The usage of the green synthesis method to produce nanoparticles (NPs) has received great acceptance among the scientific community in recent years. This, perhaps, is owing to its eco-friendliness and the utilization of non-toxic materials during the synthesizing process. The green synthesis approach also supplies a reducing and a capping agent, which increases the stability of the NPs through the available phytochemicals in the plant extractions. The present study describes a green synthesis method to produce nano-silica (SiO2) NPs utilizing Rhus coriaria L. extract and sodium metasilicate (Na2SiO3.5H2O) under reflux conditions. Sodium hydroxide (NaOH) is added to the mixture to control the pH of the solution. Then, the obtained NPs have been compared with the chemically synthesized SiO2 NPs. The structure, thermal, and morphological properties of the SiO2 NPs, both green synthesized and chemically synthesized, were characterized using Fourier-transform infrared spectroscopy (FTIR), Ultraviolet-Visible Spectroscopy (UV-Vis), X-ray diffraction (XRD), and Field Emission Scanning Electron Microscopy (FESEM). Also, the elemental compassion distribution was studied by energy-dispersive X-ray spectroscopy (EDX). In addition, the zeta potential, dynamic light scatter (DLS), thermogravimetric analysis (TGA), and differential scanning calorimetry (DSC) was used to study the stability, thermal properties, and surface area of the SiO2 NPs. The overall results revealed that the green synthesis of SiO2 NPs outperforms chemically synthesized SiO2 NPs. This is expected since the green synthesis method provides higher stability, enhanced thermal properties, and a high surface area through the available phytochemicals in the Rhus coriaria L. extract.

Sustainable construction requires high-strength cement materials that additives with silica content could provide the requirements as well. In this study, the effect of the micro and nano-size of silica on the compressive strength of cement paste using different mathematical approaches is investigated. This study compares the strength of preferentially replaced cement pastes with microsilica (MS) and nanosilica (NS) incorporation by proposing several mathematical models. In this study, 205 data were extracted from the literature and analyzed. The modeling processes considered the most significant variables as input variables that influence the compression strength, such as curing time, which ranged between 3 and 90 days, the water-cement ratio, which varied between 0.4 and 0.85, and NS ranged between 0 and 15%. MS ranged between 0 and 40% based on the weight of cement. In this process, the compressive strength of cement paste modified with NS and MS was modeled using four different models, including the Linear Regression Model (LR), Nonlinear Model (NLR), Multi-Logistic Regression Model (MLR), and artificial neural network (ANN). The efficiency of the suggested models was evaluated using different statistical assessments, such as the Root Mean Squared Error (RMES), the Mean Absolute Error (MAE), Scatter Index (SI), Objective value (OBJ), and coefficient of determination (R2). The findings revealed that the ANN model conducted better performance for predicting compressive strength for cement paste than the other models based on the statistical assessment. In addition, based on the statistical assessment of the sensitivity of parameters, NS had more of an effect on the compressive strength of cement paste, with 6.3% more than MS.

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