Recent progress in bio-mediated synthesis and applications of engineered nanomaterials for sustainable agriculture.

The ever-increasing demand for agricultural food products, medicine, and other commercial sectors requires new technologies for agricultural practices and promoting the optimum utilization of natural resources. The application of engineered nanomaterials (ENMs) enhance the biomass production and yield of food crop while resisting harmful environmental stresses. Bio-mediated synthesis of ENMs are time-efficient, low-cost, environmentally friendly, green technology. The precedence of using a bio-mediated route over conventional precursors for ENM synthesis is non-toxic and readily available. It possesses many active agents that can facilitate the reduction and stabilization processes during nanoparticle formation. This review presents recent developments in bio-mediated ENMs and green synthesis techniques using plants, algae, fungi, and bacteria, including significant contributions to identifying major ENM applications in agriculture with potential impacts on sustainability, such as the role of different ENMs in agriculture and their impact on different plant species. The review also covers the advantages and disadvantages of different ENMs and potential future research in this field.

Data on manmade sinkholes due to leakage in underground pipelines in different subsurface soil profiles.

This paper provides simulated datasets for different versions of small-scale physical sinkhole models that are essential to understand the sinkhole formation rate. These physical models were used in experiments to monitor ground settlement or collapse due to leakage from an underground pipeline. The factors under consideration were the subsurface soil profile, pattern of water flow, and leakage position in the pipeline. The experimental results and statistical analysis showed that the subsurface soil strata conditions dominated the sinkhole occurrence mechanism, although other factors also contributed to the settlement. The results also showed that the subsurface soil comprising strata sandy clay, limestone, and bedrock (SC-LS-BR) dominates the sinkhole mechanism. The data are organized and formated in a useful structure. Specifically, the dataset is presented in terms of tables to illustrate the settlements in different soil profiles under various conditions. This analysis was then used to predict the sinkhole risk level under different conditions. The formulated dataset and the results can be considered in developing a sinkhole risk index (SRI) and identifying sinkhole risk areas.