposition efficiency(DE)making the whole process a challenging task.In this work,it is reported for the first time that an artificially oxidized copper(Cu)powder containing a high oxygen content of 0.81 wt.%with a thick surface oxide scale of 0.71μm.,can help achieve an astonishing increment in DE.A transition of surficial oxide scale evolution starting with crack initiations followed by segmenting to peeling-off was observed during the high velocity particle impact of the particles,which helps in achieving an astounding increment in DE.Single-particle deposit observations revealed that the thick oxide scale peels off from most of the sprayed powder surfaces during the high-velocity impact,which leaves a clean metallic surface on the deposited particle.This makes the successive particles to bond easily and thus leads to a higher DE.Further,owning to the peeling-off of the oxide scale from the feedstock particles,very few discontinuous oxide scale segments are retained at inter-particle boundaries ensuring a high electrical conductivity within the resulting deposit.Dependency of the oxide scale threshold thickness for peeling-off during the high velocity particle impact was also investigated.     

Dual Defect-Passivation Using Phthalocyanine for Enhanced Efficiency and Stability of Perovskite Solar Cells

Semiconducting molecules have been employed to passivate traps extant in the perovskite film for enhancement of perovskite solar cells (PSCs) efficiency and stability. A molecular design strategy to passivate the defects both on the surface and interior of the CH₃NH₃PbI₃ perovskite layer, using two phthalocyanine (Pc) molecules (NP-SC₆-ZnPc and NP-SC₆-TiOPc) is demonstrated. The presence of lone electron pairs on S, N, and O atoms of the Pc molecular structures provides the opportunity for Lewis acid–base interactions with under-coordinated Pb²⁺ sites, leading to efficient defect passivation of the perovskite layer. The tendency of both NP-SC₆-ZnPc and NP-SC₆-TiOPc to relax on the PbI₂ terminated surface of the perovskite layer is also studied using density functional theory (DFT) calculations. The morphology of the perovskite layer is improved due to employing the Pc passivation strategy, resulting in high-quality thin films with a dense and compact structure and lower surface roughness. Using NP-SC₆-ZnPc and NP-SC₆-TiOPc as passivating agents, it is observed considerably enhanced power conversion efficiencies (PCEs), from 17.67% for the PSCs based on the pristine perovskite film to 19.39% for NP-SC₆-TiOPc passivated devices. Moreover, PSCs fabricated based on the Pc passivation method present a remarkable stability under conditions of high moisture and temperature levels.     

The Effect of Acoustic Field on the Thermal Conductivity of composite propellants

A study is conducted to determine the effect of acoustic field on the thermal conductivity of composite propellants based on poly- butadiene and polyurethane binders. The thermal conductivity of the propellant is determined by a specially designed equipment. The thermal conductivity measurements are made in steady state conditions and in an acoustic field of constant amplitude at various frequencies ranging from 1 kHz to 6 kHz and at different temperatures ranging from 30°C to 50°C. The results indicate that the thermal conductivities of the composite propellants increase when they are subjected to an acoustic field. The data obtained are presented in the paper. Since the burning behaviour of solid propellants is influenced also by its thermal conductivity, the present work may be of direct relevance in improving upon some of the existing burning rate and combustion models.     

Liver Tumors Segmentation Using 3D SegNet Deep Learning Approach

An ultrasonic filter detects signs of malignant tumors by analysing the image’s pixel quality fluctuations caused by a liver ailment. Signs of malignant growth proximity are identified in an ultrasound filter through image pixel quality variations from a liver’s condition. Those changes are more common in alcoholic liver conditions than in other etiologies of cirrhosis, suggesting that the cause may be alcohol instead of liver disease. Existing Two-Dimensional (2D) ultrasound data sets contain an accuracy rate of 85.9% and a 2D Computed Tomography (CT) data set of 91.02%. The most recent work on designing a Three-Dimensional (3D) ultrasound imaging system in or close to real-time is examined. In this article, a Deep Learning (DL) model is implemented and modified to fit liver CT segmentation, and a semantic pixel classification of road scenes is recommended. The architecture is called semantic pixel-wise segmentation and comprises a hierarchical link of encoder-decoder layers. A standard data set was used to test the proposed model for liver CT scans and the tumor accuracy in the training phase. For the normal class, we obtained 100% precision for chronic cirrhosis hepatitis (73%), offset cirrhosis (59.26%), and offensive cirrhosis (91.67%) for chronic hepatitis or cirrhosis (73,0%). The aim is to develop a Computer-Aided Detection (CAD) screening tool to detect steatosis. The results proved 98.33% exactness, 94.59% sensitivity, and 92.11% case with Convolutional Neural Networks (CNN) classification. Although the classifier’s performance did not differentiate so clearly at this level, it was recommended that CNN generally perform better due to the good relationship between Area under the Receiver Operating Characteristics Curve (AUC) and accuracy.