Engineered multi-scale roughness of carbon nanofiller-embedded 3D printed spacers for membrane distillation

Abstract

Membrane distillation (MD) transfers heat and mass simultaneously through a hydrophobic membrane. Hence, it is sensitive to both concentration and temperature polarisation (CP and TP) effects. In this study, we fabricated feed spacers to improve MD efficiency by alleviating the polarisation effects. First, a 3D printed spacer design was optimised to show superior performance amongst the others tested. Then, to further enhance spacer performance, we incorporated highly thermally stable carbon nanofillers, including carbon nanotubes (CNT) and graphene, in the fabrication of filaments for 3D printing. All the fabricated spacers had a degree of engineered multi-scale roughness, which was relatively high compared to that of the polylactic acid (PLA) spacer (control). The use of nanomaterial-incorporated spacers increased the mean permeate flux significantly compared to the PLA spacer (27.1 L/m²h (LMH)): a 43% and 75% increase when using the 1% graphene-incorporated spacer (38.9 LMH) and 2% CNT incorporated spacer (47.5 LMH), respectively. This could be attributed to the locally enhanced turbulence owing to the multi-scale roughness formed on the spacer, which further increased the vaporisation rate through the membrane. Interestingly, only the CNT-embedded spacer markedly reduced the ion permeation through the membrane, which may be due to the effective reduction of CP. This further decreased with increasing CNT concentration, confirming that the CNT spacer can simultaneously reduce the CP and TP effects in the MD process. Finally, we successfully proved that the multi-scale roughness of the spacer surface induces micromixing near the membrane walls, which can improve the MD performance via computational fluid dynamics.

Keywords: 3D printing; Computational fluid dynamics; Membrane distillation; Roughness; Spacer.