Hybrid Electrolyte Design for High-Performance Zinc–Sulfur Battery

Rechargeable aqueous Zn/S batteries exhibit high capacity and energy density. However, the long-term battery performance is bottlenecked by the sulfur side reactions and serious Zn anode dendritic growth in the aqueous electrolyte medium. This work addresses the problem of sulfur side reactions and zinc dendrite growth simultaneously by developing a unique hybrid aqueous electrolyte using ethylene glycol as a co-solvent. The designed hybrid electrolyte enables the fabricated Zn/S battery to deliver an unprecedented capacity of 1435 mAh g⁻¹ and an excellent energy density of 730 Wh kg⁻¹ at 0.1 Ag⁻¹. In addition, the battery exhibits capacity retention of 70% after 250 cycles even at 3 Ag⁻¹. Moreover, the cathode charge–discharge mechanism studies demonstrate a multi-step conversion reaction. During discharge, the elemental sulfur is sequentially reduced by Zn to S²⁻ (${\mathrm{S}}_8\to {\mathrm{S}}_{\mathrm{x}}^{2-}\to {\mathrm{S}}_2^{2-}+{\mathrm{S}}^{2-})$, forming ZnS. On charging, the ZnS and short-chain polysulfides will oxidize back to elemental sulfur. This electrolyte design strategy and unique multi-step electrochemistry of the Zn/S system provide a new pathway in tackling both key issues of Zn dendritic growth and sulfur side reactions, and also in designing better Zn/S batteries in the future.     

Flow physics of planar bistable fluidic oscillator with backflow limbs

Fluidic oscillators (FOs) are used in a variety of applications, including process control and process intensification. Despite the simple design and operation of FOs, the fluid dynamics of FOs exhibit rich complexities. The inherently unstable flow, jet oscillations, and resulting vortices influence mixing and other transport processes. In this work, we computationally investigated the fluid dynamics of a new design of a planar FO with backflow limbs. The design comprised of two symmetric backflow limbs leading to bistable flow. The unsteady flow dynamics, internal recirculation, jet oscillations, secondary flow vortices were computationally studied over a range of inlet Reynolds numbers (2400–12,000). The nature and frequency of the jet oscillations were quantified. The computed jet oscillation frequency was compared with the experimentally measured (using imaging techniques) jet oscillation frequency. The flow model was then used to quantitatively understand mixing, heat transfer, and residence time distribution. The approach and the results presented in this work will provide a basis for designing FO's with desired flow and transport characteristics for various engineering applications.     

A Study of Added SiC Powder in Kerosene for the Blind Square Hole Machining of CFRP Using Electrical Discharge Machining

Silicon - Carbon Fiber Reinforced Polymers (CFRPs) have been applied potentially for various application components owing to their lightweight and better mechanical properties. However, the...     

A novel approach for prediction of surface roughness in turning of EN353 steel by RVR-PSO using selected features of VMD along with cutting parameters

The abrupt changes in tool-workpiece interaction during machining process induce variation in the surface quality of work material. These interactions include built-up edge formation and their break-off, environmental conditions (use of coolant, rise of temperature etc.), material imperfections, improper structural fitness of machine & tool components, etc. This study presents prediction of surface roughness in turning of EN353 steel implementing the variational mode decomposition (VMD) for processing the vibration data, followed by estimation of the surface roughness using the relevance vector regression (RVR) optimized by particle swarm optimization (PSO). The raw vibration data has been decomposed in five discrete sets of frequency components known as variational mode functions (VMFs). A set of twenty-one statistical features in each three axes have been extracted for raw data and each VMF. The RVR has been trained using these 21×3 = 63 features and 3 cutting parameters — cutting speed, feed depth of cut. The RVR has also been trained separately using top 5 features selected through RreliefF algorithm. The optimal decomposition level has been determined to minimize the noise and predict the surface finish accurately. The results obtained in 1st VMF (high frequency, low amplitude) using its top 5 features for prediction have been found to be reliable with higher prediction accuracy.

     

Comparative study of weld-line strength for unfilled and glass-filled thermoplastic polyamide-6 materials

The injection molding process is widely accepted for the processing of engineering thermoplastics due to the ease of manufacturing complex designs. Weld-line is a defect occurring in injection molded parts when two flow fronts join each other. At weld-line locations, parts exhibit lower mechanical strength mainly due to inadequate intermolecular diffusion and fiber orientation anisotropy. The present work is aimed at investigating and comparing weld-line strength for unfilled and glass-filled polyamide-6 materials. To achieve this, polyamide-6 unfilled, 30% glass-filled, and 50% glass-filled materials are used to manufacture plaques. The special-purpose mold is designed to obtain plaques with and without weld-lines with help of Moldflow simulations. The specimens for tensile tests are then cut from molded plaques and experimental testing is conducted to evaluate tensile properties. Fractured surfaces of specimens are examined using a scanning electron microscope. The results demonstrated a significant drop in tensile strength and modulus for glass-filled material weld-line specimens when compared to specimens of no weld-line. However, for unfilled specimens, tensile strength and modulus are almost the same for samples with and without weld-line. A reduction in tensile strength of 13%, 49%, and 57% is observed for unfilled, 30% glass-filled, and 50% glass-filled polyamide-6 material respectively.     

A deep learning approach for classification of COVID and pneumonia using DenseNet-201

In the present paper, our model consists of deep learning approach: DenseNet201 for detection of COVID and Pneumonia using the Chest X-ray Images. The model is a framework consisting of the modeling software which assists in Health Insurance Portability and Accountability Act Compliance which protects and secures the Protected Health Information . The need of the proposed framework in medical facilities shall give the feedback to the radiologist for detecting COVID and pneumonia though the transfer learning methods. A Graphical User Interface tool allows the technician to upload the chest X-ray Image. The software then uploads chest X-ray radiograph (CXR) to the developed detection model for the detection. Once the radiographs are processed, the radiologist shall receive the Classification of the disease which further aids them to verify the similar CXR Images and draw the conclusion. Our model consists of the dataset from Kaggle and if we observe the results, we get an accuracy of 99.1%, sensitivity of 98.5%, and specificity of 98.95%. The proposed Bio-Medical Innovation is a user-ready framework which assists the medical providers in providing the patients with the best-suited medication regimen by looking into the previous CXR Images and confirming the results. There is a motivation to design more such applications for Medical Image Analysis in the future to serve the community and improve the patient care.