摘要
SAPO-34 nanocrystals(inorganic filler) were incorporated in polyurethane membranes and the permeation properties of CO_2, CH_4,and N_2 gases were explored. In this regard, the synthesized PU-SAPO-34 mixed matrix membranes(MMMs) were characterized via SEM, AFM, TGA, XRD and FTIR analyses. Gas permeation properties of PU-SAPO-34 MMMs with SAPO-34 contents of 5 wt%, 10 wt% and 20 wt% were investigated. The permeation results revealed that the presence of 20 wt% SAPO-34 resulted in 4.45%, 18.24% and 40.2% reductions in permeability of CO_2,CH_4,and N_2, respectively, as compared to the permeability of neat polyurethane membrane. Also,the findings showed that at the pressure of 1.2 MPa, the incorporation of 20 wt% SAPO-34 into the polyurethane membranes enhanced the selectivity of CO_2/CH_4 and CO_2/N_2, 14.43 and 37.46%, respectively. In this research, PU containing 20 wt% SAPO-34 showed the best separation performance. For the first time, polynomial regression(PR) as a simple yet accurate tool yielded a mathematical equation for the prediction of permeabilities with high accuracy(R^2>99%).
SAPO-34 nanocrystals(inorganic filler) were incorporated in polyurethane membranes and the permeation properties of CO_2, CH_4,and N_2 gases were explored. In this regard, the synthesized PU-SAPO-34 mixed matrix membranes(MMMs) were characterized via SEM, AFM, TGA, XRD and FTIR analyses. Gas permeation properties of PU-SAPO-34 MMMs with SAPO-34 contents of 5 wt%, 10 wt% and 20 wt% were investigated. The permeation results revealed that the presence of 20 wt% SAPO-34 resulted in 4.45%, 18.24% and 40.2% reductions in permeability of CO_2,CH_4,and N_2, respectively, as compared to the permeability of neat polyurethane membrane. Also,the findings showed that at the pressure of 1.2 MPa, the incorporation of 20 wt% SAPO-34 into the polyurethane membranes enhanced the selectivity of CO_2/CH_4 and CO_2/N_2, 14.43 and 37.46%, respectively. In this research, PU containing 20 wt% SAPO-34 showed the best separation performance. For the first time, polynomial regression(PR) as a simple yet accurate tool yielded a mathematical equation for the prediction of permeabilities with high accuracy(R^2>99%).
基金
Supported by the University of Kashan and the nano-organization(1393/1752)
