Anionic nylon 6 modified with poly(oxypropylene) up to 40% by weight and obtained by one-shot reaction injection molding ("one-shot" RIM) was characterized by wide-angle X-ray diffraction, transmission electron microscopy, differential scanning calorimetry, thermal gravimetric analysis, and Fourier transform infrared spectroscopy. Cross analysis by different techniques pointed out that the polymer contained only the a-form and the modifier had little, if any, influence on the crystallinity index, which was found to be about 40% when referred to the nylon 6 fraction only. Morphological changes at different modifier contents were ascribed to different block copolymer structures.
We focus on gas sorption within metal-organic frameworks (MOFs) for energy applications and identify the minimal set of crystallographic descriptors underpinning the most important properties of MOFs for CO2 and H2O. A comprehensive comparison of several sequential learning algorithms for MOFs properties optimization is performed and the role played by those descriptors is clarified. In energy transformations, thermodynamic limits of important figures of merit crucially depend on equilibrium properties in a wide range of sorbate coverage values, which is often only partially accessible, hence possibly preventing the computation of desired objective functions. We propose a fast procedure for optimizing specific energy in a closed sorption energy storage system with only access to a single water Henry coefficient value and to the specific surface area. We are thus able to identify hypothetical candidate MOFs that are predicted to outperform state-of-the-art water-sorbent pairs for thermal energy storage applications.
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