Continuously increasing energy demand and growing concern about energy resources has attracted much research in the field of clean and sustainable energy sources. In this context, zero-emission fuels are required for energy production to reduce the usage of fossil fuel resources. Here, we present the synthesis of Pd-Ag-decorated reduced graphene oxide (rGO) nanostructures using a green chemical approach with stevia extract for hydrogen production and antibacterial studies under light irradiation. Moreover, bimetallic nanostructures are potentially lime lighted due to their synergetic effect in both scientific and technical aspects. Structural characteristics such as crystal structure and morphological features of the synthesized nanostructures were analyzed using X-ray diffraction and transmission electron microscopy. Analysis of elemental composition and oxidation states was carried out by X-ray photoelectron spectroscopy. Optical characteristics of the biosynthesized nanostructures were obtained by UV-Vis absorption spectroscopy, and Fourier transform infrared spectroscopy was used to investigate possible functional groups that act as reducing and capping agents. The antimicrobial activity of the biosynthesized Pd-Ag-decorated rGO nanostructures was excellent, inactivating 96% of Escherichia coli cells during experiments over 150 min under visible light irradiation. Hence, these biosynthesized Pd-Ag-decorated rGO nanostructures can be utilized for alternative nanomaterial-based drug development in the future.
The phytochemicals found inCaralluma pauciflorawere studied for their ability to reduce silver nitrate in order to synthesise silver nanoparticles (AgNPs) and characterise their size and crystal structure. Thunbergol, 1,1,6-trimethyl-3-methylene-2-(3,6,9,13-tetram, Methyl nonadecanoate, Methyl cis-13,16-Docosadienate, and (1R,4aR,5S)-5-[(E)-5-Hydroxy-3-methylpent were the major compounds identified in the methanol extract by gas chromatography-mass spectrum analysis. UV/Vis spectra, Fourier-transform infrared spectroscopy, x-ray diffraction, scanning electron microscope with Energy Dispersive Xâray Analysis (EDAX), Dynamic Light Scattering (DLS) particle size analyser and atomic force microscope (AfM) were used to characterise theCaralluma paucifloraplant extract-based AgNPs. The crystal structure and estimated size of the AgNPs ranged from 20.2 to 43 nm, according to the characterization data. The anti-cancer activity of silver nanoparticles (AgNPs) synthesised fromCaralluma paucifloraextract. The AgNPs inhibited more than 60% of the AGS cell lines and had an IC50 value of 10.9640.318 g, according to the findings. The cells were further examined using fluorescence microscopy, which revealed that the AgNPs triggered apoptosis in the cells. Furthermore, the researchers looked at the levels of reactive oxygen species (ROS) in cells treated with AgNPs and discovered that the existence of ROS was indicated by green fluorescence. Finally, apoptotic gene mRNA expression analysis revealed that three target proteins (AKT, mTOR, and pI3K) were downregulated following AgNP therapy. Overall, the findings imply that AgNPs synthesised from Caralluma pauciflora extract could be used to treat human gastric cancer.
Prenatal heart disease, generally known as cardiac problems (CHDs), is a group of ailments that damage the heartbeat and has recently now become top deaths worldwide. It connects a plethora of cardiovascular diseases risks to the urgent in need of accurate, trustworthy, and effective approaches for early recognition. Data preprocessing is a common method for evaluating big quantities of information in the medical business. To help clinicians forecast heart problems, investigators utilize a range of data mining algorithms to examine enormous volumes of intricate medical information. The system is predicated on classification models such as NB, KNN, DT, and RF algorithms, so it includes a variety of cardiac disease-related variables. It takes do with an entire dataset from the medical research database of patients with heart disease. The set has 300 instances and 75 attributes. Considering their relevance in establishing the usefulness of alternate approaches, only 15 of the 75 criteria are examined. The purpose of this research is to predict whether or not a person will develop cardiovascular disease. According to the statistics, naïve Bayes classifier has the highest overall accuracy.