The theoretical results show that the shape of the Curves of both real and imaginary parts is depending of the fractional order of this new model named Fractional Magnetic Model (FMM). Moreover, the comparison between theoretical and experimental results show that the FMM describe quite well the complex susceptibility response of the polymeric systems containing
magnetic nanoparticles at low frequencies (from 0.1 to 10(5) Hz). (C) 2009 Wiley Periodicals, Inc. J Appl Polym Sci 112: 1943-1948, 2009″
“Using molecular dynamics method, we investigated the influence of tube diameter and chirality on the stability of single-walled carbon nanotubes (CNTs) under ion irradiation. We found that in the energy range below 1 keV, the dependence AG-881 solubility dmso of CNT stability on the tube diameter is no longer monotonic under C ion irradiation, and the thinner (5, 5) CNT may be more stable than the thicker (7, 7) BMS-777607 CNT, while under Ar irradiation, the CNT stability increases still monotonically with the CNT diameter. This stability behavior was further verified by the calculations of the threshold ion energies to produce displacement damage in CNTs. The abnormal stability of thin CNTs is related to their resistance to the instantaneous
deformation in the wall induced by ion pushing, the high self-heating capacity, as well as the different interaction properties of C and Ar ions with CNT atoms. We also found that under ion irradiation
the stability of a zigzag CNT is better than that of an armchair CNT with the same diameter. This is because of the bonding structure difference between the armchair and the zigzag CNTs with respect to the orientations of graphitic networks as well as the self-healing capacity difference. (C) 2009 American Panobinostat Institute of Physics. [DOI: 10.1063/1.3194784]“
“Principal component analysis (PCA) has been performed for some acridinone derivatives with antitumor activity to model relationships between structural descriptors and lipophilicity and their antileukemia activity. Principal component analysis obtained with the use of parameters of lipophilicity led to extract two main factors with eigenvalue higher than 1. The first principal component (factor 1) accounted for, by 84.3% of the variance in data and the second principal component (factor 2) explained 10.0% of data variance indicating that total data variance at the level 94.5% can be explained by the first two principal components. On the other hand, principal component analysis obtained with the use of parameters of lipophilicity together with some other molecular descriptors led to extract five main factors with eigenvalue higher than 1. In this case, the first principal component (factor 1) accounted for, by 62.64% of the variance in data, the second principal component (factor 2) explained 19.04% and the third principal component (factor 3) explained 8.