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

Jamaliah Aburabie, Haya Nassrullah, Raed Hashaikeh

Research output: Contribution to journalArticlepeer-review

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 Na2SO4 are studied/optimized in reference to fabrication parameters. 90% Na2SO4 rejection (7 LMH) is achieved for silane-CaAlg200-10% CNS membranes. Membranes exhibit outstanding electrical conductivity (∼2858 S m−1), 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% Na2SO4, and 54% Na2SO4 rejection in the second stage.

Original languageEnglish (US)
Article number136907
JournalChemosphere
Volume310
DOIs
StatePublished - Jan 2023

Keywords

  • Alginate
  • Electrically-conductive
  • Nanofiltration
  • Organosilicone precursor

ASJC Scopus subject areas

  • Environmental Engineering
  • Environmental Chemistry
  • General Chemistry
  • Pollution
  • Public Health, Environmental and Occupational Health
  • Health, Toxicology and Mutagenesis

Fingerprint

Dive into the research topics of 'Fine-tuning of carbon nanostructures/alginate nanofiltration performance: Towards electrically-conductive and self-cleaning properties'. Together they form a unique fingerprint.

Cite this