Identification and Characterization of Malate Dehydrogenases in Tomato (Solanum lycopersicum L.)

Malate dehydrogenase, which facilitates the reversible conversion of malate to oxaloacetate, is essential for energy balance, plant growth, and cold and salt tolerance. However, the genome-wide study of the MDH family has not yet been carried out in tomato (Solanum lycopersicum L.). In this study, 12 MDH genes were identified from the S. lycopersicum genome and renamed according to their chromosomal location. The tomato MDH genes were split into five groups based on phylogenetic analysis and the genes that clustered together showed similar lengths, and structures, and conserved motifs in the encoded proteins. From the 12 tomato MDH genes on the chromosomes, three pairs of segmental duplication events involving four genes were found. Each pair of genes had a Ka/Ks ratio < 1, indicating that the MDH gene family of tomato was purified during evolution. Gene expression analysis exhibited that tomato MDHs were differentially expressed in different tissues, at various stages of fruit development, and differentially regulated in response to abiotic stresses. Molecular docking of four highly expressed MDHs revealed their substrate and co-factor specificity in the reversible conversion process of malate to oxaloacetate. Further, co-localization of tomato MDH genes with quantitative trait loci (QTL) of salt stress-related phenotypes revealed their broader functions in salt stress tolerance. This study lays the foundation for functional analysis of MDH genes and genetic improvement in tomato.     

Influence of Sintering Temperatures on the Phase Structure and Magnetic Properties of Spark Plasma Sintered SmCo5 Magnets

Journal of Superconductivity and Novel Magnetism - SmCo5 magnets were prepared by spark plasma sintering (SPS) technique. The phase constitutions, magnetic properties, and microstructures of...     

A Machine Learning-Assisted Approach to a Rapid and Reliable Screening for Mechanically Stable Perovskite-Based Materials

The present work is designed to discover new perovskite-based materials, which are expected to show high mechanical stability during their applications, using machine learning (ML) techniques, and based on the Pugh's criterion for distinguishing brittle and ductile behaviors. For this purpose, ML models to predict the moduli of materials, bulk (B) and shear (G), are built using their crystal structure and composition information. The ML process is initiated with the information of 5663 compounds, including composition, crystal structure and moduli, as listed in AFLOW database. Following a procedure of data characteristics, feature generation, feature processing, training, and testing, the ML models are constructed with acceptable accuracy (tenfold cross-validation R² score of 0.90 and 0.89 for B and G, respectively). The validation process of the models, which is conducted using the corresponding density functional theory calculations, reveals that these models are reliable to be employed in a large-scale screening process. Indeed, the B- and G-based ML models are incorporated in a screening process, and this is also conjugated with other screening criterions, to find out thermodynamically stable and formable perovskite-based materials with improved mechanical performance.     

Designing Pair of Nonlinear Components of a Block Cipher over Gaussian Integers

In block ciphers, the nonlinear components, also known as substitution boxes (S-boxes), are used with the purpose of inducing confusion in cryptosystems. For the last decade, most of the work on designing S-boxes over the points of elliptic curves has been published. The main purpose of these studies is to hide data and improve the security levels of crypto algorithms. In this work, we design pair of nonlinear components of a block cipher over the residue class of Gaussian integers (GI). The fascinating features of this structure provide S-boxes pair at a time by fixing three parameters. But the prime field dependent on the Elliptic curve (EC) provides one S-box at a time by fixing three parameters and . The newly designed pair of S-boxes are assessed by various tests like nonlinearity, bit independence criterion, strict avalanche criterion, linear approximation probability, and differential approximation probability.     

Engineered Cobalt Single-Atoms@BiFeO3 Heteronanostructures for Highly Efficient Solar Water Oxidation

Efficient charge–carrier separation and their utilization are the key factors in overcoming sluggish four-electron reaction kinetics involved in photocatalytic oxygen evolution. Here, a novel study demonstrates the significance of Na₂S₂O₈ as a sacrificial agent in comparison to AgNO₃. Resultantly, BiFeO₃ (BFO) and titanium doped-oxygen deficient BiFeO₃ (Ti-BFO-R) nanostructures achieve ≈64 and 44.5 times higher O₂ evolution in the presence of Na₂S₂O₈ compared to AgNO₃ as a sacrificial agent, respectively. Furthermore, the presence of Co single atoms (Co-SAs) deposited via immersion method on BFO and Ti-BFO-R nanostructures led to achieving outstanding O₂ evolution at a rate of 16.11 and 23.89 mmol g⁻¹ h⁻¹, respectively, which is 153 and 227.5 times higher compared to BFO (in the presence of AgNO₃), the highest O₂ evolution observed for BFO-based materials to date. The successful deposition of Co-SAs is confirmed by aberration-corrected high-angle annular dark-field scanning transmission electron microscopy (AC HAADF-STEM) and X-ray absorption near-edge structure (XANES). The charge transfer investigations confirm the significance of Co-SAs on BFO-based photocatalysts for improved charge–carrier separation, transport, and utilization. This novel study validates the excellent role of Na₂S₂O₈ as a sacrificial agent and Co-SAs as a cocatalyst for BFO-based nanostructures for efficient O₂ evolution.     

Exploring the Bioactive Sites of New Sulfonamide Metal Chelates for Multi-Drug Resistance: An Experimental Versus Theoretical Design

Journal of Inorganic and Organometallic Polymers and Materials - During this report, the synthesis, characterization, medicinal, and molecular modeling of newly synthesized, bidentate sulfonamide...     

Design of XS2 (X = W or Mo)-Decorated VS2 Hybrid Nano-Architectures with Abundant Active Edge Sites for High-Rate Asymmetric Supercapacitors and Hydrogen Evolution Reactions

Two-dimensional layered transition metal dichalcogenides have emerged as promising materials for supercapacitors and hydrogen evolution reaction (HER) applications. Herein, the molybdenum sulfide (MoS₂)@vanadium sulfide (VS₂) and tungsten sulfide (WS₂)@VS₂ hybrid nano-architectures prepared via a facile one-step hydrothermal approach is reported. Hierarchical hybrids lead to rich exposed active edge sites, tuned porous nanopetals-decorated morphologies, and high intrinsic activity owing to the strong interfacial interaction between the two materials. Fabricated supercapacitors using MoS₂@VS₂ and WS₂@VS₂ electrodes exhibit high specific capacitances of 513 and 615 F g⁻¹, respectively, at an applied current of 2.5 A g⁻¹ by the three-electrode configuration. The asymmetric device fabricated using WS₂@VS₂ electrode exhibits a high specific capacitance of 222 F g⁻¹ at an applied current of 2.5 A g⁻¹ with the specific energy of 52 Wh kg⁻¹ at a specific power of 1 kW kg⁻¹. For HER, the WS₂@VS₂ catalyst shows noble characteristics with an overpotential of 56 mV to yield 10 mA cm⁻², a Tafel slope of 39 mV dec⁻¹, and an exchange current density of 1.73 mA cm⁻². In addition, density functional theory calculations are used to evaluate the durable heterostructure formation and adsorption of hydrogen atom on the various accessible sites of MoS₂@VS₂ and WS₂@VS₂ heterostructures.     

An Intriguing Array of Extrudate Patterns in Long-Chain Branched Polymers During Extrusion

The present study highlights a range of surface and volume extrudate patterns that can be detected during the extrusion flow of long-chain branched polymers. Thus, four linear low-density polyethylenes (LDPEs) have been extruded using a single-screw extruder coupled to an inline optical imaging system. The selected LDPEs are selected to outline the influence of molecular weight and long-chain branching on the types of melt flow extrusion instabilities (MFEI). Through the inline imaging system, space–time diagrams are constructed and analyzed via Fourier-transformation using a custom moving window procedure. Based on the number of characteristic frequencies, peak broadness, and whether they are surface or volume distortions, three main MFEI types, distinct from those typically observed in linear and short-chain branched polymers, are identified. The higher molecular weight, low long-chain branching LDPEs exhibited all three instability types, including a special type volume instability. Independently of the molecular weight, higher long-chain branching appeared to have a stabilizing effect on the transition sequences by suppressing volume extrudate distortions or limiting surface patters to a form of weak intensity type.     

Pore-to-mesoscale network modeling of heat transfer and fluid flow in packed beds with application to process design

A dual-network model (DNM) representing the topological characteristics of both the pore space and solid fraction of a packed bed was developed to study coupled incompressible water flow and heat transport from the pore-scale to mesoscale (μm-cm) with the consideration of temperature-dependent fluid viscosity. The DNM was validated and used to study the temperature and velocity at the pore scale and their effects on fluid flow and heat transfer. Then the pore volume of the DNM was varied to illustrate the effect of bed porosity on transport processes, quantifying the trade-off between flow conditions and heat transfer. This work demonstrates the ability of the DNM to simulate pore-scale fluid flow and heat transfer simultaneously, which can then be averaged over the entire simulation domain to approximate meso/macroscopic parameters efficiently in relation to the pore geometry.