Chinese Academy of Sciences

About: Chinese Academy of Sciences is a government organization based out in Beijing, Beijing, China. It is known for research contribution in the topics: Catalysis & Population. The organization has 421602 authors who have published 634849 publications receiving 14894293 citations. The organization is also known as: CAS.

Mesoporous Silica Nanoparticles: Synthesis, Biocompatibility and Drug Delivery

Abstract: In the past decade, mesoporous silica nanoparticles (MSNs) have attracted more and more attention for their potential biomedical applications. With their tailored mesoporous structure and high surface area, MSNs as drug delivery systems (DDSs) show significant advantages over traditional drug nanocarriers. In this review, we overview the recent progress in the synthesis of MSNs for drug delivery applications. First, we provide an overview of synthesis strategies for fabricating ordered MSNs and hollow/rattle-type MSNs. Then, the in vitro and in vivo biocompatibility and biotranslocation of MSNs are discussed in relation to their chemophysical properties including particle size, surface properties, shape, and structure. The review also highlights the significant achievements in drug delivery using mesoporous silica nanoparticles and their multifunctional counterparts as drug carriers. In particular, the biological barriers for nano-based targeted cancer therapy and MSN-based targeting strategies are discussed. We conclude with our personal perspectives on the directions in which future work in this field might be focused.

Low-temperature oxidation of CO catalysed by Co 3 O 4 nanorods

Abstract: [Xie, Xiaowei; Li, Yong; Shen, Wenjie] Chinese Acad Sci, Dalian Inst Chem Phys, State Key Lab Catalysis, Dalian 116023, Peoples R China. [Liu, Zhi-Quan] Chinese Acad Sci, Inst Met Res, Shenyang Natl Lab Mat Sci, Shenyang 110016, Peoples R China. [Haruta, Masatake] Tokyo Metropolitan Univ, Grad Sch Urban Environm Sci, Dept Appl Chem, Tokyo 1920397, Japan. [Haruta, Masatake] Japan Sci & Technol Agcy, CREST, Kawaguchi, Saitama 3320012, Japan.;Shen, WJ (reprint author), Chinese Acad Sci, Dalian Inst Chem Phys, State Key Lab Catalysis, Dalian 116023, Peoples R China;shen98@dicp.ac.cn

Lithium–Sulfur Batteries: Electrochemistry, Materials, and Prospects

Abstract: With the increasing demand for efficient and economic energy storage, Li-S batteries have become attractive candidates for the next-generation high-energy rechargeable Li batteries because of their high theoretical energy density and cost effectiveness. Starting from a brief history of Li-S batteries, this Review introduces the electrochemistry of Li-S batteries, and discusses issues resulting from the electrochemistry, such as the electroactivity and the polysulfide dissolution. To address these critical issues, recent advances in Li-S batteries are summarized, including the S cathode, Li anode, electrolyte, and new designs of Li-S batteries with a metallic Li-free anode. Constructing S molecules confined in the conductive microporous carbon materials to improve the cyclability of Li-S batteries serves as a prospective strategy for the industry in the future.

Highly efficient visible-light-driven photocatalytic hydrogen production of CdS-cluster-decorated graphene nanosheets.

Abstract: The production of clean and renewable hydrogen through water splitting using photocatalysts has received much attention due to the increasing global energy crises. In this study, a high efficiency of the photocatalytic H2 production was achieved using graphene nanosheets decorated with CdS clusters as visible-light-driven photocatalysts. The materials were prepared by a solvothermal method in which graphene oxide (GO) served as the support and cadmium acetate (Cd(Ac)2) as the CdS precursor. These nanosized composites reach a high H2-production rate of 1.12 mmol h–1 (about 4.87 times higher than that of pure CdS nanoparticles) at graphene content of 1.0 wt % and Pt 0.5 wt % under visible-light irradiation and an apparent quantum efficiency (QE) of 22.5% at wavelength of 420 nm. This high photocatalytic H2-production activity is attributed predominantly to the presence of graphene, which serves as an electron collector and transporter to efficiently lengthen the lifetime of the photogenerated charge carrier...

Person re-identification by Local Maximal Occurrence representation and metric learning

Abstract: Person re-identification is an important technique towards automatic search of a person's presence in a surveillance video. Two fundamental problems are critical for person re-identification, feature representation and metric learning. An effective feature representation should be robust to illumination and viewpoint changes, and a discriminant metric should be learned to match various person images. In this paper, we propose an effective feature representation called Local Maximal Occurrence (LOMO), and a subspace and metric learning method called Cross-view Quadratic Discriminant Analysis (XQDA). The LOMO feature analyzes the horizontal occurrence of local features, and maximizes the occurrence to make a stable representation against viewpoint changes. Besides, to handle illumination variations, we apply the Retinex transform and a scale invariant texture operator. To learn a discriminant metric, we propose to learn a discriminant low dimensional subspace by cross-view quadratic discriminant analysis, and simultaneously, a QDA metric is learned on the derived subspace. We also present a practical computation method for XQDA, as well as its regularization. Experiments on four challenging person re-identification databases, VIPeR, QMUL GRID, CUHK Campus, and CUHK03, show that the proposed method improves the state-of-the-art rank-1 identification rates by 2.2%, 4.88%, 28.91%, and 31.55% on the four databases, respectively.