Atomistic insight into ideal strength,fracture toughness,deformation, and failure mechanisms of magnetocaloric Fe2AlB2

The thermal and magnetic cycling of a magnetocaloric material degrades its mechanical properties and device performance. We used ab initio tensile and shear simulations to investigate the mechanical properties such as ideal strength, fracture toughness and deformation and failure mechanisms of Fe₂AlB₂ at finite strain. The weakest direction of Fe₂AlB₂ is [010], and the weakest slip system is (010)[100]. The ideal tensile strength (σ_m = 12.51 GPa) of Fe₂AlB₂ is less than its ideal shear strength (τ_m = 13.32 GPa). The strain energy difference (ΔE = −13 eV/f.u.) of Fe₂AlB₂ confirms cleavage fracture as its most plausible failure mode. The concomitant changes in the c-lattice parameter and Al–Al bond along the c-axis determine the ideal tensile strength of Fe₂AlB₂. Likewise, the subtle changes in the a-lattice parameter and Al–Al bond along the a-axis specify its ideal shear strength. The tensile strain induces a magnetic to nonmagnetic transition in Fe₂AlB₂ at the critical tensile strain (ε_c = 0.08). A similar transition occurs at the critical fracture strain (ε_cf = 0.48) due to shear deformation. The brittle nature of Fe₂AlB₂ is predicted by its anisotropic Poisson's ratios, strength ratio, and failure mode. The fracture toughness of Fe₂AlB₂ for mode I fracture is (K_Ic = 2.17 MPa m^1/2), mode II fracture is (K_IIc = 1.33 MPa m^1/2), and mode III fracture is (K_IIIc = 1.16 MPa m^1/2). The failure mechanism of Fe₂AlB₂ due to the tensile deformation is marked by the sharp and appreciable changes in the lattice parameters, bonding characteristics, and magnetic moment of Fe at the critical fracture strain (ε_cf = 0.44). This study provides a fundamental understanding of the mechanical behavior of Fe₂AlB₂ at the finite strain relevant to the cycling stability of the magnetocaloric Fe₂AlB₂.     

One Pot Synthesis and Properties of Cationic Surfactants: <Emphasis Type="Italic">n</Emphasis>-Alkyl-3-Methylpyridinium Bromide

Three new amphiphilic compounds i.e., n-decyl-3-methylpyridinium bromide (a), n-dodecyl-3-methylpyridinium bromide (b), and n-tetradecyl-3-methylpyridinium bromide (c), have been synthesized by condensation reaction and characterized by NMR (¹H, ¹³C) and FTIR spectroscopic techniques. The micellization behavior of the compounds has been studied in ethanol employing conductometry and UV/visible spectroscopy. The critical micellization concentration (CMC) values for compound a, b and c was found to be 0.31, 0.29 and 0.27 m mol L^?1, respectively. Effect of temperature on the CMC was checked in the range of 298-318 K. The thermodynamic parameters such as ΔG, ΔH and ΔS of the micellization process of these surfactants were computed. The negative values of ΔG and positive values of ΔH indicated the spontaneous and endothermic nature of the micellization process. Antimicrobial activities of these amphiphiles showed significant activity against different bacterial strains.     

Simultaneous production of hydrogen and carbon nanostructures by decomposition of propane and cyclohexane over alumina supported binary catalysts

Nano-scale, binary, 4.5 wt.% Fe–0.5 wt.% M (M = Pd, Mo or Ni) catalysts supported on alumina have been shown to be very effective for the decomposition of lower alkanes to produce hydrogen and carbon nanofibers or nanotubes. After pre-reduction at 700 °C, all three binary catalysts exhibited significantly lower propane decomposition temperatures and longer time-on-stream performances than either the non-metallic alumina support or 5 wt.% Fe/Al₂O₃. Catalytic decomposition of propane using all three catalysts yielded only hydrogen, methane, unreacted propane, and carbon nanotubes. Above 475 °C, hydrogen and methane were the only gaseous products. Catalytic decomposition of cyclohexane using the (4.5 wt.% Fe–0.5 wt.% Pd)/Al₂O₃ catalyst produced primarily hydrogen, benzene, and unreacted cyclohexane below 450 °C, but only hydrogen, methane, and carbon nanotubes above 500 °C. The carbon nanotubes exhibited two distinct forms depending on the reaction temperature. Above 600 °C, they were predominantly in form of multi-walled nanotubes with parallel walls in the form of concentric graphene sheets. At or below 500 °C, carbon nanofibers with capped and truncated stacked-cone structure were produced. At 625 °C, decomposition of cyclohexane produced a mixture of the two types of carbon nanostructures.     

Interconnect characterization of X architecture diagonal lines for VLSI design

This paper addresses the manufacturability, yield, and reliability aspects of X Architecture interconnects (diagonal lines) in a very large scale integrated (VLSI) design that enables integrated circuit (IC) chips to become faster and smaller (area) compared to the same design in Manhattan routing. Test chips that consist of comb/serpentine, maze, via chain, as well as resistance and capacitance structures are designed and fabricated using both 130- and 90-nm copper processes. A new technique to characterize interconnect physical parameters (top and bottom line widths, metal line, and dielectric thickness) is developed that requires capacitance measurement on sets of special test structures. An excellent agreement is found between the extracted process parameters, for both diagonal and Manhattan lines, using this technique and those of SEM/FIB data. Measurements of the line resistance, capacitance, and SEM/FIB data on different types of test structures show that 1:1 design rule ratio (Manhattan versus X Architecture) is manufacturable, and the uniformity and fidelity of the diagonal lines are as good as Manhattan lines. The current generation of mask, lithography, wafer processing techniques are applicable to X Architecture designs.     

Metastable polymorph of etoposide with higher dissolution rate

Etoposide, an anticancer drug, has low oral bioavailability because of low aqueous solubility, slow dissolution rate, and instability in acidic pH. Our objective was to enhance the aqueous solubility and dissolution rate of etoposide by polymorph formation. Preparation of various polymorphs of etoposide was attempted by crystallizing etoposide from organic solvents. Physicochemical properties of the crystals, namely, crystal habit, thermal behavior with hot-stage microscopy, thermal analysis by differential scanning calorimetry, IR spectrum, and solubility and dissolution rates, were examined. Based on the physicochemical characteristics, a metastable polymorph of etoposide was identified when it was crystallized from isopropanol. The metastable polymorph had an equilibrium solubility and intrinsic dissolution rate of 221 μg/ml and 16.3 μg/min/cm², respectively; 1.9 and 1.7 times that of etoposide powder at 25°C, respectively.     

Liver-Tumor Detection Using CNN ResUNet

Liver tumor is the fifth most occurring type of tumor in men and the ninth most occurring type of tumor in women according to recent reports of Global cancer statistics 2018. There are several imaging tests like Computed Tomography (CT), Magnetic Resonance Imaging (MRI), and ultrasound that can diagnose the liver tumor after taking the sample from the tissue of the liver. These tests are costly and time-consuming. This paper proposed that image processing through deep learning Convolutional Neural Network (CNNs) ResUNet model that can be helpful for the early diagnose of tumor instead of conventional methods. The existing studies have mainly used the two Cascaded CNNs for liver segmentation and evaluation of Region Of Interest (ROI). This study uses ResUNet, an updated version of U-Net and ResNet Models that utilize the service of Residential blocks. We apply over method on the 3D-IRCADb01 dataset that is based on CT slices of liver tumor affected patients. The results showed the True Value Accuracy around 99% and F1 score performance around 95%. This method will be helpful for early and accurate diagnose of the Liver tumor to save the lives of many patients in the field of Biotechnology.     

Hep-Pred: Hepatitis C Staging Prediction Using Fine Gaussian SVM

Hepatitis C is a contagious blood-borne infection, and it is mostly asymptomatic during the initial stages. Therefore, it is difficult to diagnose and treat patients in the early stages of infection. The disease’s progression to its last stages makes diagnosis and treatment more difficult. In this study, an AI system based on machine learning algorithms is presented to help healthcare professionals with an early diagnosis of hepatitis C. The dataset used for our Hep-Pred model is based on a literature study, and includes the records of 1385 patients infected with the hepatitis C virus. Patients in this dataset received treatment dosages for the hepatitis C virus for about 18 months. A former study divided the disease into four main stages. These stages have proven helpful for doctors to analyze the liver’s condition. The traditional way to check the staging is the biopsy, which is a painful and time-consuming process. This article aims to provide an effective and efficient approach to predict hepatitis C staging. For this purpose, the proposed technique uses a fine Gaussian SVM learning algorithm, providing 97.9% accurate results.     

Built-In Active Microneedle Patch with Enhanced Autonomous Drug Delivery

The use of microneedles has facilitated the painless localized delivery of drugs across the skin. However, their efficacy has been limited by slow diffusion of molecules and often requires external triggers. Herein, an autonomous and degradable, active microneedle delivery platform is introduced, employing magnesium microparticles loaded within the microneedle patch, as the built-in engine for deeper and faster intradermal payload delivery. The magnesium particles react with the interstitial fluid, leading to an explosive-like rapid production of H₂ bubbles, providing the necessary force to breach dermal barriers and enhance payload delivery. The release kinetics of active microneedles is evaluated in vitro by measuring the amount of IgG antibody (as a model drug) that passed through phantom tissue and a pigskin barrier. In vivo experiments using a B16F10 mouse melanoma model demonstrate that the active delivery of anti-CTLA-4 (a checkpoint inhibitor drug) results in greatly enhanced immune response and significantly longer survival. Moreover, spatially resolved zones of active and passive microneedles allow a combinatorial rapid burst response along with slow, sustained release, respectively. Such versatile and effective autonomous dynamic microneedle delivery technology offers considerable promise for a wide range of therapeutic applications, toward a greatly enhanced outcome, convenience, and cost.