The homopolyester of isosorbide and cis–trans 1,4‐cyclohexane dicarboxylic acid (CHDA) was prepared by three different methods, but only polycondensation of isosorbide and CHDA dichloride yielded a satisfactory molecular weight (corrected $\overline M _{\rm n} $ = 11 000 Da). For the best sample the MALDI‐TOF mass spectrum revealed a high content of cycles. The homopolyester of CHDA and isomannide or isoidide was prepared analogously. For the homopolyesters of CHDA high glass transition temperatures were found (Tg = 146 °C for isosorbide, 133 °C for isomannide, and 115 °C for isoidide), whereas the polyester of isosorbide and succinic acid (SuA) has a Tg around 77 °C. Copolyester of isosorbide and various molar ratios of CHDA and SuA were prepared by two different methods, but only rather low molecular weights were obtained. SEC measurements with and without “universal calibration” revealed that the normal calibration with polystyrene overestimates the real molecular weights by 30–45%.magnified image
A new approach to hybrid model network formation based upon heterocomplementary end‐linking of four‐arm star poly‐ε‐caprolactone (PCL) and linear polypropylene glycol (PPG) precursors is demonstrated. Specifically, hydroxy‐terminated PCL(OH)4 and an amino‐terminated linear PPG(NH2)2 are reacted with a bifunctional coupling agent containing one carboxylic acid chloride group and one oxazinone group. PCL(OH)4 is first reacted with the former in a solution, and the so‐obtained oxazinone‐terminated intermediate is then reacted with PPG(NH2)2 to form a network both in the solution and in the melt. A strong effect of electron‐withdrawing groups on the reactivity of the oxazinone group, and thus on the network formation, is evidenced. Network structure and properties are studied by swelling experiments and low‐field multiple‐quantum (MQ) NMR, which confirm the successful formation of hybrid networks and provide information on the significant network inhomogeneities. On the methodological side, a reliable approach to MQ NMR data analysis for networks of variable degree of inhomogeneity is discussed.
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