Preparation and Characterization of Waterborne Polyurethaneurea Composed of Dimer Fatty Acid Polyester Polyol

A series of polyurethaneurea (PUU) aqueous dispersions, which were stable at ambient temperature for more than 1 year, were prepared with C36-dimer-fatty-acid-based polyester polyol, isophorone diisocyanate, dimethylol propionic acid, and ethylenediamine. The particle size of all these PUU (DPU) aqueous dispersions (<100 nm) was less than that of comparable specimens, that is, poly-(neopentyl glycol adipate) polyester-polyol-based PUU (APU) aqueous dispersions, and the polydispersity index was very narrow (≤1.13). The films prepared with the DPU aqueous dispersions exhibited excellent waterproof performance, such as low amount of water absorption (1.3 wt%), and good mechanical properties (hardness and tensile strength), resulting from the strong hydrogen bonding in urea carbonyl groups and the perfect ordered structure of hard segments compared with those prepared with the APU aqueous dispersions. The surface hydrophobicity of the films prepared with modified DPU aqueous dispersions, which were modified with a fluorinated polyacrylate emulsion, was excellent, as the water contact angle on the surface of such films rose up to 100. The mechanical properties of such modified DPU films were further enhanced

Battle Against Fluctuating Quantum Noise: Compression-Aided Framework to Enable Robust Quantum Neural Network

Recently, we have been witnessing the scale-up of superconducting quantum computers; however, the noise of quantum bits (qubits) is still an obstacle for real-world applications to leveraging the power of quantum computing. Although there exist error mitigation or error-aware designs for quantum applications, the inherent fluctuation of noise (a.k.a., instability) can easily collapse the performance of error-aware designs. What's worse, users can even not be aware of the performance degradation caused by the change in noise. To address both issues, in this paper we use Quantum Neural Network (QNN) as a vehicle to present a novel compression-aided framework, namely QuCAD, which will adapt a trained QNN to fluctuating quantum noise. In addition, with the historical calibration (noise) data, our framework will build a model repository offline, which will significantly reduce the optimization time in the online adaption process. Emulation results on an earthquake detection dataset show that QuCAD can achieve 14.91% accuracy gain on average in 146 days over a noise-aware training approach. For the execution on a 7-qubit IBM quantum processor, IBM-Jakarta, QuCAD can consistently achieve 12.52% accuracy gain on earthquake detection