Manipulation of thick-walled PEEK bushing crystallinity and modulus via instrumented compression molding

An instrumented hot compression molding apparatus was fabricated to allow real-time monitoring and precise temperature control during the compaction and consolidation of large polyether ether ketone (PEEK) products. The objective was to determine the impact of controlled variables on the properties of the molded article. Four different strategies were designed to control the mold thermal profiles. The average crystallinity in a commercial molding process is restricted due to large thermal masses with low thermal conductivity. In contrast, this research was able to reduce the crystallinity range from 33% to 6% by developing a special controlled apparatus and implementing new processing methodologies. In this study, PEEK showed a significant increase in the modulus compared to typical values measured on commercially produced analogs, and a higher degree of property uniformity. In a single commercially molded PEEK billet, compressive modulus variability was 13% at room temperature, and 21% at 225°C. Properties of billets produced using the laboratory apparatus showed a reduction in variability to 2%.     

Study the Characteristic of Planer and Sandwich PSi Gas Sensor (Comparative Study)

In this work, two configurations (planner and sandwich) structures of (Al/n PSi/n-Si/Al) porous silicon (PSi) gas sensor have been fabricated and tested extensively for CO₂ gas molecules. Two laser wavelength infrared and violet of (810 nm) and (405 nm) was used in the laser assisting etching process on the n-type silicon substrate. The gas detection characteristic of planner and sandwich configuration were studied under different condition. The resistance measurement, for the planner and J-V characteristic for sandwich structures, which was analyzed based on the nano-sized silicon, porosity, layer thickness, and the effective dielectric constant of the PSi layer. The SEM image of the PSi layer showed the formation pore-like structure with cylindrical and rectangular pore shape with different dimensions for infrared illuminated PSi and crossed pores-like structure with randomly distributed for violet illuminated PSi. The sensing mechanism for sandwich structure configuration is governed by the porous silicon parameters, while for planer configuration the silicon channel among the pore has a significant role in sensing process.     

Automatic Detection of COVID-19 Using Chest X-Ray Images and Modified ResNet18-Based Convolution Neural Networks

The latest studies with radiological imaging techniques indicate that X-ray images provide valuable details on the Coronavirus disease 2019 (COVID-19). The usage of sophisticated artificial intelligence technology (AI) and the radiological images can help in diagnosing the disease reliably and addressing the problem of the shortage of trained doctors in remote villages. In this research, the automated diagnosis of Coronavirus disease was performed using a dataset of X-ray images of patients with severe bacterial pneumonia, reported COVID-19 disease, and normal cases. The goal of the study is to analyze the achievements for medical image recognition of state-of-the-art neural networking architectures. Transfer Learning technique has been implemented in this work. Transfer learning is an ambitious task, but it results in impressive outcomes for identifying distinct patterns in tiny datasets of medical images. The findings indicate that deep learning with X-ray imagery could retrieve important biomarkers relevant for COVID-19 disease detection. Since all diagnostic measures show failure levels that pose questions, the scientific profession should determine the probability of integration of X-rays with the clinical treatment, utilizing the results. The proposed model achieved 96.73% accuracy outperforming the ResNet50 and traditional Resnet18 models. Based on our findings, the proposed system can help the specialist doctors in making verdicts for COVID-19 detection.     

The Effect of Heat Treatment on Optical Properties of Copper (II) Phthalocyanine Tetrasulfonic Acid Tetrasodium Salt (CuPcTs) Organic Thin Films

Silicon - Copper (II) phthalocyanine Tetrasulfonic Acid Tetrasodium Salt (CuPcTs) thin films have been prepared using a simple spin coating method on glass substrates. Thin films (100 ± 5 nm...     

Procoagulant Extracellular Vesicles Alter Trophoblast Differentiation in Mice by a Thrombo-Inflammatory Mechanism

Procoagulant extracellular vesicles (EV) and platelet activation have been associated with gestational vascular complications. EV-induced platelet-mediated placental inflammasome activation has been shown to cause preeclampsia-like symptoms in mice. However, the effect of EV-mediated placental thrombo-inflammation on trophoblast differentiation remains unknown. Here, we identify that the EV-induced thrombo-inflammatory pathway modulates trophoblast morphology and differentiation. EVs and platelets reduce syncytiotrophoblast differentiation while increasing giant trophoblast and spongiotrophoblast including the glycogen-rich cells. These effects are platelet-dependent and mediated by the NLRP3 inflammasome. In humans, inflammasome activation was negatively correlated with trophoblast differentiation marker GCM1 and positively correlated with blood pressure. These data identify a crucial role of EV-induced placental thrombo-inflammation on altering trophoblast differentiation and suggest platelet activation or inflammasome activation as a therapeutic target in order to achieve successful placentation.