Journal of Chemistry

Panax ginseng is a famous valuable folk herb, the price of which particularly depends on its cultivation age. Since the classical chemical analysis usually needs to destroy the plants which is not suitable for the old wild ginseng due to the huge cost, in the current study, we employed the headspace solid-phase microextraction (HS-SPME) GC-MS methodology which has the advantage of time-saving, nondestructive, and green to analyze the growth age of ginseng (roots) via determining the volatile components of ginseng stem and leaves (GSLs) combined with metabolic profiles way. 100 mg of finely ground GSL samples (3–7 years) were extracted by a 65 μm PDMS-DVB fiber in the headspace and then analyzed within 20 mins. The content assay of total saponins, crude polysaccharides, and total protein was also included. As a result of GC-MS-based profiling, the orthogonal partial least-squares discriminant analysis’ (OPLS-DA) score plots showed excellent classification of four comparison groups (the 3- and 4-, 3-and 5-, 3- and 6-, and 3- and 7-year-old GSL) with 100% discrimination rate, respectively. 263 common differential variables were gained referring to the VIP and values. 32 volatile metabolites were identified and showed good age discrimination capacities with an area under the curve (AUC) value of more than 0.8 by the receiver operating characteristic (ROC) analysis. Remarkably, there was one volatile component of sesquiterpenes, 1-cyclohexene-1-carboxaldehyde, 2, 6, 6-trimethyl-, which showed an excellent prediction with the AUC value of 1.0 among all the four compared groups, which could be the potential biomarker to distinguish all the four compared groups. The total contents of different growth ages of components of ginseng stems and leaves were distinct. The contents of total protein (4.80 ± 0.27%) and crude polysaccharide (24.15 ± 0.89%) in the 4th year of GSLs were the highest among the five cultivation years. The content of total saponins (4.66 ± 0.38%) in the 5th year of GSLs was the highest. These results could provide a novel reference for the GSLs’ harvest and application periods. Moreover, the current study could also supply a promising way to set up a nondestructive, in situ, and green approach to discriminate the growth age of Panax ginseng rapidly without digging the roots out instead of detecting the VOCs of GSLs.

Numerous essential oils have been researched as biopesticides because of their effectiveness and environmental safety. Nevertheless, encapsulation is necessary to improve its physical, chemical, and biological properties. Therefore, this paper aims to investigate the physicochemical characteristics and antifungal activity of the Artemisia herba-alba essential oil (HAEO) encapsulated in succinic acid-modified β-cyclodextrin (SACD). Hydrodistillation was used to extract a yellowish oil from the plant A. herba-alba, and gas chromatography coupled to mass spectrometry was performed to determine the chemical composition. The scanning electron microscope, Fourier transform infrared, thermogravimetric analysis, and docking studies were combined to validate the molecular inclusion of HAEO with SACD. The antifungal activity was examined against Botrytis cinerea by direct contact with a potato dextrose agar. The results showed that the main compound in HAEO was α-thujone (65.0%). Furthermore, HAEO has been successfully incorporated into SACD with a binding energy value of −5.3 kJ·mol⁻¹, and its thermal stability improved. At a 0.1% concentration, HAEO in encapsulated form showed a higher ability to inhibit B. cinerea (38.34%), compared to EO in free form (12.00%). Thus, these findings produced evidence that the strategy of encapsulating EOs by SACD can develop novel B. cinerea inhibitors and a promising alternative biopesticide.

Using a hydrothermal synthesis process, Fe-doped ZnO/SnS nanostructures were created and a variety of analytical methods were used to describe their characteristics. X-ray diffraction patterns were employed to confirm the hexagonal and orthorhombic crystal structures of ZnO and SnS, respectively. Nanorods and nanoparticle clouds were visible in TEM pictures, and XPS investigation verified that the dopant Fe ions were in the 3+ oxidation state. Additionally, absorption spectroscopy revealed a decrease in the energy bandgap with an increase in Fe content, and photoluminescence analysis demonstrated that the ZSF3 sample significantly reduced the rate of recombination of charge carriers. Impressively, the optimized sample (ZSF3) displayed 95.8% more photocatalytic activity during the 120 min degradation of MB dye. This study demonstrated that an easy hydrothermal procedure, carried out at 220°C for 12 hours, may be used to create iron-doped ZnO/SnS nanocomposites. The tunable energy bandgap characteristics of heterogeneous semiconducting materials and the effective charge carrier separation were thought to be the causes of the increased photocatalytic activity. Furthermore, the heterostructure of charge carriers was proposed to facilitate photocatalytic activity when exposed to light.

A closed-vessel microwave digestion method was developed for the simultaneous determination of macro, micro, and toxic elements (Mg, K, Ca, Al, Mn, Fe, Cu, Zn, Se, Rb, V, Cd, and As) in wheat by inductively coupled plasma mass spectrometry (ICP-MS). A two-level factorial design with four variables was employed to determine the optimal conditions for the significant parameters. The optimal conditions for microwave digestion of ground wheat samples were found to be 0.5 g of the sample, 9 mL of 8 mol/L HNO₃, and 3 mL of 33% H₂O₂ at 210°C. The analytical method was validated, and the accuracy of the method was then evaluated through analysis of wheat flour-certified standard reference materials (NIST SRM 1567b). The validated procedure was subsequently applied to the analysis of 12 wheat samples sourced from local farms in the Al-Qassim region and a further six samples purchased from the Al-Qassim markets, covering six imported brands common in Saudi Arabia. The results of the study showed that the level of minerals in wheat grown in desert areas is higher than in other areas. The concentrations of macroelements and microelements in the Al-Qassim samples exceeded those of the imported samples by 12.41% and those of the samples from other countries by 33.90%. The results were also evaluated using multivariate analysis techniques: principal component analysis (PCA) and hierarchical cluster analysis (HCA).

The exploration of transforming various metals as metal sources into metal organic frameworks (MOFs) has attracted considerable attention in recent years. This study used triethylamine and 5,6-dimethylbenzimidazole (DMBIM) to modify the iron-doped cobalt zeolite imidazole framework on carbon fiber structure. The results showed that the ZIF(Fe/Co)_1.25@CNF exhibited high CEF removal efficiency that over 97% of ceftiofur sodium (CEF) was removed within 10 min, which was significantly higher than that of the common ZIF-67 materials. Compared with other ZIF-67 composites, ZIF(Fe/Co)_x@CNF has a larger specific surface area, resulting in a larger contact area and more active sites during the reaction. After the introduction of DMBIM, the ZIF(Fe/Co)_1.25@CNF not only easily separated from the solution but also enhanced the hydrophobicity, which provided higher catalytic stability and catalytic performance. In addition, the effects of different catalyst ratios, pollutant concentrations, solution pH, and different radicals (•OH, SO₄^•−, ¹O₂) on the activation of peroxymonosulfate (PMS) were investigated. The mechanism of degradation was elucidated by electron paramagnetic resonance experiments. This study provides a new perspective for the preparation of high-performance MOF catalysts with excellent catalytic performance and may facilitate the application of MOF materials in more practical situations.

This research created hot-pressed composites of the AA6063 matrix with varying concentrations of ZrO₂ (0.25, 0.5, and 1 wt %). At sliding speeds of 80, 120, and 150 mm/s, the wear performance of the specimen was studied at loads of 10 N, 15 N, 20 N, and 25 N. The authors analyzed the counter-face material, the wear debris, and the worn surfaces to learn about the wear mechanisms. Developing these three machine learning (ML) algorithms was to evaluate the ability to predict wear behavior using the same small dataset collected using varying test processes. A thorough examination of each model hyperparameter tuning phase was performed. The predictive performance was analyzed using several statistical tools. The most effective decision-making algorithms for this data collection were those based on trees. Predictions made by the decision tree algorithm for the test and validation measurements have an accuracy of 86% and 99.7%, respectively. The best model was picked out based on the results of the predictions.