Sichuan University

About: Sichuan University is a education organization based out in Chengdu, China. It is known for research contribution in the topics: Population & Catalysis. The organization has 107623 authors who have published 102844 publications receiving 1612131 citations. The organization is also known as: Sìchuān Dàxué.

The Intrinsic Nature of Persulfate Activation and N-Doping in Carbocatalysis.

Abstract: Persulfates activation by carbon nanotubes (CNT) has been evidenced as nonradical systems for oxidation of organic pollutants. Peroxymonosulfate (PMS) and peroxydisulfate (PDS) possess discrepant atomic structures and redox potentials, while the nature of their distinct behaviors in carbocatalytic activation has not been investigated. Herein, we illustrated that the roles of nitrogen species in CNT-based persulfate systems are intrinsically different. In PMS activation mediated by nitrogen-doped CNT (N-CNT), surface chemical modification (N-doping) can profoundly promote the adsorption quantity of PMS, consequently elevate potential of derived nonradical N-CNT-PMS* complexes, and boost organic oxidation efficiency via an electron-transfer regime. In contrast, PDS adsorption was not enhanced upon incorporating N into CNT due to the limited equilibrium adsorption quantity of PDS, leading to a relatively lower oxidative potential of PDS/N-CNT system and a mediocre degradation rate. However, with equivalent persulfate adsorption on N-CNT at a low quantity, PDS/N-CNT exhibited a stronger oxidizing capacity than PMS/N-CNT because of the intrinsic higher redox potential of PDS than PMS. The oxidation rates of the two systems were in great linearity with the potentials of carbon-persulfate* complexes, suggesting N-CNT activation of PMS and PDS shared the similar electron-transfer oxidation mechanism. Therefore, this study provides new insights into the intrinsic roles of heteroatom doping in nanocarbons for persulfates activation and unveils the principles for a rational design of reaction-oriented carbocatalysts for persulfate-based advanced oxidation processes.

Realizing Over 13% Efficiency in Green-Solvent-Processed Nonfullerene Organic Solar Cells Enabled by 1,3,4-Thiadiazole-Based Wide-Bandgap Copolymers

Abstract: Two novel wide-bandgap copolymers, PBDT-TDZ and PBDTS-TDZ, are developed based on 1,3,4-thiadiazole (TDZ) and benzo[1,2-b:4,5-b']dithiophene (BDT) building blocks. These copolymers exhibit wide bandgaps over 2.07 eV and low-lying highest occupied molecular orbital (HOMO) levels below -5.35 eV, which match well with the typical low-bandgap acceptor of ITIC, resulting in a good complementary absorption from 300 to 900 nm and a low HOMO level offset (≤0.13 eV). Compared to PBDT-TDZ, PBDTS-TDZ with alkylthio side chains exhibits the stronger optical absorption, lower-lying HOMO level, and higher crystallinity. By using a single green solvent of o-xylene, PBDTS-TDZ:ITIC devices exhibit a large open-circuit voltage (Voc ) up to 1.10 eV and an extremely low energy loss (Eloss ) of 0.48 eV. At the same time, the desirable high short-circuit current density (Jsc ) of 17.78 mA cm-2 and fill factor of 65.4% are also obtained, giving rise to a high power conversion efficiency (PCE) of 12.80% without any additive and post-treatment. When adopting a homotandem device architecture, the PCE is further improved to 13.35% (certified as 13.19%) with a much larger Voc of 2.13 V, which is the best value for any type of homotandem organic solar cells reported so far.

Feedback Network for Image Super-Resolution

Abstract: Recent advances in image super-resolution (SR) explored the power of deep learning to achieve a better reconstruction performance. However, the feedback mechanism, which commonly exists in human visual system, has not been fully exploited in existing deep learning based image SR methods. In this paper, we propose an image super-resolution feedback network (SRFBN) to refine low-level representations with high-level information. Specifically, we use hidden states in a recurrent neural network (RNN) with constraints to achieve such feedback manner. A feedback block is designed to handle the feedback connections and to generate powerful high-level representations. The proposed SRFBN comes with a strong early reconstruction ability and can create the final high-resolution image step by step. In addition, we introduce a curriculum learning strategy to make the network well suitable for more complicated tasks, where the low-resolution images are corrupted by multiple types of degradation. Extensive experimental results demonstrate the superiority of the proposed SRFBN in comparison with the state-of-the-art methods. Code is avaliable at https://github.com/Paper99/SRFBN_CVPR19.