Heilongjiang University

Abstract: The design and characterization of metal–organic complexes for optoelectronic applications is an active area of research. The metal–organic complex offers unique optical and electronic properties arising from the interplay between the inorganic metal and the organic ligand. The ability to modify chemical structure through control over metal–ligand interaction on a molecular level could directly impact the properties of the complex. When deposited in thin film form, this class of materials enable the fabrication of a wide variety of low-cost electronic and optoelectronic devices. These include light emitting diodes, solar cells, photodetectors, field-effect transistors as well as chemical and biological sensors. Here we present an overview of recent development in metal–organic complexes with controlled molecular structures and tunable properties. Advances in extending the control of molecular structures to solid materials for energy conversion and information technology applications will be highlighted.

Abstract: Mesoporous TiO2 has gained increasing interest because of its outstanding properties and promising applications in a wide range of fields. Herein, we report the facile synthesis of ordered mesoporous black TiO2 (OMBT) materials, which exhibit excellent photocatalytic hydrogen evolution performances. In this case, the employment of a thermally stable and high-surface-area mesoporous TiO2 as the hydrogenation precursor is the key for fabricating the OMBT materials, which not only facilitate H2 gas diffusion into TiO2 and interaction with their structures but also maintain the ordered mesoporous structures as well as inhibit the phase transformation (from anatase to rutile) and crystal growth during hydrogenation at 500 °C. The resultant OMBT materials possess a relatively high surface area of ∼124 m2 g–1 and a large pore size and pore volume of ∼9.6 nm and 0.24 cm3 g–1, respectively. More importantly, the OMBT materials can extend the photoresponse from ultraviolet to visible and infrared light regions and ...

Abstract: Chitosan is a product of the deacetylation of chitin, which is widely found in nature. Chitosan is insoluble in water and most organic solvents, which seriously limits both its application scope and applicable fields. However, chitosan contains active functional groups that are liable to chemical reactions; thus, chitosan derivatives can be obtained through the chemical modification of chitosan. The modification of chitosan has been an important aspect of chitosan research, showing a better solubility, pH-sensitive targeting, an increased number of delivery systems, etc. This review summarizes the modification of chitosan by acylation, carboxylation, alkylation, and quaternization in order to improve the water solubility, pH sensitivity, and the targeting of chitosan derivatives. The applications of chitosan derivatives in the antibacterial, sustained slowly release, targeting, and delivery system fields are also described. Chitosan derivatives will have a large impact and show potential in biomedicine for the development of drugs in future.

Abstract: Releasing social network data could seriously breach user privacy. User profile and friendship relations are inherently private. Unfortunately, sensitive information may be predicted out of released data through data mining techniques. Therefore, sanitizing network data prior to release is necessary. In this paper, we explore how to launch an inference attack exploiting social networks with a mixture of non-sensitive attributes and social relationships. We map this issue to a collective classification problem and propose a collective inference model. In our model, an attacker utilizes user profile and social relationships in a collective manner to predict sensitive information of related victims in a released social network dataset. To protect against such attacks, we propose a data sanitization method collectively manipulating user profile and friendship relations. Besides sanitizing friendship relations, the proposed method can take advantages of various data-manipulating methods. We show that we can easily reduce adversary’s prediction accuracy on sensitive information, while resulting in less accuracy decrease on non-sensitive information towards three social network datasets. This is the first work to employ collective methods involving various data-manipulating methods and social relationships to protect against inference attacks in social networks.