Zinc oxide nanoparticles (NPs) are the third highest in terms of global production among the various inorganic nanoparticles, and there are concerns because of their worldwide availability and accumulation in the environment. In contrast, zinc is an essential element in plant growth and metabolism, and ZnO NPs (nano-ZnO) may have unknown interactions with plants due to their small sizes as well as their particular chemical and physical characteristics. The present study examined the effect of nano-ZnO (25 nm) and bulk or natural form (<1000 nm, bulk-ZnO), compared with zinc in the ionic form (ZnSO4) on Nicotiana tabacum seedlings in a nutrient solution supplemented with either nano-ZnO, bulk-ZnO (0.2, 1, 5 and 25 µM) or ZnSO4 (control) for 21 days. Results showed that nano-ZnO at most of the levels and 1 µM bulk-ZnO positively affected growth (root and shoot length/dry weight), leaf surface area and its metabolites (auxin, phenolic compounds, flavonoids), leaf enzymatic activities (CAT, APX, SOD, POX, GPX, PPO and PAL) and anatomical properties (root, stem, cortex and central cylinder diameters), while bulk-ZnO caused decreases at other levels. The activities of enzymes were induced to a greater extent by intermediate nano-ZnO levels than by extreme concentrations, and were higher in nano-ZnO treated than in bulk treated tobacco. As the ZnO level increased, the vascular expansion and cell wall thickening of the collenchyma/parenchyma cells occurred, which was more pronounced when treated by NPs than by its counterpart. The Zn content of root and leaf increased in most of ZnO treatments, whereas the Fe content of leaves decreased. Our findings indicate that tobacco responded positively to 1 µM bulk-ZnO and to nearly all nano-ZnO levels (with the best levels being at 0.2 µM and 1 µM) by morphological, physiological and anatomical adjustments.

Environmental stresses, such as cold, heat, salinity, and drought, induce ethylene production and oxidative stress and cause damage in plants. On the other hand, studies have shown that salicylic acid (SA) induced resistance to environmental s...

Nanoparticles impose multidimensional effects on living cells that significantly vary among different studies. Machine learning (ML) methods are recommended to elucidate more consistence and predictable relations among the affected parameters. In this study, nine ML algorithms [Support-Vector Regression (SVR), Linear, Bagging, Stochastic Gradient Descent (SGD), Gaussian Process, Random Sample Consensus (RANSAC), Partial Least Squares (PLS), Kernel Ridge, and Random Forest] were applied to evaluate their efficiency in predicting the effects of zinc oxide nanoparticles (ZnO NPs: 0.5, 1, 5, 25, and 125 µM) and microparticles (ZnO MPs: 1, 5, 25, and 125 µM) on Carum copticum. The plant root/shoot biomass; number of leaves, branches, umbellates, and flowers; protein content; reducing sugars; phenolic compounds; chlorophylls (a, b, Total); carotenoids; anthocyanins; H2O2; proline; malondialdehyde (MDA); tissue zinc content; superoxide dismutase (SOD) activity; and media ΔpH were measured and considered input variables. All levels of ZnO MPs treatments increased growth parameters compared to the control (ZnSO4). The highest shoot/root fresh and dry mass were recorded at 5 µM ZnO MPs compared with the control. The root fresh/dry mass under ZnO NPs treatments was more sensitive than shoot parameters. The number of flowers increased by 134 and 79% in MPs and NPs treatments compared to the control, respectively. ZnO NPs reduced protein content by up to 81% in 125 µM NPs compared to ZnSO4. Reducing sugar content increased to 25, 40 and 36% in 5, 25, 125 µM MPs and 67, 68, 26, 26 and 21% in 0.5, 1, 5, 25 and 125 µM NPs treatments, respectively. The pH alteration was more significant under NPs and affected zinc uptake. All levels of ZnO NPs treatments increased growth parameters compared to the control. All ML algorithms showed varied efficiencies in predicting the nonlinear relationships among parameters, with higher efficiency in predicting the behavior of root and shoot dry mass, root fresh weight and number of flowers according to R2 index. The model obtained from SVR with the radial basis function (RBF) kernel was selected as a comprehensive model for predicting and determining the efficacy of the results.