Outlining the features of nanoparticle diameter and solid–liquid interfacial layer and Hall current effect on a nanofluid flow configured by a slippery bent surface

A study was executed to explore the flow characteristics of an electrically conducted, magnetized nanofluid across a curved stretching surface. This flow is accomplished in water-based nanofluid escorting Aluminum oxide ${(\text{Al}}_2{\mathrm{O}}_3)$ nanoparticles. The novelty of the work is primarily to capture the consequence of solid–liquid interfacial layer and diameter of Aluminum oxide ${(\text{Al}}_2{\mathrm{O}}_3)$ nanoparticles on flow mechanism. Second, the significance of Hall-current, nonlinear thermal radiation, and velocity slip of second order are engaged in flow and also assume that heat transmission of the flow is attained through convection. The foremost mathematical equations are reformed into dimensionless nonlinear ordinary differential equations through similarity performance. Then, the subsequent equations are resolved by engaging RK-4 based shooting procedure. Inspiration of stimulating flow elements is achieved accurately through various graphs and tables. Some streamline plots and surface plots are provided to enrich the results section. Temperature circulation enhances for curvature factor and thermal biot number whereas the opposite consequences are perceived when the diameter of the nano-sized particles enhances. Here, numerical consequences confirm that the utmost rate of heat transport is enriched by 82.79% and 78.28%, respectively, in appearance and nonappearance of radiative heat flux in the flow.     

Manipulating Spin Exchange Interactions and Spin-Selected Electron Transfers of 2D Metal Phosphorus Trisulfide Crystals for Efficient Oxygen Evolution Reaction

Because oxygen molecules in the ground state favor a triplet spin configuration, spin-polarized electrons at electrocatalysts may promote the generation of parallel spin-aligned oxygen atoms, enhancing oxygen evolution reaction (OER) kinetics. In this study, a significant enhancement of OER performance is demonstrated by controlling the spin-exchange interaction and spin-selected electron transfer of 2D Co_xFe_1−xPS₃ (x = 0–0.45) van der Waals (vdW) single crystals through Co doping. The pristine FePS₃ exhibits antiferromagnetic orbital ordering, while the Co-doped FePS₃ exhibits the emergence of interatomic ferromagnetism due to doping-mediated magnetic exchange interactions. The coupling between Fe and Co ions in the Co-doped FePS₃ crystal allows the formation of efficient spin-selective electron transfer channels compared to the pristine FePS₃. The correlation of spin-exchange interactions and spin-selected electron transfers of 2D Co-doped FePS₃ crystals with a superior OER performance is further revealed by superconducting quantum interference device magnetometer, in situ X-ray absorption near edge spectra and density functional theory simulations. The result suggests that manipulating the spin-exchange interactions of 2D vdW crystals to enhance the spin-selected electron transfer efficiencies through doping is an effective strategy to boost their OER catalytic performances.     

Residual UNet with Dual Attention—An ensemble residual UNet with dual attention for multi-modal and multi-class brain MRI segmentation

In this paper, we have proposed a variant of UNet for brain magnetic resonance imaging (MRI) segmentation. The proposed model, termed as Residual UNet with Dual Attention (RUDA), addresses the two significant challenges of UNet: extraction of the complex features with unclear boundaries and the problem of over-segmentation due to the redundancy caused by the skip connection usage. RUDA is constituted upon the residual blocks for extracting the complex structures. It Introduces attention into the skip connections to avoid redundancy and thereby the chance of over-segmentation. Our model segments brain MRI into white matter (WM), gray matter (GM), and cerebrospinal fluid (CSF) regions, which are considered crucial informative substructures for diagnosing neurological disorders such as Alzheimer's. It has been implemented in an ensemble manner to accommodate the multi-sequence (T1-weighted, IR, and T2-FLAIR) scans. The empirical analysis shows that with an accuracy of 93.80%, RUDA outperforms the two baseline models: UNet (91.37%), ResUNet (91.44%).     

Fabrication and mechanical properties of metakaolin-based geopolymer composites reinforced with auxetic fabrics

This work presents a novel experimental study on the use of auxetic fabrics as the main reinforcement in geopolymer composites, aiming at higher energy dissipation in impact demanding applications. For this, a potassium-based geopolymer was reinforced with an auxetic fabric consisting of basalt fiber fillings positioned between helical auxetic yarns (HAY) made of a thermoplastic polyester core, and a stiffer liquid-crystal polymer wrap, which dispersed the load demands into several single elements having different capabilities. The composites were investigated under quasi-static flexural and tensile loadings, in both longitudinal and transverse directions. The latter showed increased mechanical strengths, up to 26 MPa in tension, and 12.8 MPa in flexural strength. Each fiber portion was tested in tension separately, reaching flexible (core) and stiffer (wrap and basalt) responses, whereas HAYs displayed combined performances due to a suitable auxeticity effect, that is, a negative Poisson's ratio. The pullout investigation justified the cracking and delamination of the composites, due to its cyclic lateral area modification, which created a load demand much higher than what the brittle geopolymer can sustain in this type of solicitation. Thermogravimetric analyses helped to predict the use of such configurations under thermal exposure, pointing out that the geopolymer material could be a suitable thermal barrier to prevent sudden degradation of the fabric under these conditions.     

Physics & Modeling of Ambipolar Snapback Behavior in Gate Grounded NMOS

Silicon - This paper models first snapback ambipolar action in NMOS, when subjected to high current stress across the drain terminal. We analyze 2 − D ambipolar current in Gate Grounded NMOS...     

Stable Graphene Membranes for Selective Ion Transport and Emerging Contaminants Removal in Water

Carbon-based materials, such as graphene oxide and reduced graphene oxide membranes have been recently used to fabricate ultrathin, high-flux, and energy-efficient membranes for ionic and molecular sieving in aqueous solution. However, these membranes appeared rather unstable during long-term operation in water with a tendency to swell over time. Membranes produced from pristine, stable, layered graphene materials may overcome these limitations while providing high-level performance. In this paper, an efficient and “green” strategy is proposed to fabricate µm-thick, graphene-based laminates by liquid phase exfoliation in Cyrene and vacuum filtration on a PVDF support. The membranes appear structurally robust and mechanically stable, even after 90 days of operation in water. In ion transport studies, the membranes show size selection (>3.3 Å) and anion-selectivity via the positively charged nanochannels forming the graphene laminate. In antibiotic (tetracycline) diffusion studies under dynamic conditions, the membrane achieve rejection rates higher than 95%. Sizable antibacterial properties are demonstrated in contact method tests with Staphylococcus aureus and Escherichia coli bacteria. Overall, these “green” graphene-based membranes represent a viable option for future water management applications.     

Wireless Sensor Network-based Detection of Poisonous Gases Using Principal Component Analysis

This work utilizes a statistical approach of Principal Component Analysis (PCA) towards the detection of Methane (CH₄)-Carbon Monoxide (CO) Poisoning occurring in coal mines, forest fires, drainage systems etc. where the CH₄ and CO emissions are very high in closed buildings or confined spaces during oxidation processes. Both methane and carbon monoxide are highly toxic, colorless and odorless gases. Both of the gases have their own toxic levels to be detected. But during their combined presence, the toxicity of the either one goes unidentified may be due to their low levels which may lead to an explosion. By using PCA, the correlation of CO and CH₄ data is carried out and by identifying the areas of high correlation (along the principal component axis) the explosion suppression action can be triggered earlier thus avoiding adverse effects of massive explosions. Wireless Sensor Network is deployed and simulations are carried with heterogeneous sensors (Carbon Monoxide and Methane sensors) in NS-2 Mannasim framework. The rise in the value of CO even when CH₄ is below the toxic level may become hazardous to the people around. Thus our proposed methodology will detect the combined presence of both the gases (CH₄ and CO) and provide an early warning in order to avoid any human losses or toxic effects.     

Fatigue crack propagation of sheet molding compounds in various evironments

The effect of the absorption of water and isooctane on the rate of fatigue crack propagation of sheet molding compounds (SMC-R30 and SMC-R65) was investigated. A crack extension gage was used to measure the crack length. Results show that the absorption of water decreases the rate of fatigue crack propagation in the initial cycles but increases the rate of propagation in the final cycle. The absorption of isooctane into SMC-R65 tends to decrease the rate of fatigue crack propagation. Microscopic observation shows considerable swelling of the polyester matrix due to the absorption of water and no significant apparent effect due to the absorption of isooctane.