Nanjing University of Science and Technology

About: Nanjing University of Science and Technology is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Computer science. The organization has 31581 authors who have published 36390 publications receiving 525474 citations. The organization is also known as: Nánjīng Lǐgōng Dàxué & Nánlǐgōng.

Large‑Scale Synthesis of MOF‑Derived Superporous Carbon Aerogels with Extraordinary Adsorption Capacity for Organic Solvents

Abstract: Carbon aerogels (CAs) with 3D interconnected networks hold promise for application in areas such as pollutant treatment, energy storage, and electrocatalysis. In spite of this, it remains challenging to synthesize high-performance CAs on a large scale in a simple and sustainable manner. We report an eco-friendly method for the scalable synthesis of ultralight and superporous CAs by using cheap and widely available agarose (AG) biomass as the carbon precursor. Zeolitic imidazolate framework-8 (ZIF-8) with high porosity is introduced into the AG aerogels to increase the specific surface area and enable heteroatom doping. After pyrolysis under inert atmosphere, the ZIF-8/AG-derived nitrogen-doped CAs show a highly interconnected porous mazelike structure with a low density of 24 mg cm(-3), a high specific surface area of 516 m(2) g(-1), and a large pore volume of 0.58 cm(-3) g(-1). The resulting CAs exhibit significant potential for application in the adsorption of organic pollutants.

Finite-Time Adaptive Fuzzy Control for Nonlinear Systems With Full State Constraints

Abstract: In this paper, an adaptive fuzzy controller is constructed to address the finite-time tracking control problem for a class of strict-feedback nonlinear systems, where the full state constraints are strictly required in the systems. Backstepping design with a tan-type barrier Lyapunov function is proposed. Meanwhile, fuzzy logic systems are used to approximate the unknown nonlinear functions. The addressed control scheme guarantees that the output is followed the reference signals within a bounded error, and all the signals in the closed-loop system are bounded. The simulation results demonstrate the validity of the proposed method.

Weakly-Shared Deep Transfer Networks for Heterogeneous-Domain Knowledge Propagation

Abstract: In recent years, deep networks have been successfully applied to model image concepts and achieved competitive performance on many data sets. In spite of impressive performance, the conventional deep networks can be subjected to the decayed performance if we have insufficient training examples. This problem becomes extremely severe for deep networks with powerful representation structure, making them prone to over fitting by capturing nonessential or noisy information in a small data set. In this paper, to address this challenge, we will develop a novel deep network structure, capable of transferring labeling information across heterogeneous domains, especially from text domain to image domain. This weakly-shared Deep Transfer Networks (DTNs) can adequately mitigate the problem of insufficient image training data by bringing in rich labels from the text domain. Specifically, we present a novel architecture of DTNs to translate cross-domain information from text to image. To share the labels between two domains, we will build multiple weakly shared layers of features. It allows to represent both shared inter-domain features and domain-specific features, making this structure more flexible and powerful in capturing complex data of different domains jointly than the strongly shared layers. Experiments on real world dataset will show its competitive performance as compared with the other state-of-the-art methods.

Bacterial Cellulose Nanofiber-Supported Polyaniline Nanocomposites with Flake-Shaped Morphology as Supercapacitor Electrodes

Abstract: Bacterial cellulose (BC) nanofiber-supported polyaniline (PANI) nanocomposites have been synthesized via in situ polymerization of aniline onto BC nanofibers scalfold. Optimized preparation conditi...