Heterogeneous sono-Fenton-like process using martite nanocatalyst prepared by high energy planetary ball milling for treatment of a textile dye


Dindarsafa M., Khataee A., Kaymak B., Vahid B., Karimi A., Rahmani A.

ULTRASONICS SONOCHEMISTRY, cilt.34, ss.389-399, 2017 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 34
  • Basım Tarihi: 2017
  • Doi Numarası: 10.1016/j.ultsonch.2016.06.016
  • Dergi Adı: ULTRASONICS SONOCHEMISTRY
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.389-399
  • Anahtar Kelimeler: Nanoparticles, Martite, Heterogeneous sono-Fenton-like process, Ball milling, Artificial neural network, ARTIFICIAL NEURAL-NETWORKS, GLOW-DISCHARGE PLASMA, WASTE-WATER, PHOTO-FENTON, SONOCATALYTIC DEGRADATION, CATALYTIC OZONATION, NATURAL PYRITE, MAGNETITE, OXIDATION, REMOVAL
  • Orta Doğu Teknik Üniversitesi Adresli: Evet

Özet

High energy planetary ball milling was applied to prepare sono-Fenton nanocatalyst from natural martite (NM). The NM samples were milled for 2-6 hat the speed of 320 rpm for production of various ball milled martite (BMM) samples. The catalytic performance of the BMMs was greater than the NM for treatment of Acid Blue 92 (AB92) in heterogeneous Sono: Fenton-like process. The NM and the BMM samples were characterized by XRD, FT-IR, SEM, EDX and BET analyses. The particle size distribution of the 6 h-milled martite (BMM3) was in the range of 10-90 nm, which had the highest surface area compared to the other samples. Then, the impact of main operational parameters was investigated on the process. Complete removal of the dye was obtained at the desired conditions including initial pH 7, 2.5 g/L BMM3 dosage, 10 mg/L AB92 concentration, and 150 W ultrasonic power after 30 min of treatment. The treatment process followed pseudo-first order kinetic. Environmentally-friendly modification of the NM, low leached iron amount and repeated application at milder pH were the significant benefits of the BMM3. The GC-MS was successfully used to identify the generated intermediates. Eventually, an artificial neural network (ANN) was applied to predict the AB92 removal efficiency based upon the experimental data with a proper correlation coefficient (R-2 = 0.9836). (C) 2016 Elsevier B.V. All rights reserved.