Efficient Dye Degradation and Antimicrobial Behavior with Molecular Docking Performance of Silver and Polyvinylpyrrolidone-Doped Zn-Fe Layered Double Hydroxide.

Zn-Fe layered double hydroxide (LDH) was synthesized through the low-temperature-based coprecipitation method. Various concentrations of Ag (1, 3, and 5 wt %) with a fixed amount (5 wt %) of polyvinylpyrrolidone (PVP) were doped into LDH nanocomposites. This research aims to improve the bactericidal properties and catalytic activities of doping-dependent nanocomposites. Adding Ag and PVP to LDH enhanced oxygen vacancies, which increased the amount of hydroxide adsorption sites and the number of active sites. The doped LDH was employed to degrade rhodamine-B dye in the presence of a reducing agent (NaBH4), and the obtained results showed maximum dye degradation in a basic medium compared to acidic and neutral. The bactericidal efficacy of doped Zn-Fe (5 wt %) showed a considerably greater inhibition zone of 3.65 mm against Gram-negative (G-ve) or Escherichia coli (E. coli). Furthermore, molecular docking was used to decipher the mystery behind the microbicidal action of Ag-doped PVP/Zn-Fe LDH and to propose an inhibition mechanism of ß-ketoacyl-acyl carrier protein synthase IIE. coli (FabH) and deoxyribonucleic acid gyrase E. coli behind in vitro results.

Efficient Samarium-Grafted-C3N4-Doped α-MoO3 Used as a Dye Degrader and Antibacterial Agent: In Silico Molecular Docking Study.

This study was used to evaluate the catalytic activity (CA) and bactericidal activity of α-MoO3 and Sm-g-C3N4-doped α-MoO3 composites prepared through an efficient, cost-effective coprecipitation route. Their characteristic studies verify the formation of α-MoO3 and its composites (3, 6, and 9 mL Sm-g-C3N4-doped α-MoO3), which showed high crystallinity, as confirmed by X-ray diffraction (XRD) analysis. The production of superoxide and hydroxyl radicals due to charge transfer through α-MoO3 and g-C3N4 eventually forms electrons in g-C3N4 and holes around α-MoO3. CA against Rhodamine B (RhB) in basic medium provides maximum results compared to acidic and neutral media. The bactericidal efficacy of the (9 mL) doped sample represents a greater inhibition zone of 6.10 mm against the negative bacterial strain Escherichia coli. Furthermore, in silico studies showed that the generated nanorods may inhibit DNA gyrase and dihydropteroate synthase (DHPS) enzymes.

Mathematical analysis and molecular descriptors of two novel metal-organic models with chemical applications.

Metal-Organic Networks (MONs) are made by chemical molecules that contain metal ions and organic ligands. A crystalline porous solid called Metal-Organic Networks (MONs) is made up of a [Formula: see text] metal network of ions held in place by a multidentate ligand. (MONs) can be used for gas storage, purification drug delivery, gas separation, catalysis, and sensing applications. There is enormous potential for effective integration and research of MONs in diverse applications. Molecular descriptors are arithmetic measures that reveal a chemical substance's physical and chemical characteristics in its foundational network in a natural relationship. They demonstrate an important role in theoretical and ecological chemistry, and in the field of medicine. In this research, we calculated various recently discovered molecular descriptors viz. the modified version of second zagreb index, harmonic index, reciprocal randic index, modified version of forgotten topological index, redefined first zagreb topological index, redefined second zagreb topological index and redefined third zagreb topological index for two separate metal-organic networks. The numerical and graphical comparative analysis of these considered molecular descriptors are also performed.

Disease Detection in Plum Using Convolutional Neural Network under True Field Conditions.

The agriculture sector faces crop losses every year due to diseases around the globe, which adversely affect food productivity and quality. Detecting and identifying plant diseases at an early stage is still a challenge for farmers, particularly in developing countries. Widespread use of mobile computing devices and the advancements in artificial intelligence have created opportunities for developing technologies to assist farmers in plant disease detection and treatment. To this end, deep learning has been widely used for disease detection in plants with highly favorable outcomes. In this paper, we propose an efficient convolutional neural network-based disease detection framework in plum under true field conditions for resource-constrained devices. As opposed to the publicly available datasets, images used in this study were collected in the field by considering important parameters of image-capturing devices such as angle, scale, orientation, and environmental conditions. Furthermore, extensive data augmentation was used to expand the dataset and make it more challenging to enable robust training. Investigations of recent architectures revealed that transfer learning of scale-sensitive models like Inception yield results much better with such challenging datasets with extensive data augmentation. Through parameter quantization, we optimized the Inception-v3 model for deployment on resource-constrained devices. The optimized model successfully classified healthy and diseased fruits and leaves with more than 92% accuracy on mobile devices.