Nano

Graphitic carbon nitride (g-C₃N₄)-based materials are considered promising catalysts for organic waste photodegradation due to their excellent photocatalytic activity and stability. Herein, a ternary thin layer system based on g-C₃N₄ was prepared by hexagonal boron nitride (h-BN) deposition followed by liquid phase ultrasonic stripping and Ag deposition (marked as Ag-BCN), which increased the specific surface area of g-C₃N₄, expanded the corresponding range of the visible-near-infrared spectrum of g-C₃N₄ and promoted the photo-generated electron–hole pair separation in g-C₃N₄ to improve the photocatalytic efficiency of pure g-C₃N₄. The results showed that after deposition of h-BN and Ag into bulk g-C₃N₄ and liquid phase ultrasonic stripping, the efficiency of photocatalytic degradation of tannic acid (TA) was enhanced. The efficiency of photocatalytic degradation of TA by Ag-BCN-2 was 84.3% at 90 min, which was 1.6 times the efficiency of bulk g-C₃N₄.

This study provides evidence for the green synthesis of zinc oxide nanoparticles (ZnO NPs) using Vitex negundo leaf extract. The UV–Visible (UV–Vis) spectrum of ZnO NPs and calcinated ZnO NPs (ZnO-C) showed peaks at 370nm and 374nm, respectively, confirming zinc ion reduction to zinc oxide. The ZnO NPs and calcinated counterparts were further characterized by FTIR, XRD, FE-SEM and EDX. FTIR results revealed the presence of alcoholic and aromatic groups, like flavonoids, in the leaf extract. The XRD pattern showed a distinctive Wurtzite crystalline phase with a hexagonal shape. The Brunauer–Emmett–Teller (BET) analysis data revealed that ZnO’s surface area and pore size is 22.8m²/g and 12.9nm, whereas ZnO-C exhibited a surface area of 23.5m²/g and pore size of 13.1nm. The SEM data demonstrated numerous irregular and agglomerated flakes fusing to form a roughly spherical morphology with the size, in the range of 15–20nm and 11–16nm for ZnO and ZnO-C NPs, respectively. The results of the antimicrobial assay by disc diffusion method and MIC testing revealed that ZnO and ZnO-C NPs exhibited moderate to high antimicrobial activity against various microorganisms, indicating their application against bacterial infection. In addition, the ZnO NPs significantly disrupted the biofilm of Bacillus cereus and Pseudomonas aeruginosa, as confirmed by CV assay and fluorescent microscopy.

This paper has presented a novel method for the synthesis of regular and reproductive ZnO nanostructures by microwave heating. Unlike the previous studies, it has focused on ZnO nanostructures grown on the Si wafer used as a cover rather than on the precursor top and the results show that ZnO nanostructures with different shapes depending on the amount of mixture and the position in the Si wafer grow on the regular and reproductive basis. In the center of the Si wafer, short and coarse ZnO nanorods have grown, and on the outer edges, the structure of bundles consisting of ZnO nanorods with much longer lengths and smaller diameters has been synthesized. Morphology of ZnO nanorods was characterized by using scanning electron microscope and the nanorods grown on the outer edges had a mean diameter of 200nm and were up to $100\mu$m long. In order to elucidate the growth mechanism of nanorods, we have carried out SEM, energy dispersive spectrometer (EDS) and XRD characterization, which shows that ZnO nanorods have grown according to VLS mechanism. A controllable growth method of ZnO nanoneedle is suggested.

Despite the emergence of numerous carbon dots (CDs)-based sensors, most of them are fluorescent probes. To improve the reliability of the results, using ratiometric fluorescence/UV-vis absorption dual-signal probes is an effective method. Herein, nitrogen and chlorine co-doped carbon dots (N, Cl-CDs) with yellow-green fluorescence were prepared and used as a dual-function probe for ratiometric fluorescence/UV-vis. The detection range of the fluorescence method was 0.05–78$\mu$M, and the limit of detection (LOD) was 1.2nM (3$\sigma$/s). For the colorimetric method, the linear range was 0.05–186$\mu$M with LOD of 10.7nM. Importantly, the N, Cl-CDs were successfully used to detect Quercetin (QT) in actual water samples. Besides, due to their good biocompatibility, the N, Cl-CDs were applied to in vivo biological imaging of oocysts and shown the potential for environmental survey and biological assay.

Electrospinning is a well-established technique for creating submicron polymer fibers to create distinctive functional nanostructures. Electrospinning parameters including solution concentration, tip-to-collector distance and applied voltage are adjusted to produce nonwovens with variable fiber diameter distributions. This research on the response surface methodology (RSM) is being used to model the diameter of a polyamide-6,6 (PA-6,6) nanofiber mat. The experiments were designed using central composite design (CCD) and RSM, which evaluated the interactions between the operating variables (polymer concentration, applied positive voltage, needle tip to collector distance and spinning angle) on the diameter of the PA-6,6 nanofiber. The average nanofiber diameter increased as the concentration of the PA-6,6 polymer solution increased. The model’s strong regression coefficient ($R^2=0.98$) reveals that it did a desirable of predicting the diameter of PA-6,6 fibers. The experimental findings and predicted fiber diameters were in good agreement. The results demonstrate that the optimization of electrospinning process to produce the smallest nanofibers and narrowest diameter distribution ($183\pm \mathrm{\text{nm}}$) combined effects of 10% polymer concentration, 21kV applied positive voltage, 18cm needle tip to collector distance and ${60}^{\circ }$ spinning angle are substantial interacting effects that have an impact on the surface response nanofibers. The results of the research and several mathematical models will offer useful guidelines for choosing parameter settings for electrospinning PA-6,6 to achieve the required fiber diameter. By saving time, effort and money more effectively, this research will expand the field of investigation for the quality of electrospun nanofibers.

Controlling foodborne pathogens is challenging due to the new emergence of antimicrobial resistance to conventional antimicrobials. Therefore, new alternative antimicrobials need to be developed to control foodborne pathogens and avoid microbial resistance. Furthermore, the potential of trans-Himalayan high-altitude seabuckthorn (Hippophae rhamnoides ) plants has not been explored much for the synthesis of silver nanoparticles having antibacterial properties. Hence, this study investigated the development of long-term stable silver nanomaterials using the aqueous extract of seabuckthorn leaves through a green-synthesis approach and evaluated their antibacterial efficacy. These synthesized nanomaterials were characterized along with their stability using UV spectrometry for lambda max ($\lambda max$) characteristics. Further, the nanoparticles were evaluated for zeta potential (mV), polydispersity index, particle size distribution (nm), electrophoretic mobility ($\mu$mcm/Vs) and conductivity (mS/cm). The seabuckthorn silver nanoparticles are of 260nm size and stable (−15mV zeta potential) with good antioxidant and antibacterial properties against two typical food pathogens, e.g., Escherichia coli (MTCC 3222) and Salmonella typhimurium (MTCC 3224). Hence, this study developed an eco-friendly, rapid, low-cost green synthesis method for the development of seabuckthorn-coated, stable silver nanoparticles with good antibacterial activity against foodborne pathogens.

Diabetes is a chronic disease that affects millions of people worldwide. Accurate and timely diagnosis of diabetes is crucial for its effective treatment and management. While machine learning has shown promise in predicting the disease, missing data, outliers, class imbalance and limitations of classifiers can hinder accuracy.

To address these challenges, we propose a novel machine learning approach that combines adaptive iterative imputation (AII) for missing value imputation, dynamic ensemble isolation forest (DE-IF) for outlier detection and removal, Iterated KMeans SMOTEENN (IKMSENN) for class imbalance, and an adaptive extra tree classifier (AETC) for classification. Our approach is evaluated using the Pima Indian Diabetes Dataset (PIDD), a widely used benchmark dataset in diabetes disease prediction.

Experimental results show that our approach outperforms several state-of-the-art machine learning models in terms of accuracy, precision, recall, $f$-measure, and the area under the receiver operating characteristic (ROC) curve (AUC-ROC). Our approach achieved an accuracy of 98.58%, with a precision of 0.986, recall of 0.987, $f$-measure of 0.985, and ROC of 0.965 on the PIDD dataset.

Our research presents a significant contribution to the field of diabetes disease prediction by introducing novel machine learning approaches that address common challenges such as missing data, outliers and class imbalance, as well as limitations of classifiers. Our approach has the potential to greatly improve the accuracy and effectiveness of diabetes disease prediction and has important implications for the diagnosis and management of the disease.