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Zinc oxide nanoparticles (ZnO NPs) were formed through a simple green synthesis route using Vitex negundo (V. negundo) leaf extract as reducing and capping source. Morphological, structural, chemical and optical features of the prepared ZnO NPs were examined by field emission SEM, XRD, EDAX, PL, FTIR and UV–vis DRS, respectively. FESEM images precisely visualized morphology of the ZnO NPs as spherical with particles size ranges between 5 and 35 nm having a mean diameter of ∼ 19 nm. XRD pattern revealed formation of hexagonal wurtzite structured ZnO NPs with high crystallinity. Further, the observed asymmetric stretching vibration of Zn-O bond confirmed the formation of hexagonal wurtzite structured ZnO NPs. Photocatalytic activity of the ZnO NPs was assessed against methylene blue (MB) dye degradation under natural sunlight illumination. Results of the photocatalytic experiment disclosed an impressive MB dye degradation efficiency of 98.50 % at 60 min. Moreover, green synthesized ZnO NPs exhibited a maximum mineralization (TOC removal) efficiency of 92.34 % at 5 h of sunlight illumination.

A facile hydrothermal route was employed for the synthesis of iron-nickel bimetal organic frameworks (Fe-Ni bi-MOFs) and composite with an acid functionalized multi-walled carbon nanotubes (Fe-Ni MOF/f-MWCNTs) for electrochemical detection of tartrazine. The as-prepared Fe-Ni MOF/f-MWCNTs was confirmed by the several physicochemical studies. A micro spindle shaped, highly porous, and crystalline Fe-Ni MOF/f-MWCNTs was noticed. The high sensitivity and stability of Fe-Ni MOF/f-MWCNTs/GCE modified electrode was analyzed. Due to its high porosity nature, the analyte molecule effectively gets adsorbed on the modified electrode and undergo electrochemical oxidation effectively. The modified electrode exhibits low limit of detection (LOD) and limit of quantification (LOQ) as 0.04 × 10 -6 mol/L and 0.13 × 10 -6 mol/L towards tartrazine. These results reveal the potential applications of Fe-Ni MOF/f-MWCNTs/GCE as modified electrode material for sensitive detection of tartrazine along with its robust reproducibility, stability, and effective sensing properties., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

In this research, the impact of the different zinc (Zn) concentrations on the physical and optoelectronic properties of Bi 2 S 3 nanorods as self-powered and photodiode applications was investigated. The performance of P-N junction photodiodes has been for decades since they are crucial in energy applications. The structure, degree of crystallinity, and shape of Zn-doped Bi 2 S 3 nanorods of various doping percentages formed onto the indium tin oxide (ITO) substrates by the dip coating technique are investigated using X-ray powder diffraction (XRD) and SEM. With increasing illumination time, the current-voltage (I-V) graphs demonstrate a rise in photocurrent. The diode's idealist factor was estimated using the I-V technique under 30 min of light illumination., (© 2024 John Wiley & Sons Ltd.)

Objectives: In this paper, we look at how to design and build a system to find tumors using 2 Convolutional Neural Network (CNN) models. With the help of digital image processing and deep Learning, we can make a system that automatically diagnoses and finds different diseases and abnormalities. The tumor detection system may include image enhancement, segmentation, data enhancement, feature extraction, and classification. These options are set up so that the CNN model can give the best results., Methods: During the training phase, the learning rate is used to change the weights and bias. The learning rate also changes the weights. One Epoch is when all of the training images are shown to the model. As the training data may be very large, the data in each epoch are split into batches. Every epoch has a training session and a test session. After each epoch, the weights are changed based on how fast the CNN is learning. This is done with the help of optimization algorithms. The suggested technique uses the anticipated mean intersection over union value to identify failure instances in addition to forecasting the mean intersection over union., Results: This paper talks about how to separate brain tumors from magnetic resonance images of patients taken from "Brain web." Using basic ideas of digital image processing, magnetic resonance images are used to extract and find tumors using a hybrid method. In this paper, the proposed algorithm is applied with the help of MATLAB. In medical image processing, brain tumor segmentation is an important task. The goal of this paper is to look at different ways to divide brain tumors using magnetic resonance imaging. Recently, automatic segmentation using deep learning methods has become popular because these methods get the best results and are better at solving this problem than others. Deep learning methods can also be used to process and evaluate large amounts of magnetic resonance imaging image data quickly and objectively., Conclusions: A classification method based on a convolution neural network is also added to the proposed scheme to make it more accurate and cut down on the amount of time it takes to do the calculations. Also, the results of the classification are given as images of a tumor or a healthy brain. The training is 98.5% correct. In the same way, both the validation accuracy and validation loss are high., Competing Interests: The authors declare no conflicts of interest., (Copyright © 2024 Wolters Kluwer Health, Inc. All rights reserved.)

Nanotechnology has advanced significantly; however, little is known about the potential implications on human health-related issues, particularly blood carrying enzymes. Ionic liquids are also well-recognized for maintaining the structure and activity of enzymes. In this regard, we delineate a facile synthetic approach of preparation of Fe 3 O 4 nanoparticles (NPs) as well as choline hydroxide [CH][OH] ionic liquid (IL)-supported Fe 3 O 4 NPs (Fe 3 O 4 -CHOH). This approach of combining magnetic nanoparticles (MNPs) with IL results in distinctive properties, which may offer enormous utility in the field of biomedical research due to the effortless separation of MNPs by an external magnetic field. Detailed characterization of MNPs including Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), Raman spectroscopy, transmission electron microscopy (TEM), and scanning electron microscopy (SEM) was carried out. The biomolecular interactions of Fe 3 O 4 and Fe 3 O 4 -CHOH NPs with cytochrome c (Cyt c) were studied in detail using various spectroscopic and microscopic techniques. From spectroscopic studies, it can be concluded that the secondary structure of Cyt c is more stable in the presence of Fe 3 O 4 -CHOH NPs than Fe 3 O 4 NPs. The binding constant of Cyt c in the presence of MNPs was also calculated using the Benesi-Hildebrand equation. Furthermore, dynamic light scattering (DLS), ζ-potential, and microscopic studies were performed to study the interaction of Cyt c with MNPs. These studies provided evidence favoring the formation of bionanoconjugates of Cyt c with MNPs. Moreover, the enzymatic activity of Cyt c increases in the presence of both MNPs. The peroxidase activity of Cyt c in MNPs explicitly elucidates that the enzyme is preserved for a long time in the presence of Fe 3 O 4 -CHOH NPs. Later on, TEM and field emission scanning electron microscopy (FESEM) were also performed to gather more information regarding the morphology of Cyt c in the presence of MNPs.