Biologically Sensitive FETs: Holistic Design Considerations from Simulation,Modeling and Fabrication Perspectives

Silicon - The conceptualization of biomolecule sensing accomplished by field effect transistor (FET) devices have been attracting substantial contemplation for over twenty years owing to the...     

Environmental stability of additively manufactured siliconized silicon carbide for applications in hybrid energy systems

A key consideration for the successful operation of hybrid energy systems will be the environmental stability of materials used for their construction, particularly when experiencing service environments containing water vapor at high temperatures. Here, we report results from the characterization of siliconized silicon carbide (Si-SiC) prepared via binder jet additive manufacturing and reactive silicon melt infiltration after being exposed to environments representative of those in solid oxide fuel cell (SOFC) anodes, and to exhaust gases inside a microturbine operating on natural gas. In both cases, it was found that oxide scales formed on the surface and that these scales were dense, continuous, and well-bonded to the substrates, although there was evidence of transverse and longitudinal cracking most likely as a result of mismatches in the thermal expansion of the scale and the substrate. Measured values of the thickness of the oxide scale were compared to those predicted by parabolic oxidation kinetics of silicon, but the potential effects of silica volatilization induced by water vapor, and silica reduction when exposed to hydrogen are discussed. The overall results showed that the oxide scale is expected to be protective under the conditions of hybrid power generation systems.     

Deep Learning ResNet101 Deep Features of Portable Chest X-Ray Accurately Classify COVID-19 Lung Infection

This study is designed to develop Artificial Intelligence (AI) based analysis tool that could accurately detect COVID-19 lung infections based on portable chest x-rays (CXRs). The frontline physicians and radiologists suffer from grand challenges for COVID-19 pandemic due to the suboptimal image quality and the large volume of CXRs. In this study, AI-based analysis tools were developed that can precisely classify COVID-19 lung infection. Publicly available datasets of COVID-19 (N = 1525), non-COVID-19 normal (N = 1525), viral pneumonia (N = 1342) and bacterial pneumonia (N = 2521) from the Italian Society of Medical and Interventional Radiology (SIRM), Radiopaedia, The Cancer Imaging Archive (TCIA) and Kaggle repositories were taken. A multi-approach utilizing deep learning ResNet101 with and without hyperparameters optimization was employed. Additionally, the features extracted from the average pooling layer of ResNet101 were used as input to machine learning (ML) algorithms, which twice trained the learning algorithms. The ResNet101 with optimized parameters yielded improved performance to default parameters. The extracted features from ResNet101 are fed to the k-nearest neighbor (KNN) and support vector machine (SVM) yielded the highest 3-class classification performance of 99.86% and 99.46%, respectively. The results indicate that the proposed approach can be better utilized for improving the accuracy and diagnostic efficiency of CXRs. The proposed deep learning model has the potential to improve further the efficiency of the healthcare systems for proper diagnosis and prognosis of COVID-19 lung infection.     

Variable thermal conductivity influence on micropolar nanofluid flow triggered by a stretchable disk with Arrhenius activation energy

The analysis of magnetized micro–nanoliquid flows generated by the movable disk is executed in this study. The disk is contained under the porous zone influence. The heat generation, heat sink, and temperature-dependent conductance analysis are reported through the energy equation. The activation energy in terms of a chemical reaction is incorporated through the mass equation. The flow model is normalized through the implementation of similarity transformations. The numerical algorithm Runge–Kutta–Fehlberg is used to solve the reduced system. Results are plotted graphically and in tabular format to investigate the velocity, thermal, and concentration fields. Numeric benchmarks of couple and shear stresses, thermal and concentration rates are also computed. The temperature is augmented against the incremented thermophoretic, variable conductivity, and Brownian movement parameters. The presence of variable conductivity parameter resulted in a weaker rate of heat transportation. The heat transportation rate is boosted with an incremented Prandtl number.     

Understanding the Diffusion-Dominated Properties of MOF-Derived Ni–Co–Se/C on CuO Scaffold Electrode using Experimental and First Principle Study

Batteries and supercapacitors continue to be one of the most researched topics in the class of energy storage devices. The continuous development of battery and supercapacitor cell components has shown promising development throughout the years—from slabs of pure metal to porous and tailored structures of metal-based active materials. In this direction, metal–organic frameworks (MOFs) serve great advantages in improving the properties and structure of the derived metal-based active materials. This research provides a novel electrode material, Ni–Co–Se/C@CuO, derived from Ni–Co-MOF integrated with pre-oxidized Cu mesh. The superior electrochemical performance of Ni–Co–Se/C@CuO over Ni–Co-MOF@CuO is evident through its higher specific capacity, lower resistivity, richer redox activity, and more favorable diffusion-dominated storage mechanism. When assembled as a hybrid supercapacitor (HSC), the hybrid device using rGO and Ni–Co–Se/C@CuO as electrodes exhibits a high energy density of 42 W h kg⁻¹ at a power density of 2 kW kg⁻¹, and maintains its capacity retention even after 20 000 cycles. The improved capacity performance is also evaluated using first-principle investigations, revealing that the unique and preserved heterostructure of Ni–Co–Se/C@CuO portrays enhanced metallic properties. Such evaluation of novel electrodes with superior properties may benefit next-generation electrodes for supercapacitor devices.