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α-Fe2O3 Nanodisk/Bacterial Cellulose Hybrid Membranes as High-Performance Sulfate-Radical-Based Visible Light Photocatalysts under Stirring/Flowing States.

Zhong-Shuai ZhuJin QuShu-Meng HaoShuang HanKun-Le JiaZhong-Zhen Yu
Published in: ACS applied materials & interfaces (2018)
High activity and long-term stability are particularly important for peroxymonosulfate (PMS)-based degradation processes in wastewater treatment, especially under a flowing state. However, if the highly active nanomaterials are in a powder form, they could disperse well in water but would not be convenient for application under varied flow rates. A metal oxide/bacterial cellulose hybrid membrane fixed in a flowing bed is expected to solve these problems. Herein, α-Fe2O3 nanodisk/bacterial cellulose hybrid membranes as high-performance sulfate-radical-based visible light photocatalysts are synthesized for the first time. The bacterial cellulose with excellent mechanical stability and film-forming feature not only benefits the formation of a stable membrane to avoid the separation and recycling problems but also helps disperse and accommodate α-Fe2O3 nanodisks and thus enhances the visible light absorption performances, leading to an excellent PMS-based visible light degradation efficiency under both stirring and flowing states. Particularly, the optimized hybrid membrane photocatalyzes both cationic and anionic organic dyes under a flowing bed state for at least 84 h with the catalytic efficiency up to 100% and can be easily separated after the reaction, confirming its remarkable catalytic performance and long-term stability. Even under varied flow rates during the continuous process, it efficiently degrades rhodamine B and orange II from 3 to 16 mL h-1. When the flow rate goes back from high to low, the hybrid membrane quickly recovers its original performance, demonstrating the high activity and stability of the α-Fe2O3/bacterial cellulose membrane.
Keyphrases
  • visible light
  • wastewater treatment
  • ionic liquid
  • aqueous solution
  • mental health
  • silver nanoparticles
  • deep learning
  • mass spectrometry