A series of self-matting waterborne polyurethanes (WPUs) were successfully prepared by introducing hydrophilic units into both soft and hard segments. By employing a polycaprolactone polyol containing carboxylate groups within the polymer chains to provide hydrophilicity directly, the matting performance of WPU films was greatly improved. The chemical structures of the WPU resins were confirmed by FTIR spectroscopy, and the morphology of WPU films was observed by SEM. The parameters of WPU preparation were investigated in detail. It was found that the surface gloss of WPU films as well as the particle sizes of WPU dispersions were closely associated with the content of hydrophilic units. As the content of carboxylates or sulfonates increased, the particle sizes of WPU decreased, while the gloss increased gradually. When the particle sizes of dispersions were greater than 3 μm, the gloss of WPU films coated on a leather surface was lower than 1. The results of TG showed that, the initial decomposition temperatures of WPU films were higher than 280 °C, which indicated these films also had good thermal stability. The prepared self-matting WPU coatings would have potential application prospects in the field of leather finishing.
As the core component of proton exchange membrane fuel cell, proton exchange membranes (PEM) have attracted much attention of researchers. To trade-off the proton conduction, dimensional stability and anti-oxidation ability, graphene oxide (GO) and acidized multi-walled carbon nanotubes (MWCNT) using calcium ion as coordination bridge (GO-Ca 2+ -MWCNT) was synthesized, and then incorporating into sulfonated poly(arylene ether nitrile) (SPEN) to fabricate SPEN/GO-Ca 2+ -MWCNT organic-inorganic composite membranes by solution-casting method and explore the in uence of varying loading on performances as PEM. It was found that the proton conductivity of the composite membranes was higher than that of SPEN, while maintaining better dimensional stability, excellent anti-oxidation ability and good mechanical properties. All of these were attributed to the formation of three-dimensional structure between GO and MWCNT bridged by Ca 2+ . Particularly, the SPEN/GO-Ca 2+ -MWCNT-1 composite membrane exhibited excellent tensile strength of 71.45 MPa, better thermal stability as well as high proton conductivity (0.054 S/cm at 30 ℃, and 0.193 S/cm at 90 ℃), above 10 -2 S/cm, satisfying the requirement of fuel cells. All in all, the results indicate that the ller with three-dimensional network structure can effectively improve the performances of SPEN, and the prepared composite membranes show potential applications in many elds.
As the core component of proton exchange membrane fuel cell, proton exchange membranes (PEM) have attracted much attention of researchers. To trade-off the proton conduction, dimensional stability and anti-oxidation ability, graphene oxide (GO) and acidized multi-walled carbon nanotubes (MWCNT) using calcium ion as coordination bridge (GO-Ca2+-MWCNT) was synthesized, and then incorporating into sulfonated poly(arylene ether nitrile) (SPEN) to fabricate SPEN/GO-Ca2+-MWCNT organic-inorganic composite membranes by solution-casting method and explore the influence of varying loading on performances as PEM. It was found that the proton conductivity of the composite membranes was higher than that of SPEN, while maintaining better dimensional stability, excellent anti-oxidation ability and good mechanical properties. All of these were attributed to the formation of three-dimensional structure between GO and MWCNT bridged by Ca2+. Particularly, the SPEN/GO-Ca2+-MWCNT-1 composite membrane exhibited excellent tensile strength of 71.45 MPa, better thermal stability as well as high proton conductivity (0.054 S/cm at 30 ℃, and 0.193 S/cm at 90 ℃), above 10-2 S/cm, satisfying the requirement of fuel cells. All in all, the results indicate that the filler with three-dimensional network structure can effectively improve the performances of SPEN, and the prepared composite membranes show potential applications in many fields.
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