Nankai University

About: Nankai University is a education organization based out in Tianjin, China. It is known for research contribution in the topics: Catalysis & Adsorption. The organization has 42964 authors who have published 51866 publications receiving 1127896 citations. The organization is also known as: Nánkāi Dàxué.

Controlling the effective surface area and pore size distribution of sp2 carbon materials and their impact on the capacitance performance of these materials.

Abstract: A series of sp2 carbon materials with different specific surface area (SSA) and controlled pore size distribution (PSD) were synthesized at large scale through a facile and low-cost method. The SSA and PSD of these carbon materials were controlled by using different carbon sources and preparation methods. With different total and effective SSA (E-SSA) and PSD, the impacts on their capacitance performance were investigated thoroughly, which demonstrated that both E-SSA and PSD played the most important roles in their capacitance performance. Furthermore, theoretical modeling was performed, and the results are in agreement with the experimental results for the influence of E-SSA and PSD on their capacitance performance. Based on these, a general model using the slit/cylindrical NL-DFT approach is proposed for the estimation of the specific capacitance of sp2 carbon materials, which offers a simple but reliable method to predict the capacitance performance of these materials, thus speeding up the design and ...

MS-TCN++: Multi-Stage Temporal Convolutional Network for Action Segmentation.

Abstract: With the success of deep learning in classifying short trimmed videos, more attention has been focused on temporally segmenting and classifying activities in long untrimmed videos. State-of-the-art approaches for action segmentation utilize several layers of temporal convolution and temporal pooling. Despite the capabilities of these approaches in capturing temporal dependencies, their predictions suffer from over-segmentation errors. In this paper, we propose a multi-stage architecture for the temporal action segmentation task that overcomes the limitations of the previous approaches. The first stage generates an initial prediction that is refined by the next ones. In each stage we stack several layers of dilated temporal convolutions covering a large receptive field with few parameters. While this architecture already performs well, lower layers still suffer from a small receptive field. To address this limitation, we propose a dual dilated layer that combines both large and small receptive fields. We further decouple the design of the first stage from the refining stages to address the different requirements of these stages. Extensive evaluation shows the effectiveness of the proposed model in capturing long-range dependencies and recognizing action segments. Our models achieve state-of-the-art results on three datasets: 50Salads, Georgia Tech Egocentric Activities (GTEA), and the Breakfast dataset.

Synthesis and Electrochemical Performance of Sulfur/Highly Porous Carbon Composites

Abstract: Sulfur/highly porous carbon (HPC) composites were synthesized by thermally treating a mixture of sublimed sulfur and HPC. The microstructure of the HPC and the composite was characterized by transm...

Efficient and large-scale synthesis of few-layered graphene using an arc-discharge method and conductivity studies of the resulting films

Abstract: An arc-discharge method using a buffer gas containing carbon dioxide has been developed for the efficient and large-scale synthesis of few-layered graphene. The resulting samples of few-layered graphene, well-dispersed in organic solvents such as N,N-dimethylformamide (DMF) and 1,2-dichlorobenzene (o-DCB), were examined by transmission electron microscopy (TEM), X-ray diffraction (XRD), Raman spectroscopy, atomic force microscopy (AFM), and thermal gravimetric analysis (TGA). The electrical conductivity and transparency of flexible films prepared using a direct solution process have also been studied.