Cellulose fibre- perlite depth filters with cellulose nanofibre top coating for improved filtration performance

Aysu Onur, Kirubanandan Shanmugam, Aaron Ng, Gil Garnier, Warren Batchelor

Research output: Contribution to journalArticleResearchpeer-review

3 Citations (Scopus)

Abstract

Depth type composite filters are used to remove contaminants from liquids by combining mechanical entrapment and adsorption. Commonly, depth filters can adsorb either anionic or cationic charged contaminants, but not both, as well as filter micron-sized particles. Therefore, there is still a need to improve the existing depth filter performance. In this study, we developed a two-layer filters with enhanced functionality by spray coating an anionically charged cellulose nanofibre (CNF) layer onto a cationically charged depth filter layer. The depth filter layer was prepared via papermaking technique of vacuum filtration from wood pulp fibres, perlite adsorbent and cationic polyamide-amine epichlorohydrin (PAE) wet strength resin. The CNF coat weight ranged from 4 to 30 g per square metre (gsm). The two layer filters were proven to have triple functionality, capable of adsorbing anionic molecules in the cationic depth filter layer, while the anionic CNF layer could adsorb cationic molecules and was also a microfiltration membrane. A gradual increase in CNF coat weight improved filtration performance but decreased the filter flux. The optimum performance was found at an 8 gsm CNF coat weight, where the membrane layer rejected 80% of 5000 kDa molecular weight polyethylene glycol, at a flux of 133 Litres per square meter per hour (LMH) under 1.5 bar pressure, while still achieving a good breakthrough curve when absorbing cationic methylene blue dye.

Original languageEnglish
Article number123997
Number of pages8
JournalColloids and Surfaces A: Physicochemical and Engineering Aspects
Volume583
DOIs
Publication statusPublished - 20 Dec 2019

Keywords

  • Adsorption
  • Cellulose nanofibre
  • Depth filters
  • Filtration
  • Spray coating

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