Rheological,Thermodynamic, and Gas Solubility Properties of Phenylacetic Acid‐Based Deep Eutectic Solvents

Choline chloride + phenylacetic acid‐based deep eutectic solvents are studied. Their most relevant experimental physicochemical properties at different mixing ratios together with the CO₂ solubility data obtained in wide pressure and temperature ranges are reported. The presented materials exhibit a significant CO₂ capture performance with low corrosion effect when compared with the most common amine‐based CO₂ capture agents. Detailed rheological measurements are carried out and various models are applied to describe the dynamic flow behavior of the solvents. The CO₂ absorption mechanism is evaluated by studying the behavior of the liquid gas and interface. Due to the advantages of low cost, nontoxicity, and favorable physical properties, these solvents are an environmentally promising alternative for effective CO₂ capture technological applications.     

Unsteady nanofluid flow between two spinning expanding disks with continuous vertical motion under the influence of modified Hall effect

The principle aim of this article is to detect the effects of externally applied magnetic force in the nanofluid flow, flowing between two co-axially rotating and expanding disks where the upper disk is continuously moving vertically upward and downward. Also, the modified Hall Effect has been considered as an effective factor of the flow. The lower disk is vertically static. The rotation and vertical motion of the disks create a three-dimensional flow of nanofluid. Heat and mass density along with the motion of the flow has been analyzed under the variation in magnetic and Hall parameters. The findings have been compared with the results in Von Karman flow of nanofluid between two rotating and stretching disks. The velocity components have been largely influenced by magnetic and Hall parameters in case of downward movement of the upper disk. The fluid temperature is detected higher in case of upward velocity and lower in case of the downward motion of the upper disk. The heat transferability of the disks is effected differently at two different disks with the influence of magnetic force and Hall effect.     

Hierarchically Porous Multimetal‐Based Carbon Nanorod Hybrid as an Efficient Oxygen Catalyst for Rechargeable Zinc–Air Batteries

The lack of efficient strategies to address the intrinsic activity, site accessibility, and structural stability issues of metal‐carbon hybrid catalysts is restricting their real‐world implementation on the basis of rechargeable zinc–air batteries. Herein, a dual metal–organic frameworks (MOFs) pyrolysis strategy is developed to regulate the intrinsic activity and porous structure of the derived catalysts, where a Fe₂Ni_MIL‐88@ZnCo_zeolitic imidazolate framework (ZIF), with a hierarchically porous structure, multifunctional components, and an integrated architecture, acts as an ideal precursor to obtain multimetal based porous nanorod (FeNiCo@NC‐P). Benefitting from the synergetic effect of the multimetal components, facilitated reactant accessibility, and the well‐retained integrated structure, the resultant FeNiCo@NC‐P catalyst exhibits an oxygen reduction reaction half‐wave potential of 0.84 V as well as an oxygen evolution reaction potential of 1.54 V at 10 mA cm^–2. Furthermore, the practical application of FeNiCo@NC‐P in the zinc–air battery displays a low voltage gap and long‐term durability (over 130 h at a current density of 10 mA cm^–2), which outperforms the commercial noble metal benchmarks. This work not only affords a competitive bifunctional oxygen electrocatalyst for zinc–air batteries but also paves a new way to design and fabricate MOF‐derived materials with tunable catalytic properties.     

Heat transfer and fluid flow characteristics in multistaged Tesla valves

The use of Tesla valves for flow control and rectification in mini and microfluidic applications is appealing due to their passive operation and no-moving-parts design. The effectiveness of such valves can be increased through their in-series arrangement, i.e., a multistaged Tesla valve (MSTV). In this study, the effect of inlet Reynolds number (25–200) on the flow rectification and thermal enhancement capabilities of a single Tesla valve and MSTV (up to 10 stages) is numerically investigated through 3D computational fluid dynamics. Based on the simulation results, power-law correlations for MSTV design and performance in terms of Nusselt number, Darcy friction factor, pressure diodicity, and thermal diodicity are derived and provided. The results demonstrate that heat transfer enhancement during reverse flow through the Tesla valve structure is attributable to flow bifurcation, stagnation, and mixing mechanisms; average Nusselt numbers as high as 7.1 were observed for Re?=?200. The ability of a Tesla valve to function as a mini-to-micro-type heat exchanger, thermal diode, and/or check valve can benefit several thermal/flow control applications.     

BrC-MCDLM: breast Cancer detection using Multi-Channel deep learning model

Multimedia Tools and Applications - Breast cancer (BrC) is a lethal form of cancer which causes numerous deaths in women across the world. Generally, mammograms and histopathology biopsy images are...     

Viscosities of l-Histidine/l-Glutamic Acid/l-Tryptophan/Glycylglycine?+?2 M Aqueous KCl/KNO3 Solutions at T?=?(298.15 to 323.15)?K

Viscosity values of l-histidine/l-glutamic acid/l-tryptophan/glycylglycine + 2 M aqueous KCl/KNO₃ solutions have been determined experimentally as a function of molal concentration of amino acid/peptide at different temperatures: (298.15, 303.15, 308.15, 313.15, 318.15, and 323.15) K. Using the viscosity values of the solvent and solution, the relative viscosity, specific viscosity, and viscosity B-coefficient values have been computed. The trends of the variation of experimental and computed parameters with the solute concentration and temperature have been interpreted in terms of zwitterions–ions, zwitterions–water dipoles, ions–water dipoles, and ions–ions interactions operative in the systems.     

Thermal Modeling of Hybrid Storage Clusters

There is a lack of thermal models for storage clusters; most existing thermal models do not take into account the utilization of hard drives (HDDs) and solid state disks (SSDs). To address this problem, we build a thermal model for hybrid storage clusters that are comprised of HDDs and SSDs. We start this study by generating the thermal profiles of hard drives and solid state disks. The profiling results show that both HDDs and SSDs have profound impacts on temperatures of storage nodes in a cluster. Next, we build two types of hybrid storage clusters, namely, inter-node and intra-node hybrid storage clusters. We develop a model to estimate the cooling cost of a storage cluster equipped with hybrid storage nodes. The thermal model is validated against data acquired by temperature sensors. Experimental results show that, compared to the HDD-first strategy, the SSD-first strategy is an efficient approach to minimize negative thermal impacts of hybrid storage clusters.     

A multi-class skin Cancer classification using deep convolutional neural networks

Skin Cancer accounts for one-third of all diagnosed cancers worldwide. The prevalence of skin cancers have been rising over the past decades. In recent years, use of dermoscopy has enhanced the diagnostic capability of skin cancer. The accurate diagnosis of skin cancer is challenging for dermatologists as multiple skin cancer types may appear similar in appearance. The dermatologists have an average accuracy of 62% to 80% in skin cancer diagnosis. The research community has been made significant progress in developing automated tools to assist dermatologists in decision making. In this work, we propose an automated computer-aided diagnosis system for multi-class skin (MCS) cancer classification with an exceptionally high accuracy. The proposed method outperformed both expert dermatologists and contemporary deep learning methods for MCS cancer classification. We performed fine-tuning over seven classes of HAM10000 dataset and conducted a comparative study to analyse the performance of five pre-trained convolutional neural networks (CNNs) and four ensemble models. The maximum accuracy of 93.20% for individual model amongst the set of models whereas maximum accuracy of 92.83% for ensemble model is reported in this paper. We propose use of ResNeXt101 for the MCS cancer classification owing to its optimized architecture and ability to gain higher accuracy.

     

Intracellular Ionic Strength Sensing Using NanoLuc

Intracellular ionic strength regulates myriad cellular processes that are fundamental to cellular survival and proliferation, including protein activity, aggregation, phase separation, and cell volume. It could be altered by changes in the activity of cellular signaling pathways, such as those that impact the activity of membrane-localized ion channels or by alterations in the microenvironmental osmolarity. Therefore, there is a demand for the development of sensitive tools for real-time monitoring of intracellular ionic strength. Here, we developed a bioluminescence-based intracellular ionic strength sensing strategy using the Nano Luciferase (NanoLuc) protein that has gained tremendous utility due to its high, long-lived bioluminescence output and thermal stability. Biochemical experiments using a recombinantly purified protein showed that NanoLuc bioluminescence is dependent on the ionic strength of the reaction buffer for a wide range of ionic strength conditions. Importantly, the decrease in the NanoLuc activity observed at higher ionic strengths could be reversed by decreasing the ionic strength of the reaction, thus making it suitable for sensing intracellular ionic strength alterations. Finally, we used an mNeonGreen–NanoLuc fusion protein to successfully monitor ionic strength alterations in a ratiometric manner through independent fluorescence and bioluminescence measurements in cell lysates and live cells. We envisage that the biosensing strategy developed here for detecting alterations in intracellular ionic strength will be applicable in a wide range of experiments, including high throughput cellular signaling, ion channel functional genomics, and drug discovery.     

Inclusion of recycled PPTA fibre in development of cut-resistant gloves

PPTA is a high-performance fibre with premium mechanical and heat-resistant properties. This study focused on developing cut-resistant gloves from recycled PPTA fibre. The cut resistance of knitted glove, from virgin and recycled PPTA fibre yarn with/without steel core, was evaluated to determine the performance of the glove. The fibre and yarn parameters were studied to understand the underlying factors which aided in premium cut-resistant properties of gloves manufactured from recycled fibres – higher than that of gloves manufactured from virgin PPTA fibres.