Nanjing Tech University

About: Nanjing Tech University is a education organization based out in Nanjing, China. It is known for research contribution in the topics: Catalysis & Membrane. The organization has 21827 authors who have published 21794 publications receiving 364050 citations. The organization is also known as: Nangongda & Nánjīng Gōngyè Dàxúe.

Utilizing d–pπ Bonds for Ultralong Organic Phosphorescence

Abstract: Developing pure organic materials with ultralong lifetimes is attractive but challenging. Here we report a concise chemical approach to regulate the electronic configuration for phosphorescence enhancement. After the introduction of d-pπ bonds into a phenothiazine model system, a phosphorescence lifetime enhancement of up to 19 times was observed for DOPPMO, compared to the reference PPMO. A record phosphorescence lifetime of up to 876 ms was obtained in phosphorescent phenothiazine. Theoretical calculations and single-crystal analysis reveal that the d-pπ bond not only reduces the (n, π*) proportion of the T1 state, but also endows the rigid molecular environment with multiple intermolecular interactions, thus enabling long-lived phosphorescence. This finding makes a valuable contribution to the prolongation of phosphorescence lifetimes and the extension of the scope of phosphorescent materials.

Shear Mechanisms in Reinforced Concrete Beams under Impact Loading

Abstract: A well-instrumented drop-weight test program was developed to investigate shear failure modes of large-scale RC beams under impact loading. Test variables included beam span, transverse rei...

High-Performance Platinum-Perovskite Composite Bifunctional Oxygen Electrocatalyst for Rechargeable Zn–Air Battery

Abstract: Constructing highly active electrocatalysts with superior stability at low cost is a must, and vital for the large-scale application of rechargeable Zn-air batteries. Herein, a series of bifunctional composites with excellent electrochemical activity and durability based on platinum with the perovskite Sr(Co0.8Fe0.2)(0.95)P0.05O3-delta (SCFP) are synthesized via a facile but effective strategy. The optimal sample Pt-SCFP/C-12 exhibits outstanding bifunctional activity for the oxygen reduction reaction and oxygen evolution reaction with a potential difference of 0.73 V. Remarkably, the Zn-air battery based on this catalyst shows an initial discharge and charge potential of 1.25 and 2.02 V at 5 mA cm(-2), accompanied by an excellent cycling stability. X-ray photoelectron spectroscopy, X-ray absorption near-edge structure, and extended X-ray absorption fine structure experiments demonstrate that the superior performance is due to the strong electronic interaction between Pt and SCFP that arises as a result of the rapid electron transfer via the Pt-O-Co bonds as well as the higher concentration of surface oxygen vacancies. Meanwhile, the spillover effect between Pt and SCFP also can increase more active sites via lowering energy barrier and change the rate-determining step on the catalysts surface. Undoubtedly, this work provides an efficient approach for developing low-cost and highly active catalysts for wider application of electrochemical energy devices.

Dinitrogen complexation and reduction at low-valent calcium

Abstract: Here we report that attempted preparation of low-valent CaI complexes in the form of LCa-CaL (where L is a bulky β-diketiminate ligand) under dinitrogen (N2) atmosphere led to isolation of LCa(N2)CaL, which was characterized crystallographically. The N22ˉ anion in this complex reacted in most cases as a very potent two-electron donor. Therefore, LCa(N2)CaL acts as a synthon for the low-valent CaI complex LCa-CaL, which was the target of our studies. The N22ˉ anion could also be protonated to diazene (N2H2) that disproportionated to hydrazine and N2. The role of Ca d orbitals for N2 activation is discussed.

Temporal Structure Mining for Weakly Supervised Action Detection

Abstract: Different from the fully-supervised action detection problem that is dependent on expensive frame-level annotations, weakly supervised action detection (WSAD) only needs video-level annotations, making it more practical for real-world applications. Existing WSAD methods detect action instances by scoring each video segment (a stack of frames) individually. Most of them fail to model the temporal relations among video segments and cannot effectively characterize action instances possessing latent temporal structure. To alleviate this problem in WSAD, we propose the temporal structure mining (TSM) approach. In TSM, each action instance is modeled as a multi-phase process and phase evolving within an action instance, \emph{i.e.}, the temporal structure, is exploited. Meanwhile, the video background is modeled by a background phase, which separates different action instances in an untrimmed video. In this framework, phase filters are used to calculate the confidence scores of the presence of an action's phases in each segment. Since in the WSAD task, frame-level annotations are not available and thus phase filters cannot be trained directly. To tackle the challenge, we treat each segment's phase as a hidden variable. We use segments' confidence scores from each phase filter to construct a table and determine hidden variables, i.e., phases of segments, by a maximal circulant path discovery along the table. Experiments conducted on three benchmark datasets demonstrate the state-of-the-art performance of the proposed TSM.