Dataset on the optimization by response surface methodology for the synthesis of silver nanoparticles using...

Silver nanoparticles (AgNP) are important materials in developing novel devices owing to their unique physical and chemical properties. It attracted much interest because it exhibits a prominent Surface Plasmon Resonance (SPR) property that is dependent on its nanodimension and nanostructure. Its green synthesis using biological extracts offers a cheap and benign process to afford AgNPs. However, natural extracts contain thousands of metabolites that affect the formation of the desired AgNP. Other factors such as temperature, pH, time, and volume also influence the formation of the nanometal hence, optimization is always carried out to afford sufficient amounts of the nanometals. To eliminate further trials and errors, this work reports the optimization of AgNP using the aqueous extract from Laxitextum bicolor, a wild type of mushroom from the family of Stereacea. Using central composite design (CCD) under Response Surface Methodology (RSM), five levels from each of the five independent parameters (pH, temperature, time, volume of extract, and volume of AgNO₃) in a single-block mode afforded 32 experimental runs where SPR at 420 nm of the formed AgNP was measured as the dependent variable. ANOVA evaluation revealed that the p-value of the refined model is significant (p-value = 0.00) and the p-value of lack-of-fit is insignificant (LoF = 0.223). Model statistics displayed acceptable goodness of fit (R² = 98.54%, adjusted R² = 97.34, predicted R² = 92.50%). The predicted optimal condition to synthesize AgNP from aqueous extract of L. bicolor were determined to be pH = 10, Temperature = 55 °C, Time = 180 min, Vol. Ext = 1.5 mL, and Vol. AgNO₃ = 20 mL. To check the accuracy and repeatability of predicted optimal synthesis conditions, the UV-Vis analysis was employed. It showed that the peak intensity has a narrow peak with an absorbance of 3.40 at around 420 nm, which was the set criteria for choosing the optimal synthesis condition.