Improving ultrafiltration membrane performance with pre-deposited carbon nanotubes/nanofibers layers for drinking water treatment

2019 
Abstract To efficiently improve the performance of ultrafiltration (UF) membrane for drinking water treatment, carbon nanotubes (CNTs) and carbon nanofibers (CNFs) were utilized as pre-deposited coating layers on membrane surface. A comparative study between these two carbon nanomaterials for enhancing pollutants removal and mitigating membrane fouling induced by natural organic matter (NOM) was carried out. The surface morphologies were characterized by scanning electron microscopy, and the results indicated that the CNTs coating layer was more dense and homogeneous with a smaller pore size than that of CNFs. The removal and antifouling performance of CNTs/CNFs coated membranes were investigated with typical NOM, i.e., humic acid, bovine serum albumin, sodium alginate, as well as natural surface water. The results showed that the presence of coating layers was very effective to improve the rejection rate of NOM, among which CNTs exhibited significant better performance than CNFs. The fouling control performance was influenced by the NOM fraction and coating mass (6–50 g/m 2 ). Generally, CNTs coating layer was more efficient in alleviating both reversible and irreversible membrane fouling, while CNFs exhibited limited effect on irreversible fouling control. Both pre-adsorption and size exclusion contributed to the rejection of membrane foulants, thus reducing the organics directly contacted with the underlying membrane. In natural surface water treatment, the pre-deposited coating layers significantly delayed the transition of fouling mechanisms from pore blocking to cake filtration. The experimental results were expected to illustrate the feasibility of pre-deposited CNTs/CNFs layers for enhancing membrane performance during drinking water treatment.
    • Correction
    • Source
    • Cite
    • Save
    • Machine Reading By IdeaReader
    51
    References
    24
    Citations
    NaN
    KQI
    []