Nanoscale Fe/Ag particles activated persulfate: optimization using response surface methodology

• Published in: Water science and technology Vol. 75; no. 9-10; pp. 2216 - 2224
• Main Authors: Silveira, Jefferson E, Barreto-Rodrigues, Marcio, Cardoso, Tais O, Pliego, Gema, Munoz, Macarena, Zazo, Juan A, Casas, José A
• Format: Journal Article
• Language: English
• Published: England IWA Publishing 01-05-2017
• Subjects: Bimetals; Catalysis; Catalysts; Chloride; Chlorophenol; Chlorophenols; Design optimization; Electron microscopy; Environmental science; Hazardous materials; Iron; Iron - chemistry; Mathematical models; Metals; Microscopy, Electron, Scanning; Mineralization; Nanoparticles; Nanoparticles - chemistry; Oxidation; Particle physics; Pollutants; Removal; Response surface methodology; Scanning electron microscopy; Silver; Silver - chemistry; Sodium; Studies; Surface area; Temperature effects; Water Pollutants, Chemical - chemistry; Water Purification - methods; X-Ray Diffraction
• ISSN: 0273-1223; 1996-9732
• DOI: 10.2166/wst.2017.063

This work studied the bimetallic nanoparticles Fe-Ag (nZVI-Ag) activated persulfate (PS) in aqueous solution using response surface methodology. The Box-Behnken design (BBD) was employed to optimize three parameters (nZVI-Ag dose, reaction temperature, and PS concentration) using 4-chlorophenol (4-CP) as the target pollutant. The synthesis of nZVI-Ag particles was carried out through a reduction of FeCl
with NaBH
followed by reductive deposition of Ag. The catalyst was characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM) and Brunauer-Emmett-Teller (BET) surface area. The BBD was considered a satisfactory model to optimize the process. Confirmatory tests were carried out using predicted and experimental values under the optimal conditions (50 mg L
nZVI-Ag, 21 mM PS at 57 °C) and the complete removal of 4-CP achieved experimentally was successfully predicted by the model, whereas the mineralization degree predicted (90%) was slightly overestimated against the measured data (83%).