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The formation of fungus-serpentine aggregation and its immobilization of lead(II) under acidic conditions.

Chengfeng YuLuting ZhangShameer SyedYing LiMin XuBin Lian
Published in: Applied microbiology and biotechnology (2021)
Serpentine has weak immobilization capacity for Pb(II), especially under acidic conditions. In order to improve its application potential, a new biological modification method was adopted, i.e., the serpentine powder was weathered by Aspergillus niger and the fungus-serpentine aggregation (FSA) formed was investigated for its Pb(II) immobilization potential and underlying mechanism. Batch adsorption of Pb(II) by FSA closely followed the Langmuir model, while the maximum adsorption capacity of FSA (370.37 mg/g) was significantly higher than fungal mycelium (31.85 mg/g) and serpentine (8.92 mg/g). The adsorption process can be accurately simulated by pseudo-second-order kinetic model. Our data revealed the loading of organic matter is closely related to the adsorption of FSA, and the stronger immobilization capacity was mainly related to its modified porous organic-inorganic composite structure with extensive exchangeable ions. Moreover, FSA is an economical bio-material with excellent Pb(II) adsorption (pH = 1-8) along with significantly lower desorption efficiency (pH = 3-8), especially under acidic conditions. These findings provide a new perspective to explore the usage of fungus-minerals aggregation on heavy metals immobilization in acidic environments. Key Points • Co-culture of Aspergillus niger and serpentine produced a porous composite material like fungus-serpentine aggregation. • Fungus-serpentine aggregation has a surprisingly higher adsorption capacity of Pb(II) and significantly lower desorption efficiency under acidic conditions. • The loading of organic matter is closely related to the adsorption of FSA.
Keyphrases
  • aqueous solution
  • heavy metals
  • organic matter
  • ionic liquid
  • risk assessment
  • health risk
  • electronic health record
  • single cell
  • big data
  • atomic force microscopy
  • deep learning
  • cell wall
  • high speed