Biogenic silver nanoparticles were synthesized using novel Terminalia bellerica kernel extract. Optimal synthesis of silver nanoparticles was achieved at 0.016 mg/mL kernel extract and 2.0 mM silver nitrate concentrations under ambient conditions. Silver nanoparticles were characterized by ultraviolet–visible absorption spectroscopy, transmission electron & scanning electron microscopy, energy dispersive X-ray analysis, X-ray diffraction, and Fourier transform infrared spectroscopy. Synthesized silver nanoparticles displayed innate catalytic reduction of organic pollutants such as 4-nitrophenol, methylene blue, eosin yellow and methyl orange. Results revealed that among all the pollutants, nanosilver exhibited higher reduction of 4-nitrophenol than others and reaction was found following the pseudo-first order kinetics. An artificial neural networks (ANNs) model based on experimental data was developed to predict the catalytic performance of nanosilver. Good correlation between ANN model based results and experimental data indicated that it could be used to forecast the catalytic performance and hence extent of pollutant reduction at various catalyst concentrations.

使用新型Terminalia bellerica合成生物银纳米颗粒仁提取物。在环境条件下，以0.016 mg / mL的仁提取物和2.0 mM的硝酸银浓度可实现银纳米颗粒的最佳合成。银纳米颗粒的特征在于紫外可见吸收光谱，透射电子和扫描电子显微镜，能量色散X射线分析，X射线衍射和傅里叶变换红外光谱。合成的银纳米颗粒表现出先天催化还原有机污染物（例如4-硝基苯酚，亚甲基蓝，曙红和甲基橙）的能力。结果表明，在所有污染物中，纳米银对4-硝基苯酚的还原度均高于其他污染物，并且其反应遵循拟一级动力学。建立了基于实验数据的人工神经网络模型，以预测纳米银的催化性能。基于ANN模型的结果与实验数据之间的良好相关性表明，它可用于预测催化性能，从而预测各种催化剂浓度下污染物的还原程度。