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Naturally derived biopolymers have been widely used for biomedical applications such as drug carriers, wound dressings, and tissue engineering scaffolds. Chitosan is a typical polysaccharide of great interest due to its biocompatibility and film-formability. Chitosan membranes with controllable porous structures also have significant potential in membrane chromatography. Thus, the processing of membranes with porous nanoscale structures is of great importance, but it is also challenging and this has limited the application of these membranes to date. In this study, with the aid of a carefully selected surfactant, polyethyleneglycol stearate-40, chitosan membranes with a well controlled nanoscale structure were successfully prepared. Additional control over the membrane structure was obtained by exposing the suspension to high intensity, low frequency ultrasound. It was found that the concentration of chitosan/surfactant ratio and the ultrasound exposure conditions affect the structural features of the membranes. The stability of nanopores in the membrane was improved by intensive ultrasonication. Furthermore, the stability of the blended suspensions and the intermolecular interactions between chitosan and the surfactant were investigated using scanning electron microscope and Fourier transform infrared spectroscopy (FTIR) analysis, respectively. Hydrogen bonds and possible reaction sites for molecular interactions in the two polymers were also confirmed by FTIR analysis.

We have investigated the crystal structure, the microstructural and morphological characteristics, as well as the magnetic properties of Co nanoparticles (NPs) synthesized by a hydrothermal method. A series of samples has been elaborated for different concentrations of sodium hydroxide. The analysis of X-ray diffraction patterns, using two different wavelengths, has evidenced the coexistence of both $\alpha$-Co and $\beta$-Co phases in the samples. The lattice parameter for both phases is in good agreement with those values expected for their bulk Co counterparts; the grain sizes of NPs were found to be dependent on the NaOH concentration. The scanning electron microscope micrographs show that Co NPs are agglomerated forming micrometer-sized entities whose shape evolves, indicating that the synthesis process affects the morphology of the powdered samples. Magnetic measurements indicate that the coercivity is slightly larger, $H_C>200$ Oe, for Co NPs with dendritic-like shape, probably due to an increase in the magnetocrystalline anisotropy.

Magnetometry and atomic force microscopy (AFM) were used to study the magnetic and structural properties of the R–Fe–B-type (R = Y, Nd, Gd, Ho) alloys. The alloys were synthesized by means of induction melting. The nanocrystalline state of the R–Fe–B-type alloys was reached, mainly, by melt spinning (MS). A multistage treatment of R–Fe–B-type alloys, which included severe plastic deformation of melt-quenched ribbons and subsequent heat treatment, was also used. The surface morphology of samples was studied in detail to interpret the observed magnetic hysteresis loops of the samples. It was found that the type of rare earth ion and treatment methods had the most important influence on the microstructure and magnetic properties.

The structural, electronic, magnetic, and optical properties of Au, Cu, Cr, Mn, Co, Ni, and Fe atoms doped 13-atom silver clusters were investigated by the density functional theory (DFT) in the theoretical frame of the generalized gradient approximation (GGA) exchange-collection function. The results show that all the ground state structures of Au, Cu, Cr, Mn, Co, Ni, and Fe atoms doped 13-atom silver clusters are icosahedral, respectively. The Au atom doped on the surface of Ag${}_{13}$ cluster is stable, while other atoms doped in the center of Ag${}_{13}$ cluster are stable. The electronic stability order from high to small is Ag${}_{12}$Cr₁, Ag${}_{12}$Cu₁, Ag${}_{12}$Co₁, Ag${}_{12}$Fe₁, Ag${}_{12}$Au₁, Ag${}_{12}$Mn₁, Ag${}_{12}$Ni₁. Their magnetic moments are not only related to the doping atom but also the doping location of the atom. The magnetic moments of the Cu, Au, Mn, Co, Ni, Fe, and Cr atoms doped in the Ag${}_{13}$ cluster are 5.0, 3.0, 1.0, 3.0, 4.0, 2.0, and 0.0${\mu }_B$, respectively. Compared with the optical absorption spectrum of the Ag${}_{13}$ cluster, the Au, Cr, and Mn atoms doped the Ag${}_{13}$ cluster leading to blue shift, and the Cu, Co, Ni, and Fe atoms doped the Ag${}_{13}$ cluster resulting in red shift. These studies provide a theoretical basis on applications for clusters in electronic, magnetic, and optical devices.

A serious of hydrotalcite-like compounds such as Mg-Al-LDHs, Mg-Al-Cu-LDHs, Mg-Al-Fe-LDHs and Mg-Al-Ni-LDHs were prepared by coprecipitation. The structures were characterized by X-ray diffraction (XRD) and infrared spectroscopy (FT-IR). TGA and DTA were used to characterize their thermal stabilities. Results from XRD and FT-IR showed that the prepared hydrotalcites had a typical layered structures and the layer distances of hydrotalcites with Cu²⁺ and Ni²⁺ were increased and the weak coordination bonds existed in Mg-Al-Ni-LDH and Mg-Al-Fe-LDH. The results of TGA and DTA proved Mg-Al-Cu-LDH has a better thermal stability and there are decomposition reactions happened in Mg-Al-Ni-LDH and Mg-Al-Fe-LDH under 100℃.

There has been growing interest in exploring the topology structure of networks, especially multi-layer networks. However, with the rapid development of social media and technology, it has shown that modeling such multi-dimensional systems leads to a huge complexity in dealing with them. A fundamental open question is then how many layers are really needed to represent the systems, which has also attracted many scientists from various fields. Here we adopt the Von Neumann entropy theory to measure the information contained in network and through defining a distance based on it, we are able to reduce the number of network layers, as well as retaining the main characteristics of the whole system. Besides, in order to validate the method, we have built a benchmark networks which consists of three different kinds of networks to realize the aggregation process. Surprisingly, the method can not only reduce the redundancy but also distinguish the structure diversity, since it first merged the same kind of networks. Our results provide a better understanding of entropy theory and could also lead to interesting future research about how to reduce the structural reducibility of complex systems.