Long-Service-Life Rigid Polyurethane Foam Fillings for Spent Fuel Transportation Casks.

Soft materials bearing rigid, lightweight, and vibration-dampening properties offer distinct advantages over traditional wooden and metal-based fillings for spent fuel transport casks, due to their low density, tunable structure, excellent mechanical properties, and ease of processing. In this study, a novel type of rigid polyurethane foam is prepared using a conventional polycondensation reaction between isocyanate and hydroxy groups. Moreover, the density and size of the pores in these foams are precisely controlled through simultaneous gas generation. The as-prepared polyurethane exhibits high thermal stability exceeding 185 °C. Lifetime predictions based on thermal testing indicate that these polyurethane foams could last up to over 60 years, which is double the lifetime of conventional materials of about 30 years. Due to their occlusive structure, the mechanical properties of these polymeric materials meet the design standards for spent fuel transport casks, with maximum compression and tensile stresses of 6.89 and 1.37 MPa, respectively, at a testing temperature of -40 °C. In addition, these polymers exhibit effective flame retardancy; combustion ceased within 2 s after removal of the ignition source. All in all, this study provides a simple strategy for preparing rigid polymeric foams, presenting them as promising prospects for application in spent fuel transport casks.

DeepLogic: Joint Learning of Neural Perception and Logical Reasoning.

Neural-symbolic learning, aiming to combine the perceiving power of neural perception and the reasoning power of symbolic logic together, has drawn increasing research attention. However, existing works simply cascade the two components together and optimize them isolatedly, failing to utilize the mutual enhancing information between them. To address this problem, we propose DeepLogic, a framework with joint learning of neural perception and logical reasoning, such that these two components are jointly optimized through mutual supervision signals. In particular, the proposed DeepLogic framework contains a deep-logic module that is capable of representing complex first-order-logic formulas in a tree structure with basic logic operators. We then theoretically quantify the mutual supervision signals and propose the deep&logic optimization algorithm for joint optimization. We further prove the convergence of DeepLogic and conduct extensive experiments on model performance, convergence, and generalization, as well as its extension to the continuous domain. The experimental results show that through jointly learning both perceptual ability and logic formulas in a weakly supervised manner, our proposed DeepLogic framework can significantly outperform DNN-based baselines by a great margin and beat other strong baselines without out-of-box tools.

[Raman Spectra Study on the Structure and Thermal Stability of Vacuum Annealed Cr-DLC Films Prepared with UBMS].

Chromium doped amorphous hydrogenated carbon a-C∶H(Cr) films (2.23 µm in thickness)have been successfully prepared with unbalanced magnetron sputtering technology(UBMS) in this paper. Raman spectrum and X-ray photoelectron spectroscopy (XPS) have been used to study the structure and thermal stability of the Chromium doped diamond-like carbon films (Cr-DLC). The results indicate that, at the room temperature, the Raman absorption peak "G" near 1 580 cm-1was assigned to C­C stretching vibration mode (E2g) in the Cr-DLC films, while the peak "D" near 1 350 cm-1was assigned to sp2 breathing vibration mode (A1g). The relative concentration of sp3 bond in the film is at 48%. When heated till 300 ℃, the Raman pattern of sample is similar to that of the sample at room temperature, indicating the structure of Cr-DLC film is stable. At 400 ℃, the ratio of ID/IG and the concentration of sp2 bond clearly increase, indicating the change of the structure in the Cr-DLC and the appearance of graphitization. With temperature going up, the rise of ID/IG ratio and the shift of the peak "G" to high wavenumber show that the rise of sp2/sp3 ratio, the intensity of graphitization, and the decline of the disorder of sp2 in the Cr-DLC films lead to the increase of the friction coefficient and wearing rate and the decline of the Cr-DLC thermal stability. When the it is up to 600 ℃, the ratio of ID/IG and the concentration of sp2 bond reach the max value while the Cr-DLC film loses its efficacy.