Fine-tuning of carbon nanostructures/alginate nanofiltration performance: Towards electrically-conductive and self-cleaning properties.

Chemosphere

NYUAD Water Research Center, Engineering Division, New York University Abu Dhabi, Abu Dhabi, P.O. Box 129188, United Arab Emirates. Electronic address:

Published: January 2023

AI Article Synopsis

  • Electrically-conductive membranes utilizing carbon nanostructures (CNS) show improved ion selectivity and self-cleaning capabilities, enhancing their filtration efficiency.
  • A water-based method was developed to integrate CNS into alginate networks, resulting in membranes with high electrical conductivity (∼2858 S m) and effective swelling/shrinkage resistance.
  • The membranes achieved notable filtration results, including 90% rejection of sodium sulfate (NaSO) and successful dye/salt separation in a two-stage process.

Article Abstract

Electrically-conductive membranes became the center of attention owing to their enhanced ion selectivity and self-cleaning properties. Carbon nanostructures (CNS) attain high electrical conductivity, and fast water transport. Herein, we adopt a water-based, simple method to entrap CNS within Alginate network to fabricate self-cleaning nanofiltration membranes. CNS are embedded into membranes to improve the swelling/shrinkage resistivity, and to achieve electrical-conductivity. The CaAlg PEG-formed pores are tuned by organic-inorganic network via silane crosslinking. Flux/rejection profiles of NaSO are studied/optimized in reference to fabrication parameters. 90% NaSO rejection (7 LMH) is achieved for silane-CaAlg-10% CNS membranes. Membranes exhibit outstanding electrical conductivity (∼2858 S m), which is attractive for fouling control. CaAlg/CNS membranes are tested to treat dye/saline water via two-stage filtration, namely, dye/salt separation and desalination. A successful dye/salt separation is achieved at the first stage with a rejection of 100%-RB and only 3.1% NaSO, and 54% NaSO rejection in the second stage.

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Source
http://dx.doi.org/10.1016/j.chemosphere.2022.136907DOI Listing

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