Adsorptive Removal of Amoxicillin Using Green-Synthesized SiNH2@FeNP Nanocomposite: Characterization, Optimization, and Modeling


PAMUKOĞLU M. Y., Babar Yoldaş B.

ACS Omega, cilt.10, sa.24, ss.25951-25961, 2025 (SCI-Expanded, Scopus)

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 10 Sayı: 24
  • Basım Tarihi: 2025
  • Doi Numarası: 10.1021/acsomega.5c02529
  • Dergi Adı: ACS Omega
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Directory of Open Access Journals
  • Sayfa Sayıları: ss.25951-25961
  • Süleyman Demirel Üniversitesi Adresli: Evet

Özet

The increasing presence of pharmaceutical contaminants, particularly amoxicillin (AMX), in aquatic environments necessitates the development of efficient and sustainable removal strategies. In this study, the SiNH2@FeNP nanocomposite was synthesized via a green synthesis approach using licorice (Glycyrrhiza glabra) root extract as a reducing and stabilizing agent. The synthesized nanocomposite was comprehensively characterized using Fourier transform infrared spectroscopy, X-ray diffraction, scanning electron microscopy (SEM), and Brunauer-Emmett-Teller surface area analysis to confirm its structural and morphological features. SEM imaging revealed significant morphological changes, demonstrating a uniform FeNP distribution on the SiNH2 surface. The adsorptive performance of the nanocomposite was optimized using the Box-Behnken experimental design, considering key operational parameters such as pH, initial AMX concentration, and adsorbent dosage. The statistical analysis validated a quadratic model as the best fit, achieving a maximum removal efficiency of 95% under optimized conditions (pH 5.6, AMX concentration 40.79 mg/L, and adsorbent dosage 1.85 g/L). These results demonstrate the high potential of SiNH2@FeNP as a low-cost, eco-friendly adsorbent for pharmaceutical removal from water, providing a scalable and sustainable alternative for wastewater treatment applications. This approach represents a novel combination of green nanotechnology and statistical optimization for AMX removal.