Boosting thermal energy transport across the interface between phase change materials and metals via self-assembled monolayers.

Thermal energy storage using phase change materials (PCMs) has great potential to reduce the weather dependency of sustainable energy sources. However, the low thermal conductivity of most PCMs is a long-standing bottleneck for large-scale practical applications. In modifications to increase the thermal conductivity of PCMs, the interfacial thermal resistance (ITR) between PCMs and discrete additives or porous networks reduces the effective thermal energy transport. In this work, we investigated the ITR between a metal (gold) and a polyol solid-liquid PCM (erythritol) at various temperatures including temperatures below the melting point (300 and 350 K), near the melting point (390, 400, 410 K, etc) and above the melting point (450 and 500 K) adopting non-equilibrium molecular dynamics. Since the gold-erythritol interfacial thermal conductance (ITC) is low regardless of whether erythritol is melted or not (<40 MW m-2K-1), self-assembled monolayers (SAMs) were used to boost the interfacial thermal energy transport. The SAM with carboxyl groups was found to increase the ITC most (by a factor of 7-9). As the temperature increases, the ITC significantly increases (by ∼50 MW m-2K-1) below the melting point but decreases little above the melting point. Further analysis revealed that the most obvious influencing factor is the interfacial binding energy. This work could build on existing composite PCM solutions to further improve heat transfer efficiency of energy storage applications in both liquid and solid states.

GPA-Net:No-Reference Point Cloud Quality Assessment with Multi-task Graph Convolutional Network.

With the rapid development of 3D vision, point cloud has become an increasingly popular 3D visual media content. Due to the irregular structure, point cloud has posed novel challenges to the related research, such as compression, transmission, rendering and quality assessment. In these latest researches, point cloud quality assessment (PCQA) has attracted wide attention due to its significant role in guiding practical applications, especially in many cases where the reference point cloud is unavailable. However, current no-reference metrics which based on prevalent deep neural network have apparent disadvantages. For example, to adapt to the irregular structure of point cloud, they require preprocessing such as voxelization and projection that introduce extra distortions, and the applied grid-kernel networks, such as Convolutional Neural Networks, fail to extract effective distortion-related features. Besides, they rarely consider the various distortion patterns and the philosophy that PCQA should exhibit shift, scaling, and rotation invariance. In this paper, we propose a novel no-reference PCQA metric named the Graph convolutional PCQA network (GPA-Net). To extract effective features for PCQA, we propose a new graph convolution kernel, i.e., GPAConv, which attentively captures the perturbation of structure and texture. Then, we propose the multi-task framework consisting of one main task (quality regression) and two auxiliary tasks (distortion type and degree predictions). Finally, we propose a coordinate normalization module to stabilize the results of GPAConv under shift, scale and rotation transformations. Experimental results on two independent databases show that GPA-Net achieves the best performance compared to the state-of-the-art no-reference PCQA metrics, even better than some full-reference metrics in some cases. The code is available at: https://github.com/Slowhander/GPA-Net.git.