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Polype ntafluoropropane glycidyl ether (PPFEE), a new random block hydroxyl-terminated polyfluoroether, was synthesized successfully by cationic ring-opening polymerization of 2-(2,2,3,3,3-pentafluoropropoxymethyl) oxirane, and its molecular structure was confirmed by Fourier transform infrared spectroscopy, nuclear magnetic resonance spectrometry, and gel permeation chromatography. The PPFEE-based polyurethane elastomers featuring fluorine in their side chains were prepared using PPFEE as soft segments, polyisocyanate polyaryl polymethylene isocyanate as hard segments, and dibutyltin dilaurate as catalysts under different curing conditions. The microphase separation, mechanical performance, and thermal behavior of the elastomers were investigated by differential scanning calorimetry, uniaxial tensile test, and thermal gravimetric analysis, respectively. Based on the results, the percentage of hard segments dissolved into the soft segments of elastomers was opposite to the change in breaking strength. The PPFEE-based polyurethane elastomer cured with 20 wt% PAPI at the curing temperature of 50 °C displayed the maximum tensile elongation of 2.26 MPa with an elongation at break of nearly 150%. The increased contents of PAPI can effectively strengthen the tensile strength, and the maximum tensile elongation was 3.04 MPa with an elongation at break of nearly 90% when the content of PAPI was 26 wt%. In addition, the PPFEE-based polyurethane elastomers exhibited excellent resistance to thermal decomposition and a sharp weight loss temperature at around 371 °C. All the results demonstrated that the PPFEE may be a potential polymeric binder as one of the ingredients applied to future propellant formulations. [ABSTRACT FROM AUTHOR]

In unsupervised learning, clustering is a common starting point for data processing. The convex or concave fusion clustering method is a novel approach that is more stable and accurate than traditional methods such as k-means and hierarchical clustering. However, the optimization algorithm used with this method can be slowed down significantly by the complexity of the fusion penalty, which increases the computational burden. This paper introduces a random projection ADMM algorithm based on the Bernoulli distribution and develops a double random projection ADMM method for high-dimensional fusion clustering. These new approaches significantly outperform the classical ADMM algorithm due to their ability to significantly increase computational speed by reducing complexity and improving clustering accuracy by using multiple random projections under a new evaluation criterion. We also demonstrate the convergence of our new algorithm and test its performance on both simulated and real data examples. [ABSTRACT FROM AUTHOR]