Facile green synthesis of silver nanoparticles using Terminalia bellerica kernel extract for catalytic reduction of anthropogenic water pollutants

• Published in: Colloid and interface science communications 2020-07, Vol.37, p.100276, Article 100276
• Main Authors: Sherin, Lubna, Sohail, Ayesha, Amjad, Um-e-Salma, Mustafa, Maria, Jabeen, Riffat, Ul-Hamid, Anwar
• Format: Article
• Language: English
• Subjects: Artificial neural networks; Catalytic reduction; Pollutants; Silver nanoparticles; Terminalia bellerica
• ISSN: 2215-0382; 2215-0382
• DOI: 10.1016/j.colcom.2020.100276

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. [Display omitted] •Biogenic silver nanoparticles were synthesized using novel T. bellerica kernel extract.•Reaction parameters were evaluated for the facile one pot synthesis of nanosilver.•Silver nanoparticles displayed innate catalytic reduction of organic pollutants.•Nanoparticles catalysis data was modelled via Artificial neural networks modeling.