Abstract
Proton conductivity is determining parameter of performance for proton exchange membrane cells in energy systems. The presence of sulfonated and fluorinated groups in polymer and using theoretical techniques make it possible to design a high-performance membrane with optimized conductivity. Thus, proton conductivity of 19 membranes of sulfonated/fluorinated polysulfone case studies are evaluated using artificial neural network (ANN). Fluorinated regions are incorporated into sulfonated polymer via synthesis of sulfonated/partially fluorinated polymers, blending sulfonated and fluorinated polymers, and electrospun fluorinated mat impregnated with a sulfonated matrix. Sulfonation degree (WR_S), content of fluorinated groups (WR_F), and membrane cost are studied as influential factors on conductivity. According to ANN, there is a direct relationship between WR_S and proton conductivity. Regarding WR_F, the optimal value should be allocated. Membrane cost including the effect of diol structure, WR_S, and WR_F has the highest impact. Additionally, at high WR_S, proton conductivity is determined by WR_S and WR_F.
Published Version
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