A Study of the Reinforcement Effect of MWCNTs onto Polyimide Flat Sheet Membranes

Polyimides rank among the most heat-resistant polymers and find application in a variety of fields, including transportation, electronics, and membrane technology. The aim of this work is to study the structural, thermal, mechanical, and gas permeation properties of polyimide based nanocomposite membranes in flat sheet configuration. For this purpose, numerous advanced techniques such as atomic force microscopy (AFM), SEM, TEM, TGA, FT-IR, tensile strength, elongation test, and gas permeability measurements were carried out. In particular, BTDA–TDI/MDI (P84) co-polyimide was used as the matrix of the studied membranes, whereas multi-wall carbon nanotubes were employed as filler material at concentrations of up to 5 wt.% All studied films were prepared by the dry-cast process resulting in non-porous films of about 30–50 μm of thickness. An optimum filler concentration of 2 wt.% was estimated. At this concentration, both thermal and mechanical properties of the prepared membranes were improved, and the highest gas permeability values were also obtained. Finally, gas permeability experiments were carried out at 25, 50, and 100 ◦C with seven different pure gases. The results revealed that the uniform carbon nanotubes dispersion lead to enhanced gas permeation properties

Effects of carbon nanotubes on the mechanical strength of self-polishing antifouling paints

Antifouling (AF) paints are of great importance for marine vehicles in preventing biofouling. After the banning of tributyltin-based paints because of their genotoxic effects on non-target marine organisms, research on the development of eco-friendly antifouling paints has been accelerated. The mechanical strength of antifouling paints is also an important issue for the service life of coatings. The effect of carbon nanotubes (CNTs) on the mechanical strength of a self-polishing antifouling paint was investigated in the present study. The experimental data were also modelled using an artificial neural network (ANN). In conclusion, an optimum amount of CNT leads to an increase in the mechanical strength of self-polishing antifouling paints. It was also found that ANN is a useful tool for modelling antifouling paint compositions. (C) 2016 Elsevier B.V. All rights reserved