Sustainable agriculture is a key component of the effort to meet the increased food demand of a rapidly increasing global population. Nano-biotechnology is a promising tool for sustainable agriculture. However, rather than acting as nanocarriers, some nanoparticles (NPs) with unique physiochemical properties inherently enhance plant growth and stress tolerance. This biological role of nanoparticles depends on their physiochemical properties, application method (foliar delivery, hydroponics, soil), and the applied concentration. Here we review the effects of the different types, properties, and concentrations of nanoparticles on plant growth and on various abiotic (salinity, drought, heat, high light, and heavy metals) and biotic (pathogens and herbivores) stresses. The ability of nanoparticles to stimulate plant growth by positive effects on seed germination, root or shoot growth, and biomass or grain yield is also considered. The information presented herein will allow researchers within and outside the nano-biotechnology field to better select the appropriate nanoparticles as starting materials in agricultural applications. Ultimately, a shift from testing/utilizing existing nanoparticles to designing specific nanoparticles based on agriculture needs will facilitate the use of nanotechnology in sustainable agriculture.

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This paper proposes an adaptive localized decision variable analysis approach under the decomposition-based framework to solve the large scale multi-objective and manyobjective optimization problems. Its main idea is to incorporate the guidance of reference vectors into the control variable analysis and optimize the decision variables using an adaptive strategy. Especially, in the control variable analysis, for each search direction, the convergence relevance degree of each decision variable is measured by a projection-based detection method. In the decision variable optimization, the grouped decision variables are optimized with an adaptive scalarization strategy, which is able to adaptively balance the convergence and diversity of the solutions in the objective space. The proposed algorithm is evaluated with a suite of test problems with 2-10 objectives and 200-1000 variables. Experimental results validate the effectiveness and efficiency of the proposed algorithm on the large scale multiobjective and many-objective optimization problems.

The rapid development of nanotechnology makes the environmental impact assessment a necessity to ensure the sustainable use of engineered nanomaterials. Here, silver nanoparticles (AgNPs) at 100 mg/kg were added to soils in the absence or presence of cucumber (Cucumis sativa) plants for 60 days. The response of the soil microbial community and associated soil metabolites was investigated by 16S rRNA gene sequencing and gas chromatography−mass spectrometry (GC−MS)-based metabolomics, respectively. The results show that AgNP exposure significantly increased the soil pH in both unplanted and cucumber-planted soils. The soil bacterial community structure was altered upon Ag exposure in both soils. Several functionally significant bacterial groups, which are associated with carbon, nitrogen, and phosphorus cycling, were compromised by AgNPs in both unplanted and cucumber-planted soils. Generally, plants played a limited role in mediating the impact of AgNPs on the bacterial community. Soil metabolomic analysis showed that AgNPs altered the metabolite profile in both unplanted and cucumber-planted soils. The significantly changed metabolites are involved in sugar and amino acid-related metabolic pathways, indicating the perturbation of C and N metabolism, which is consistent with the bacterial community structure results. In addition, several fatty acids were significantly decreased upon exposure to AgNPs in both unplanted and cucumber-planted soils, suggesting the possible oxidative stress imposed on microbial cell membranes. These results provide valuable information for understanding the biological and biochemical impact of AgNP exposure on both plant species and on soil microbial communities; such understanding is needed to understand the risk posed by these materials in the environment.