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Polyurea elastomers are utilized for a myriad of applications ranging from coatings and foams to dielectric materials for capacitors and actuators. However, current synthetic methods for polyureas rely on highly reactive isocyanates, solvents, and catalysts, which collectively pose serious safety considerations. This report details the synthesis and characterization of melt processible, poly(tetramethylene oxide) (PTMO)-based segmented polyurea elastomers utilizing an isocyanate-, solvent-, and catalyst-free approach. Dynamic mechanical analysis and differential scanning calorimetry suggested microphase separation between the hard and soft segments. Tensile analysis revealed high strain at break for all segmented copolymers between 340 and 770%, and tunable modulus between 0.76 and 29.5 MPa. Dielectric spectroscopy revealed that the composition containing 20 wt% hard segment offered the highest permittivity at 10.6 (1 kHz, 300 K) of the segmented copolymers, indicating potential as a dielectric elastomer. 

Abstract—Numerical simulation of weather is resolution-constrained due to the high computational cost of integrating the coupled PDEs that govern atmospheric motion. For example, the most highly-resolved numerical weather prediction models are limited to approximately 3 km. However many weather and climate impacts occur over much finer scales, especially in urban areas and regions with high topographic complexity like mountains or coastal regions. Thus several statistical methods have been developed in the climate community to downscale numerical model output to finer resolutions. This is conceptually similar to image super-resolution (SR) [1] and in this work we report the results of applying SR methods to the downscaling problem. In particular we test the extent to which a SR method based on a Generative Adversarial Network (GAN) can recover a grid of wind speed from an artificially downsampled version, compared against a standard bicubic upsampling approach and another machine learning based approach, SR-CNN [1]. We use ESRGAN ([2]) to learn to downscale wind speeds by a factor of 4 from a coarse grid. We find that we can recover spatial details with higher fidelity than bicubic upsampling or SR-CNN. The bicubic and SR-CNN methods perform better than ESRGAN on coarse metrics such as MSE. However, the high frequency power spectrum is captured remarkably well by the ESRGAN, virtually identical to the real data, while bicubic and SR-CNN fidelity drops significantly at high frequency. This indicates that SR is considerably better at matching the higher-order statistics of the dataset, consistent with the observation that the generated images are of superior visual quality compared with SR-CNN. 

We characterize the properties of Ce1−xYbxRhIn5 single crystals with 0  x  1 using measurements of powder x-ray diffraction, energy dispersive x-ray spectroscopy, electrical resistivity, magnetic susceptibility, specific heat, x-ray absorption near edge structure (XANES), and neutron diffraction. The Yb valence vYb, calculated from the magnetic susceptibility and measured using XANES, decreases from 3+ at x = 0 to ∼2.1+ at xact = 0.2, where xact is the measured Yb concentration. A transition from incommensurate to commensurate antiferromagnetism is observed in neutron diffraction measurements along Q = (0.5, 0.5, l) between 0.2  xact  0.27; this narrative is supported by specific-heat measurements in which a second robust feature appears at a temperature TI (TI < TN) for the same concentration range. Magnetic susceptibility measurements also reveal features which provide additional evidence of magnetic ordering. The results of this study suggest that the evolution of the Yb valence plays a critical role in tuning the magnetic ground state of Ce1−xYbxRhIn5.